BUS610: Business Intelligence and Analytics

Explore this article to understand the definitions and common functions of BI technologies, which include reporting, online analytical processing (OLAP), analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

Read this for an introduction to business intelligence.

To understand definitions regarding the taxonomy of BI, read this paper, where an example of the methodology in the research process is used. It also discusses how the taxonomy for BI and analysis was developed, how it is applied, and an analysis of the current status with predicted development for the next wave or 3.0 of BI, as well as potential gaps. A clear diagram of the taxonomy development process is shown in Figure 6. While a picture is worth a thousand words, sometimes you must explain complex processes narratively.

Read this for a quick overview and definition of Competitive Intelligence. Remember, as we discussed in the introduction to this unit, the two may have similar methods and sometimes even inputs. However, BI is internally focused, while CI is externally focused.

This paper results from research surveying executives with robust analysis and offers insight into their needs. This case study shows that current tools were insufficient. More information architecture using data warehousing, OLAP tools, and data mining was required to equip them for their information needs and better decision-making. Consider when you have not had the tools to perform your best analysis. Were you able to obtain the tools and information you needed, or did you have to be creative or "make do"? How can an analyst influence the decisions on providing tools, appropriate architecture, and data sources within a firm?

System engineering can best be explained as coordinating multiple tasks within the two disciplines of engineering and engineering management. This paper highlights the systems method of coordinated tasks and its relevance concerning current and future business system life cycles: concept, design, planning, testing, optimization, and deployment. It defines the boundaries necessary for a robust life cycle and analysis to occur.

Read this definition of the uses of business engineering in developing and implementing business solutions for human-technology interaction. What solutions will be needed as data gets "bigger" and more complex? Will analysts be able to find ways to capture and manage all that is relevant, or will they have to live in constant fear of missing that "golden nugget" of source material that would have made all the difference in their findings?

Read this brief explanation of where BI sits in an Enterprise Resource Planning (ERP) system, which is used by a business to manage its processes. If you want to gain more insight regarding all ERP modules, feel free to follow the link to the whole book from which this explanation is extracted. 

These seven articles provide a nice overview of how businesses use information, however; note that data and information are not synonymous. Jot down for yourself how they differ to be sure you can keep them straight. Hint: Our old friend "structure" is involved!

Read this paper for an overview and examples of how big data is used in specific areas, such as supply chain management, risk management, and logistics of business in industry. One of the biggest issues for analysts with big data is knowing how to separate the valuable data from that which does not help answer their requirements.

Sometimes people describe intelligence as "connecting the dots", but it is rarely simple like a "paint-by-numbers" art project. The dots are not just lying around waiting to be connected. More appropriately, it has been described as filtering out the right radio signals from the fray in a huge city. You have to be carefully tuned to your requirements, which will be discussed at length in Unit 2 and again in Unit 8, as these are the guide stars that keep you on track to finding the right data to answer the questions you need to focus on.

After reading this paper, you should clearly understand the relationship between the analytic methodologies and techniques associated with big data and how to integrate it with a new correlation taxonomy. This paper adds more distinction to the 5Vs of big data you read about previously. You will recognize the characteristics and significance of the descriptive, diagnostic, predictive, and prescriptive integration methods.

As you read, pay attention to Figure 1, which outlines the research process and provides a clear 3-step map. Follow through each section of this paper to understand modeling for effectiveness.

One of the preeminent voices regarding future information systems and technology trends is Mary Meeker, an acclaimed Venture Capitalist. She has presented her annual "Internet Trends" report (this is from 2019) for the last twenty-five plus years with wide acclaim for its foresight. This 30-minute video is a quick overview of her findings for 2019.

In the video, she encourages everyone to look at the full set of slides available through her company Bond Cap. Their website has an archive of past reports showcasing her forecasting prowess for trends and how these technologies apply to the business intelligence process. This is not required for the course, but knowing the major thought leaders in your field is important.

This chapter discusses trends in information technology such as; digital forensics, the shift to a distributed workforce (very relevant during the 2020 COVID-19 global pandemic), and the increasing use of grid computing while acknowledging the rapid pace of change. Consider some benefits to the high-level accessibility of information for the average employee. Conversely, how can this level of access be detrimental to businesses? 

Read this article on privacy, security, and ethical concerns with integrating IoT in the business intelligence cycle. As the section overview states, IoT is a collection of different technologies working together. Still, it is also an amalgamation. This article will help you understand in a detailed discussion how IoT fits into everyday life and its potential from both the technological and sociological perspectives. How can the connected devices you own be utilized with others?

Watch this video to see what happens when people ask the right questions. We will look into this a bit more in section 2.1.1.

Several years ago, a market research firm did a project for a religiously-based non-profit organization that once provided death benefit insurance to a now-defunct fraternal order. The group was trying to determine how to retain relevance and market share in a dynamic financial services industry. They intended for the firm to evaluate their products to determine what new lines they should offer.

As the market research firm discussed the client's problem with their team, they discovered that the client was not sure to whom they would sell these new products because their traditional customer base had disappeared. The firm planned the project to determine not the best product but who the best customers would be for their existing products, which they already knew well. Fortunately, this client was open-minded enough to recognize that the original question they had asked was not the one they needed answered.

Once you have read, think through the discussion questions after reading the example of Belgium Brewing. How does this process take place in your organization? Are the employees a big part of the decision-making process, or does management maintain most of the control? Have you been asked to support strategic, operational, or tactical decision-making? How could Belgian Brewing use BI to enhance its decisions on how to increase sustainability while growing profits in the future?

Think about Malcolm Gladwell's stories about his friend Howard Moskowitz. Imagine how much more satisfied Prego was with Howard's decision-making support than Pepsi was. It took Howard several years after the Pepsi taste tests to realize Pepsi was asking the wrong question about how much aspartame to put in its new Diet Pepsi formula. By that time, he was primed to rethink Prego's approach to gaining market share by focusing on taste and texture.

When conducting market or competitive intelligence analysis, an external client typically arrives with a question they need to be answered to increase their market share or otherwise increase their competitive advantage(s). Such questions are called intelligence requirements. In business intelligence, which is focused internally, the client will come from within the business itself. This can make the process of ensuring the client is asking the right questions more or less difficult than telling an external client that they don't need to know what they think they need to know.

When an organization has fully embraced the value of BI, its culture will allow the analyst to "speak truth to power" and inform management that they are asking the wrong question without issue. When a business only thinks it is using BI, it may be more difficult for the analyst to speak up and challenge prevailing assumptions. This culture is the first thing for the analyst to recognize.

If the environment is permissive, there will be no obstacles to the analyst suggesting changes to the requirement. In a less permissive business environment, the analyst will have to learn how to most effectively communicate with their decision-makers by first identifying their individual motivations and influences and how to best communicate their findings, whether through written reports or in-person briefings.

The analyst will also have to learn how formal or informal these communications should be. This is fodder for an entire course as the possible intentions and communication strategies are infinite, but the article on negotiation in 2.1.3. may be of some use as you develop your strategies for communicating with your decision-makers.

Without a TOR, analysts and teams are likely to go off and get lost "in the weeds", chasing interesting tangential leads that may not have ultimate value to the project, thus expending limited resources in both time and budget without gaining relevant insights and moving the project forward.

This additional reading provides some process ideas, from brainstorming to mind mapping, that may help you think about ensuring all needed elements end up in the TOR. Here are two models from environmental protection and education that can help you create the TOR template that is right for your business.

We will further investigate refining the scope of your project in the next lesson once the main parameters of the project are defined in the TOR.

This article will help you to understand the importance of setting parameters and defining your scope to be sure everyone stays on target and all the moving parts and pieces can be harnessed for a positive project outcome.

Sometimes there are competing priorities in an analyst team. We will discuss analytic team dynamics later in the course. For now, it will be useful to understand that defining the scope of your project beyond the TOR will require some negotiation within the team. To do this effectively without causing disagreements within a team that must continue to work together for a shared goal. Read this article on negotiation to understand how to manage competing priorities, etc.

Have you participated in negotiations at work? Have they followed the flow of the model from the article? Or have they been contentious or messy without a collaborative or satisfactory outcome? When everyone comes to the table with a positive attitude and is prepared with all of the information needed to develop an effective work plan, the experience can be very positive.

The article mentions the need for managers involved in the negotiation to be mindful of delegation. Part of the negotiation at this stage should be establishing the roles and responsibilities of each team member. It will be important to determine who has the needed skills and place them in the appropriate roles with the necessary authorities so they can participate in the project as meaningfully as possible. Once roles and responsibilities are determined, deliverables, project milestones, and timelines can be established.

It is valuable to note the various decision-making styles briefly described: psychological, cognitive, and normative. Understanding which of these your management uses will go a long way to helping you most effectively negotiate requirements, project scope, and communicate your analytical findings. Another useful takeaway from the article is the description of the three main approaches to decision-making: avoiding, problem-solving, and problem-seeking. Sometimes the right decision is no decision, so avoiding it is not necessarily bad. However, if your manager consistently avoids decisions claiming there is not enough information despite your best efforts to provide it, you may need to find new ways to communicate your findings to allow the manager to have more confidence in making decisions.

The challenge of problem-seeking when it sends the team back to the proverbial drawing board can be overcome with a robust TOR development process and a fully informed and formal scope negotiation process, both of which will help alleviate concerns that the project is not progressing as needed. It will be quite valuable for you to know the individual approach your manager or management team is likely to use and that of the overall organizational culture.

The article mentions Daniel Kahneman without explaining much about him. We will get to know him much better in the next section when we start to think about thinking.

Sometimes the most well-structured decision-making processes go awry, not because of the process itself, but because of the participants or the environment. This article shows us what can go wrong and how to get things back on track to meet
your TOR deliverables.

The article discusses obstacles to improved decision-making, including "cognitive limitations, heuristics and biases and individual inclinations". Heuristics are mental shortcuts individuals use to solve problems. These have great use, for instance, telling humans to run when they see a saber-toothed tiger without thinking too much about the decision. The choice of which cat to adopt from a shelter today may require less use of heuristics and more cognitive exercise.

This insight has profound implications for economics, public policy, and our self-awareness, which can help BI analysts avoid bias.

Kahneman and his long-time thinking partner, Amos Tversky, famously changed how humans think about their own cognition and are known for popularizing the concepts associated with behavioral economics and our awareness and understanding of our thought processes, or metacognition.

Kahneman's examples may not seem relevant to business or management, but a decision-maker's memory of the outcome of previous decisions may inappropriately cloud their recall of the process or certain aspects of the implementation. This may result in a manager believing deeply that a particular analytic method or other approach is useless because "it did not work last time". This natural human failure to correctly recall an experience and rely on memory, what Tversky and Kahneman call simply "the story we tell ourselves about the experience", can result in faulty decision-making.

In this exercise, you will create a two-column journal and describe what the text is saying and how that information makes you feel or think. This gives us insights into our thinking processes and pathways. Read the first excerpt, then make your own basic evidence log. Consider what the text says and means to you, and practice using your log for the next examples.

The article describes a few common biases that are important to be aware of. Go through the brief quiz at the end and see how you do. If you miss any questions, go back and re-read that section. Think about how you might apply the concepts in that section so that the ideas will make sense and stick with you. Work through the "Personal Application Question:" Which types of bias do you recognize in your decision-making processes? How has this bias affected how you've made decisions in the past, and how can you use your awareness of it to improve your decisions making skills in the future?

Consider a specific time when you showed bias in coming to a conclusion about a person or event. How did you realize your thinking process was biased? How can you use this knowledge to avoid making this type of biased decision in the future?

High school teachers have developed a useful matrix to determine when students are effectively using heuristics that are useful to us. Think about how you have used some of these heuristics today or in the past few days. How useful were they in determining an optimal outcome?

The examples from the article on defining your scope were varied, but your team is likely to produce a series of brief reports that bring you closer to understanding your target or full requirement. Once you have broken the requirement into specific parts, you will begin to attack the target from various sides and must identify who is responsible for what. As you will see from reading the article, some decision-making methods are used to determine who needs to be part of the decision-making process, depending upon their deciding authority or skills. For instance, someone from finance would need to be in on a procurement process, but maybe not someone from HR, unless it was a tool to be used by their department.

Read this article that goes more in-depth on the RAPID decision-making tool.

This provides a few examples to show how it can be used and why having such a tool can improve decision-making. In the RAPID example, the BI analyst or team will likely have the most influence on the "I" or "Input" part of the process. This is where additional information can be injected into the discussion.

Even if RAPID does not feel like the tool for your business, having a structured and well-informed process can make all the difference, so you can always do some research and find one that suits your business culture and decision-making needs.

Read this article that will introduce you to the latest buzzword on how to more effectively manage all of this information: the dashboard.

The fox vs. hedgehog construct can be likened to the "Soldier" vs. "Scout" approach to the world described in the video.

Do you think an analyst should be more like Galef's Soldier or Scout? What about a manager? How can the Scout traits be enhanced to increase analytic agility like the Fox's? Not only do they know many tricks, but they assess when and how to use them for the greatest effect. This is the same way a good analyst uses various analytic techniques to ensure the one or a combination selected is the right approach for each requirement.

Author Malcolm Gladwell popularized the "gut" approach the same year with his book, "Blink", which suggested that some people's snap decisions may be better than analysis. In combination, the two books were conflated by laypeople to suggest that there is little need for analysis, but that is an erroneous reading of both books.

Tetlock suggested experts get lazy and start thinking they know everything about their narrow field, and that's when they start making poor forecasts. Dilettantes know they don't know everything and try to update their understanding. Analysts use tested methods to analyze the situation and constantly allow the introduction of new information, whether or not it confirms or refutes their initial hypothesis, using lots of clever analytic tricks to maintain objectivity and create the most accurate estimates.

Gladwell said experts can sometimes make snap decisions better than others based on their inherent subject-matter knowledge. He used the example of an ancient Greek art expert who could spot a fake statue based on a feeling when he saw it objectively because "it just didn't look right...it looked fresh" and not 2,000 years old.

The first expert's colleague, also an expert, so wanted such an object in his museum he allowed his expertise and objectivity to be set aside to believe the fake was real, thus allowing any new information to either confirm his belief or he rejected the information out of hand. This is not good analysis. The snap decision of the correct expert is also not analysis; it was a good use of his specialized art history knowledge.

Watch this video for a basic case study showing how some simple forecasting can inform the optimal ways for a business to intervene in its strategy as conditions change. For instance, if you are trying to sell sneakers in the Netherlands, how could foreknowledge of the pandemic have helped you plan?

While public health officials have warned that the possibility of such an event was increasing with the rise of globalization, most businesses were probably not prepared for it unless they sold personal protective equipment (PPE). For instance, if you were the owner of a sports footwear company that happened to be the daughter of a high-ranking World Health Organization official, you might have been thinking about a pandemic.

Once it began, you were ready for the rise in outdoor activities as spring turned into summer in the Netherlands, and people were spending more time outdoors as they had time and needed to entertain their kids. The virus is believed to be spread less quickly in areas with good ventilation. Thus, even your older population with little interest in sports may become a new target market. Due to your forecasting ability, you were prepared ahead of your competition to make an intervention in your sports shoe strategy and be the first to serve this space.

Review this section to be sure you understand variable, fixed, and mixed costs.

This review of current literature explores text mining techniques and industry-specific applications. Selecting and using the right techniques and tools according to the domain helps make the text-mining process easier and more efficient. As you read this article, understand this includes applying specific sequences and patterns to extract useful information by removing irrelevant details for predictive analysis. Of course, major issues that may arise during the text mining process include domain knowledge integration, varying concepts of granularity, multilingual text refinement, and natural language processing ambiguity. Figure 3 shows the inter-relationships among text mining techniques and their core functionalities. Using this as a blueprint, apply one example from your industry to each part of the Venn diagram.

According to the conclusion of this article, "data mining is a young discipline with wide and diverse applications, there is still a significant gap between general principles of data mining and domain specific, effective data mining tools for particular applications". Are there some areas where you have seen improvements? Are there others where there could be more?

Data mining will occupy an increasingly important position as the world moves from solving issues related to collecting data to generating information from large masses of data that are now easily gathered. This paper emphasizes that many industries depend on insights gathered from data, and thus naturally, data mining will become a central focus. We are now moving into an era where pattern recognition and prediction are common. What patterns do you recognize? Are you able to glean some insights into how you are learning?

This simple video tells the story of the growth of big data from the 1960s to today's cloud architecture. What might come after the cloud? This isn't easy to imagine. It seems that when we are at the point of beginning to adopt new technology, it is the end of change. However, data scientists are already looking for something to manage big data that is even more interconnected and accessible than the cloud, even before most of humanity has adopted it. This is why analysis and its inputs and technical tools are a constantly moving target. How exciting to be part of a field that is so dynamic! How scary that, one day, machine learning could even take our jobs!

Watch this short video for a succinct simple explanation of big data. Does this mesh with your understanding?

Big data is defined by the techniques and tools needed to process the dataset - multiple physical/virtual machines working together to process all the data within a reasonable time. This article highlights tools for analyzing big data, including Apache Hadoop, Apache Spark, and TensorFlow. Keep this list of tools handy, as it will prove beneficial in the future as we explore data architecture.

This article describes text mining as the "retrieval, extraction and analysis of unstructured information in digital text". It allows scientists and other researchers to gather massive amounts of written material and add automation for analyzing it efficiently. This will revolutionize literature review capabilities and allow people in specialized fields to quickly understand the current state of knowledge. How might data and text mining differ regarding generation, storage, standardization, and exploitation?

Review these key points on the internal and external data available when constructing a business report to better understand the concepts covered in this piece. These data can also be split into qualitative (description, non-numeric, usually require context) and quantitative (numeric, measure); both are useful. Primary research is research one has done by oneself. Secondary research is research based on other people's primary research. Be sure to do the practice questions to solidify your understanding.

When evaluating sources, we look at quality, accuracy, relevance, bias, reputation, currency, and credibility factors in a specific work. This article breaks down the questions to ask yourself when evaluating a source – who, what, where, when, and why (sometimes we also need to add "how") – it then summarises these into the 5Ws. What are your 5Ws?

There are some common issues when dealing with big data. Two critical ones are data quality and data variety (such as multiple formats within the dataset) – deep learning techniques, such as dimension reduction, can be used to solve these problems. Traditional data models and machine learning methods struggle to deal with these data issues, further supporting the case of deep learning, as the former cannot handle complex data with the framework of Big Data.

The visuals in this article highlight the importance of the 'Big Picture of Statistics' and summarize the general steps of a statistical study: from coming up with the research question, determining what to measure and collecting data, to conducting exploratory analysis on this data and inference to draw up a conclusion on the population in question. Using the example showcased on this page, pick a topic or issue important to your industry and make a visual representation of the concepts.

Imagine how befuddled our Sumerian trader would be with the dizzying amount of data we can capture and use today!

This article briefly explores the history and development of the purposes and phases of data collection and storage. It can broadly be characterized by shifts in how and where the data is stored and the nature of the data (structured to unstructured to mobile and sensor-based content). What are some uses of data warehousing in your life based on each of the big data phases defined? What do you think the next phase of big data will include?

Data sharing is important to furthering scientific discovery; this has never been more important than today with the COVID-19 pandemic. While the medical sector is one of the most important areas for data sharing, can you think of other sectors where data sharing is highly significant? For example, agriculture and the processes by which our food is developed. While applying the concepts of this article, what are some additional potential concerns for data sharing, and how could it affect future research?

A data warehouse is considered a core business intelligence and mining component. What are some differences between data warehousing and data mining, and how do the two intersect/relate to each other?

The various data storage media throughout time are shown in this page: vinyl, floppy disks, CDs to USBs, and memory cards. As you can see, media have gotten smaller/more pocket size and more secure. How many of these have you used? Can you identify when each was core to data storage and their advantages and disadvantages? How long before all personal hardware modes of data storage are defunct?

Data was stored in file-based systems before the advent of database systems which were developed/created because of the disadvantages of the file-based system. This included integrity problems, isolation of data, and security issues. How might a small agricultural business use a database system to gain a competitive advantage in the marketplace? Think about in what areas and how a database system would be used. For example, how could a homeowner implement a database to finesse their home management?

The main real-world datasets used in the studies analyzed for this paper were sensor data, image metadata, website publications, and electronic documents. Most of the studies analyzed did not document the specific languages they used to model their data or the tool they used. But due to the need to analyze large volumes of data with various structures, which arrive in high frequency, database research became more focused on NoSQL than relational databases. Why might a NoSQL vs. Relational approach be best for database management, according to growing trends captured in this review of research?

This article explores the various tools and technologies currently being leveraged (like Hadoop, which is useful for developing applications that can perform absolute statistical analysis on vast quantities of data) and the issues faced when using them (heterogeneity and timeliness, security, incompleteness and scalability of the data are the biggest obstacles when analyzing big data). What are some additional areas where big data utilization can grow? What needs to improve? What other technologies do you envision being used in collaboration with big data in the future, and in what ways?

This article lists the various computer information systems/storage types and how they work. This article includes definitions of various types of storage, from hard drives and flash memory, such as USB drives and solid state drives (memory cards), to optical discs and smart cards. We currently use smart cards more than this article suggests.

This paper explores what a Database Management System (DBMS) suited to the future may look like based on issues that can be seen today, as well as emerging trends and how this system may be created. An apt example includes a system that allows efficient and continuous querying and mining of data flows that can be employed on media with different computing capacities. What human-to-machine communication and interoperability do you think was most beneficial? Consider how, for example, an individual embedded medical device will be included in DBMS as processes get more complex and storage facilities become more distributed. What are some key aspects of DBMS that could benefit future architectures?

This video provides an in-depth explanation with real-world case examples in specific industries of DW strategies. How might you apply these concepts to your industry? What would the pros and cons be?

This article highlights the importance and intersection of data mining and data warehousing in the context of big data. In a data warehouse, data helps analysts to make informed decisions in an organization. Based on your understanding of a database warehouse and the data mining life cycle, consider a specific issue in an industry you know. How might you apply these steps to address the issue?

Businesses and institutions must collect and store temporal data for accountability and traceability. This paper highlights an approach to dealing with transaction lineage that considers how data can be stored based on timestamp granularities and methods for refreshing data warehouses with time-varying data via batch cycles. Identify three ways transaction lineage can be used and how this is relevant to temporal data. What industries do you think transaction lineage will always be relevant in? How?

This case study provides insight into how a data warehouse was built for a firm in the financial sector using its existing Microsoft technology. It touches on the current form of "static reports" currently used within the company, which we have identified as problematic. This case study showcases a step-by-step method of how this DW is built. After reading, you should understand the theory and practical application of the DW approach. How would you apply a similar framework to a large department store chain's supply chain?

"Good" hardware is integral for a data warehouse and its software to function efficiently, and the architect of the warehouse must be "hardware aware". As each hardware and software technology advances, so do data warehouses with the advent of, for example, new nonvolatile memory (NVM) and high-speed networks for base support. This article focuses on the need to develop and adopt new management and analysis methods.

Big data and business analytics methods for improved business decision-making, technological approaches, applications, and open research challenges. Big data has brought companies in developed countries many positive effects, which those in emerging and developing nations may replicate. However, big data's many challenges include data security, management, characteristics, compliance, and regulation. This paper contains a neatly wrapped breakdown outlining the structure, components, and tools that provide effective and efficient processing for the Hadoop ecosystem.

This toolkit was developed with the World Bank to teach and provide tools for entrepreneurs to collect data. Business analysis for tech hubs is difficult because the hubs simultaneously influence and are influenced by their local ecosystems. Areas in which tech hubs may benefit from business analytics include finding focus, sharing success with customers, and fundraising.

Imagine you are setting up a tech hub using the framework provided in this toolkit. Make a plan of how you would effectively collect the data. How would you decide what to measure? What resources will you need to effectively implement, monitor, and report the services your tech hub offers?

This article highlights use cases of ocean observation to explore how cloud computing can be improved to handle increased data flows. As the amount of data ingested increases, the cloud could replace traditional approaches to data warehousing. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations near the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Apply this structure in your industry regarding how to get data, store data, organize it, and conduct analysis and visualization in the cloud. What are some potential problems for large datasets? Think about how you would overcome those challenges. How would "sandboxes" provide some security when testing a system?

Cloud computing is useful in providing easy-to-access high-performance computing, networking, and storage via the net. Future work should be geared toward working on data science/AI/ML services to protect user data to make data more secure. What are the three service models of the cloud? How do they differ for the consumer? Identify, define, and provide some examples of issues with potential solutions you may have experienced with your social media accounts.

Look at this simple flow chart that provides a basic analysis process. These were the preparation phases of analysis. This diagram does not include warehousing, where data is stored, and where much processing occurs. Analytics is where the information becomes intelligence. It is transformed from disparate data points that can be described in terms of data sets into patterns resulting from the analysis. This is where the real brainwork of the analytic process takes place. The methods are myriad and highly dependent upon a particular project's available inputs and requirements.

Read this overview of statistical analysis. While it can be a form of analysis used by intelligence analysts, what differentiates us is the types of data we often find ourselves using. Sometimes it is detailed internal data already neatly cleaned and standardized, but more often, it is qualitative data that we have to normalize ourselves. This is the "art" of intelligence analysis.

A Fortune 500 insurance company hired an analyst team to assess how well their 2,000 independent agents used online marketing tools. The company had provided a one-page web template to all the analysts the previous year to, at a minimum, have a single result in a search for the name of their agency so people could find their contact information and perhaps a basic idea of the products they offered. A year later, the company hoped to discover that most of their agents had moved on to develop a more robust web presence. To conduct this analysis, the team worked with the company's strategic marketing team to identify what the company wanted its agents to use. For instance, was there a simple description of each of their products, was there a one-click method to get an insurance quote, were their links to the agents' Facebook, Instagram, Twitter, or other online social media account, had there been something posted on these accounts in the past week, etc.? Once these "robust web presence" parameters had been established, the analyst team painstakingly reviewed all 2,000 websites and used a spreadsheet to mark an "X" where each agent had each desired item on their website. These were not weighted but only tabulated to give each agent a "web use score". These scores were analyzed to determine how close each agent was to a "perfect" score, meaning they had all the desired items. A one-page snapshot was developed for each agency when the analysis was completed. It told them their overall score was out of the total possible, showing what they were doing well and where they needed to improve.

This was a classic case of using internal data for a business intelligence process to improve internal processes to increase market share. It is also a case of developing new indicators and a new analytic method to determine an outcome that me the client's requirements. The project could have continued and become a competitive intelligence project by assessing what share of their geographic market each agent had captured the previous year. This would likely have yielded more evidence for the company to persuade agents that more effective use of web-based tools could result in a competitive advantage...but ONLY if that is what the data indicated. One firm, for example, is one of the oldest in this company's agent "family". It is located within one block of the corporate headquarters. This agency only used a single template page and had a massive geographic market share. Its market was older, wealthier, traditional people with generational histories of using this firm. Their marketing was nil. It was based on families bringing new drivers who needed auto insurance, newlyweds who wanted to explore life insurance, etc., for their market. This model worked for them, but it is unclear whether it would remain sustainable as their customers became more web savvy. Thus, it is important to look at outliers, who may tell more of the story but not assume they tell the whole story.

The amount of data collected is staggering. The article was written in the middle of 2019; how much data is now collected daily? The National Security Agency monitors hot spots for terrorist activities using drone feeds. They admitted several years ago that analyzing what they had already collected would take decades, and the collection continues. The key to effective analysis is identifying the most relevant datasets and applying the correct analytic techniques, returning to our mix of art and science. As the article indicates, very little has been studied on removing uncertainty from the value of datasets growing daily. At least with BI, you are typically looking mainly at the data created within your firm, which places some limits on the amounts and type of data, but in a firm as large as, say, Amazon, imagine the amount of data created every day, not only at the point of purchase but in all of its hundreds (maybe thousands) of automated fulfillment centers around the world. Looking at figure 1, the 5Vs of Big Data characteristics, think about the challenges of the kinds and amount of data collected daily by your firm. Is it housed in a common system or different systems depending on the department collecting and using it? How would you characterize its various Vs? Is it manageable? What level and types of uncertainty would you assign to the various datasets you regularly work with?

This article outlines the structure, purpose, and use of decision trees. The nice thing about them is that they allow for chance, which can throw other analytic techniques into a tailspin. Remember our hapless weatherman, who carried out his analysis to a perfect outcome but was thwarted when his boss smelled french fries and ended up at the same fast food restaurant where the weatherman was hiding? Follow the logic and build a decision tree to decide something simple, but to which you can add a lot of variables, like what to have for dinner tonight. Chance occurrences include your child or roommate bringing a friend home unexpectedly.

This article provides a step-by-step process on how to build a decision tree. Follow the logic and build a decision tree to decide something simple, but to which you can add a lot of variables, like what to have for dinner tonight. Chance occurrences include your child or roommate bringing a friend home unexpectedly. As noted, decision trees are inherently built on the cost-benefit analysis model but carry it out to the furthest degree possible. Decision trees can also include a financial component. Create a decision tree to decide what your next vehicle should be. This may inform your actual purchase or be a fantasy. Remember to stick to facts when you do your analysis exercises and try to remove any biases. There are true impediments to purchasing a Lamborghini when you are a student or have several small children.

This decision tree model uses more data types and provides ways to classify data. These graphics clarify how and why decision trees can be used. Follow the examples closely to see how these might be useful in your work. Have you used decision trees in your work? Does your firm have software or other drawing tools to help you create decision trees? Without a program, the analyst has to depend on their bias reduction skills, so it is best to have a team work together to ensure all possibilities are considered and inherent biases do not creep in to rot your tree.

This article provides a nice overview of analytic tools and their value. While SACH is one of the best-known for reducing bias and ensuring all information is included, it is onerous for analysts to learn. Still, once they do, it is one of the best for ensuring a complete audit trail and keeping analysts "honest" by ensuring they include all evidence without bias. Adding automation to these tools makes them much easier to use, and as a result, they have increased, and their use has become more ubiquitous in the past decade. What structured methods have you used? Which have proven easy and which difficult? Which would you recommend to a new analyst?

ACH is one of the best for ensuring a complete audit trail and keeping analysts "honest" by ensuring they include all evidence without bias. A country stability report is one of the most basic studies new intelligence studies students complete. They use ACH to determine whether an assigned country will likely be stable in the next 18 months. Yes or No are the only options. This does not help a decision-maker trying to ensure a region of the world remains stable; they need more information. Structured ACH allows the analyst to repeat the ACH exercise, drilling deeper into the analysis until the available evidence has been exhausted. For instance, the nation of Diania is expected to become unstable in the next 18 months. So what? The analyst can repeat the analysis with hypotheses positing that instability will be caused by H1: Inflation or by H2: Unemployment. H2 is disproven because the country has had high unemployment for the past ten years, and the informal economy now functions well enough that the official numbers no longer matter.

H1, however, is of concern. Diania is in a tense standoff with neighboring Ruania over their shared main river access, with Diania starting to build a hydroelectric dam for a self-sufficient energy source. The dam will not be operational for five years. In the meantime, Ruania is threatening to double the price of oil it now sells to Duania, and winter is coming. People are likely to be unable to afford fuel to heat their homes. In this case, the long-term energy independence strategy will have little value if people freeze to death next month due to inflated oil prices. NOW the decision-maker has something to work with beyond "likely to be unstable in 18 months". They can either support the dam building, provide a subsidy for the increased cost of oil, or offer to broker an agreement between the two nations. Adding structure helps ACH to provide decision-makers with far more actionable intelligence.

Have you used ACH? Can you think of a recent decision at work or home in which using a SACH matrix might have helped you decide? Use an ACH matrix to decide between two open positions you have saved on your favorite job announcement app. Does ACH help you keep your bias from the process and make it more objective? Or did it only show you that there is one more attractive position for reasons you had not articulated before you undertook your analysis? Now you know what they are, and even if your ACH shoots them down, you may still want them... Even if ACH does not eliminate bias, when applied to personal decisions, it can at least reveal what they are. Acknowledgment is half the battle against irrational decision-making.

One of the most important lessons of effective use of ACH and many other analytic methods is to ensure you are not wedded to proving your brilliant hypotheses. The most effective approach to achieving analytic objectivity and, thus, accuracy is to work hard to disprove them. Having a diverse analytic team will help with this, especially if all members are competent enough to have confidence in their contributions and collaborative enough to invite conflict and disagreement about what the findings may seem to indicate. 

The relationship between Alice and George illustrates the aggravating, frustrating team that, if they learn to communicate and check their biases and egos at the door, can ensure accuracy and, more importantly, the seeds for effective advocacy that ensures changes and correct decisions are made. The pressure George placed on Alice daily to prove that her findings along the way and in the end were accurate despite his disputation allowed her to have the certainty and confidence to keep challenging the common wisdom about the safety of x-rays on fetuses to ultimately save the lives of millions of children. Our work may not have that consequential or widespread result, but the results will have the same relative value in the context of our teams and our organizations.

Have you ever tried to disprove your hypothesis or someone else's? Taking this approach as a single member of a team is akin to playing the "Devil's advocate" and can make you wildly unpopular, hence the name. Have you ever resented a team member who played that role? If so, did you ever recognize their value in the project? It has been proven that just playing at being an oppositional advocate is not a truly effective method. They were being contrary for contrariness' sake and risked team cohesiveness. If the person playing the role is doing so for the right reasons, to ensure a lack of bias and full accuracy, the effect on the overall findings can be enormously positive, as in the case of Alice and George. One of the most important lessons of effective use of ACH and many other analytic methods is to ensure you are not wedded to proving your brilliant hypotheses. The most effective approach to achieving analytic objectivity and, thus, accuracy is to work hard to disprove them. Having a diverse analytic team will help with this, especially if all members are competent enough to have confidence in their contributions and collaborative enough to invite conflict and disagreement about what the findings may seem to indicate.

This brief overview is a good introduction to thinking about predictive modeling.

This fascinating talk describes big data and how it can be used today to ensure everyone in your family can have their favorite pie. Is this why Big Data exists? Well, sort of... It can help find the tiniest niche products and connect them to the people who want them, so in a way, it helps you get the pie you want and not make you always get stuck with apple. However, it has and will have many other uses, some of which may sound scary but still worth knowing about. How likely could it be that learning machines eliminate your job? What can you do to prepare yourself to deal with this future scenario? You might be able to apply a decision tree or ACH to help support your predictive analysis.

This article is a bit heavy on jargon for data scientists. Still, it makes the interesting case that what we often call prediction is only making inferences, identifying trends in data, and interpreting them, not using them effectively to predict what is likely to happen next. The article also makes the point that prediction may not be the endpoint of machine learning but that providing prescriptions on what to do about likely future outcomes will become the standard soon. Be sure to read carefully through the box office, marketing, and industry trend examples to see how to apply the concepts in the article.

Review this presentation and learn from its many examples of preparing data for improved analysis. However, it is optional, only provided for additional information, and is not central to your learning about predictive analytics.

Here is a small list of various analytic methods. How many of these methods have you used? How could these ideas be applied in your work?

This is a useful article for ensuring the validation of your statistical analyses. However, much of what a BI analyst does deals with qualitative data that may not as strictly adhere to the validation recommendations and requirements presented here. Within the field of intelligence analysis, much work has been done to identify ways to quantify qualitative assessments of validity, reliability, analytic confidence, and other aspects to ensure validation of intelligence findings, many modeled on statistical validation. Think about your most recent project, whether for work or school. How could you numerically and objectively evaluate the validity of your research?

This article describes what the human brain is doing when we define the problem (requirements and scope), plan for problem-solving (select datasets and filter or standardize and clean them for relevant information), and engage in the creative thinking process that is analysis. The author differentiates the creative process from the analytical process she terms "insight problem solving", but without creativity, the analyst would not know which methods to apply to the dataset and would have more difficulty expressing their findings in a way that is actionable for the decision-maker. To do this effectively requires a certain amount of empathy to understand what the decision-maker needs and in what format so that they can digest it most thoroughly and see the action steps needed for implementation.

It is interesting to see the problem-solving process laid out in a neurological sense when it is second nature to seasoned analysts. The author describes Tversky and Kahneman's thinking processes that allow analysts to figure out big problems while driving home in light traffic as if on autopilot. One analyst claims to solve most problems by walking away from her computer, riding her bike, or going rollerblading. Sometimes, like figuring out where we left the remote, the important things are fleeting and can only occur to us when we are doing something else. How might you have solved an important thinking problem in an unlikely place or while doing something non-analytical?

This article uses algebraic logic to solve very specific problems. Although intelligence analysis can be a bit messier than algebra, the process is essentially the same. We use our information (datasets) and the questions we need to answer (requirements) to define our real-world problem. We use analytic techniques, rather than linear equations, as our roadmaps, and we find solutions (findings) that we communicate in a standardized language, ensuring our decision-maker understands the reliability of our information, our confidence in our analysis, and the degree to which our estimates are likely to be the future outcomes. We go from A to B, but not always in a straight line.

This blog entry speaks about role-playing in intelligence analysis in the context of a historically-oriented game. Write about a time when you have found yourself unable to rely on a key component of a complex plan, whether it was the weather, a ride, a working computer, an accomplice, etc. Were you able to adapt? If so, you have shown the creative insight needed to be a successful intelligence analyst.

This article gives an overview of what BI is and how it can provide actionable information for a business. It provides a brief testimonial on a single case study and a well-done video giving examples of how it can be used. A key point from the article is that the activity of conducting BI can be more broadly dispersed throughout organizations rather than left to a small cadre of analysts with special technical skills who "owned" the data but did not necessarily understand how different departments and departments and managers could best use it. Constantly evolving dashboards and data packages make analysis more accessible. However, it is still important not to let the data be used randomly by anyone with access, as the results may not be sound.

Consider when you could have used big data to answer a manager's question. Did you have instant access to all the information you needed, or did you have to ask an IT or other area specialist? Was it difficult to obtain? Did you end up getting what you needed? Did you have to spend a lot of time manipulating the data to put it into a usable form? How does your organization collect, organize and disseminate its data?

This article describes using simulation programs for making IT decisions, but similar simulations are made to determine geopolitical and business outcomes based on specific conditions. These are different from scenarios as they are usually computer-based. In contrast, scenarios are typically role-played, even when they are "table-top exercises", meaning that the entire scenario environment has not been replicated but imagined in a symposium-like setting. Have you ever designed or participated in a simulation? Did it provide full information to inform the resulting decision-making?

Utilizing VR, AR, and MR for Big Data Visualization has potential with some clear advantages and disadvantages. Graphs and tables developed in this method are powerful storytelling tools and can offer new critical data visualization components in business intelligence. What role do you think MR will play in the development of data visualization?

This video highlights that business intelligence is evolving into analytics thanks to machine learning and visualization tooling advances. Analytics takes a more holistic and proactive view of a business by automating the process of extracting useful insights and looking to predict the future rather than affirm the past. Why is this key?

By classifying business intelligence appropriately, we allow ourselves to spot opportunities for investment and exploitation, increasing our ability to turn the data and insight we collect into profit. Business intelligence and its research can be divided into a taxonomy. This paper breaks that down. Even without data, are there areas that may contain similar opportunities?

Data visualization is both an art and a science, taking descriptive statistics and making them engaging and a jumping-off point for new questions. One of the leaders in the field, Edward Tufte, summarised the main principle of good data visualization as "Excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency. Graphics reveal data. Indeed graphics can be more precise and revealing than conventional statistical computations." Think about your favorite sport and how the graphics reveal more than just the score.

This paper discusses how graphics reveal data features that statistics and models may miss and why this is important. The paper also points out that we often accept graphics unquestioned as truth when sometimes they are incorrect or misrepresent the data by not giving the full perspective. Graphics raise questions that stimulate research and suggest ideas. After reading this paper, look at graphs used in news reports online and see if you can find at least one that fits each of the issues named.

This extensive listing of tools should be explored in-depth to apply some practical skills for creating your own data visualization tool.

Data visualizations are different from infographics. However, both allow you to use the available data and transform it into a compelling presentation or powerful story. These products are highly shareable on social media; when well done, they can give more mileage to your content.

Most social impact data is in textual formats. This creates or magnifies problems with reporting on the progress of social change initiatives. Visualizations allow you to efficiently communicate complex situations to stakeholders, be transparent with your analysis, and build trust in your insights. Watch this video. What questions would you use to achieve insights for funding?

Good data visualization uses different visual characteristics (color, size, orientation, etc.) to encode information effectively at higher densities than in plain text. Testing your visualizations with real users during the development process is important. This testing should focus on measuring the expressiveness and effectiveness of the presentation. How might people with a visual impairment be included in the development process?

OLAP systems allow flexible and dynamic questions to be asked of big data. By combining OLAP with multicriteria decision-making techniques, we can allow business executives to incorporate insights from real-world data into the systematic evaluation of different business options. This improves the quality of complex decisions and leads to better business outcomes with the same resources.

OLAP allows complex, multidimensional queries over large datasets to be rapidly answered. An OLAP system models the world as facts representing quantitative or qualitative measurements of things of interest. This article defines the different types and their pros and cons.

Anytime a new phrase of AI/ML innovation occurs, some people instantly claim that robots will replace us. One counterargument to this armageddon scenario is that it will not happen because of language nuance. However, GPT-3 is now on the scene. Read this explanation. Does it make you think we will enter some "brave new world"? What challenges and opportunities do you see this technology presenting?

This five-minute video gets to the core of explaining the newest AI technology in learning human language and characteristics. How long before GPT-3 is not recognizable as a computer?

This short video highlights some examples of careers that may disappear by being replaced by AI. How about parking garage attendants? Why do we need someone to hit a button to make the barrier arm go up once we pay, especially when we can only pay with a card? Although the friendly wave and smile and hearty wish for a nice day from that same attendant have not yet been replicated by computerized voices. What do you think about the concept of employment in the future?

"The use of OLAP data cube models for psychometrics opens the door to complex and dynamic uses of that data. This paper asserts that data cube modelling would allow larger, aligned, and integrated datasets to be constructed that could be used to build knowledge graphs or feed machine learning systems". Consider what this means and list some opportunities afforded by applying psychometric criteria to classifying content in e-learning systems that could improve your education.

To get to the crux of how the future workplace will be recast by robotics and AI, we need to consider how the next level of change will be more philosophical, sociological, and ethical. Considering what you know now about our present world situation, what do you consider the biggest issue when expanding the use of AI and robotics in the workplace?

After reading and reflecting on the results of this trends survey, are you fearful or hopeful? While the expert participants offered their highly valued insights, do you agree or disagree? What areas do you believe should be added to their list of concerns and potential solutions?

As AI capabilities and ubiquity are extended, humans must learn how to work with it and ensure that its influence on human well-being is positive. We must ensure that human judgment maintains importance and that we are up to the task. There will be immense economic pressure to adopt AI, and we must train a new generation of data scientists and data science users who can guide this adoption for humankind's benefit. Educators should explicitly consider these pressures and a world where data science is more central when designing student curricula. A highly specialized US agency senior official recently stated that the humanities will need to be taught more than ever, even as the world pivots to STEM (Science, Technology, Engineering, and Mathematics) education. He says we will need historians and philosophers, psychologists and sociologists to ensure engineers do not just build technologies because they can. Someone needs to be sure that the should". How do you feel about the use of AI in early education? What should other kinds of higher education and thinking be encouraged to support this effort?

The model described in this paper connects management information to positive changes in four categories of metrics for decision-making.

When leading a well-known organization, their leadership style is often studied and reviewed with every action and activity they undertake. People like Steve Jobs, Indra Nooyi, Ursula Burns, Warren Buffett, and Jack Welch have diverse skills that became significant at different levels and can be broken down into three general types: technical, human relations, and conceptual skills. Based on the responsibilities outlined and organizational structure, at what level is your managerial skill today? Which areas do you feel are your strongest and why? What are you lacking, and what methods can you deploy to upskill to aid your company and personal growth based on the critical thinking questions at the end of this paper?

Managers uphold the responsibility for decisions and actions made in a company. To achieve the most positive outcome, managerial performance is key to accountability. How managers leverage their ability to negotiate and motivate teams is just one area covered in this overview. The SMART model is a good framework (specific, measurable, achievable, realistic, and time-targeted) for goal setting. How many of a manager's roles have you already taken on? Which ones interest you, and which ones would you prefer to avoid? How can you adjust your management style within the confines of your organization to enjoy managing your teams and using your best skills most of the time?

Writing effectively for different circumstances is key in business communication, which must be objective and avoid subjective preferences as these "words" may have legal standing. Look at Table 2.3, which provides a small chart checklist to aid your writing in the future. Try the exercises at the end, as well.

A degree of technical writing is needed to communicate a specific purpose within a company. This is what memos offer, as you can see from this breakdown of the format and example. To hone your skills, write a letter using this as a guide, being mindful of these tips.

Mass communication takes many forms in business. Memoranda and letters are two generally used in an official capacity. Watch this brief video to understand when they should be used, how, their format, key components, and their differences. Have you ever written one? Pick a topic and practice writing a one-page memo about a policy change in using the office break room to improve social distancing. Remember, it will be posted for all your colleagues to see.

BLUF is the acronym for Bottom Line Up Front, a method of placing conclusions at the beginning rather than the end. The alternative is BLAB (Bottom Line at Bottom); most managers hate this.

This paper breaks down how to calculate the confidence interval, also offering an alternative approach with Error Bound. Take your time and work through the provided case studies, as that will aid your understanding of all the computations.

If you estimate some parameter, a confidence interval is a range of values of that parameter for a specified degree of confidence (called the 'confidence level'). If you're estimating the mean mass of an apple based on a basket of apples, what would a 95% confidence interval tell you about the range of masses?

Some newspapers and journals will use the phrase "margin of error". Other reports will not use that phrase but include a confidence interval as the point estimate plus or minus the margin of error. A confidence interval is another type of estimate, but instead of just one number, it is an interval of numbers. It is the range. These are two ways of expressing the same concept. Watch this video for a clear and concise explanation of the confidence interval. Take the short quiz at the end of the article to solidify your learning, although you won't be graded.

This video provides an example of how to build a particular dashboard. You will see as the speaker selects and adds requirements. You'll also be able to view how the result is visualized and can be interpreted.

Understanding how a person interacts with your site is key for further development. 'Usability' is a combination of factors covered in this short article. When you visit or view a site, what factors are key for your continued use? Think about the dashboards you have seen. Do they seem intuitive to use? Do you like how they function?

A structured development process with adequate stakeholder involvement is required to ensure a dashboard's success. This research assignment has provided mandatory dashboard criteria, categorized as dashboard content, analysis, visual effects, platforms, business culture, and maintenance. There is a discussion regarding "real-time" vs. "static". How would you define each, and how would either affect the design outcome of your dashboard?

This is an amalgamation of archived dashboards with population data on country-specific refugee/human rights issues from the UNHCR, which provides a fascinating view of how data can be presented in a digestible way through dashboards and well-thought-out visualizations. UNHCR uses various software and technologies in this archive. Take your time and study some of the visualizations and their sources.

Dashboards can help create meaning by highlighting the most important values of a raw dataset and giving context to the numbers. Now that you have some examples of free dashboards available, check them out and familiarize yourself with one.

This article summarizes how this company has three key insights on dashboard use for non-profit organizations. A good dashboard includes relevant metrics for board members, visually-pleasing formatting, and an informative story of how someone/something benefited from your organization's work. What are your relevant metrics? After reading the previous archive, you may already have a few design ideas. 

All software has limitations, and often these will stem from the expense or challenges of implementation, especially when integrating the dashboard with different solutions already deployed in the company. Some others to consider include: when comparing elements on a dashboard, causality can sometimes be misattributed due to a grouping, a common limitation of dashboards. Another is the misalignment of company priorities and metrics loaded into the software, often occurring when the person tasked with this job is unfamiliar with the company strategy and goals. Not knowing what is important causes challenges, so during your requirements phase, be sure to ask lots of questions, as nuance and context matter.

The greatest limitation of dashboards is the human that develops them. You must know what you plan to do with your dashboard so that it is designed to do that and not some other unnecessary things or not be usable to the people who need to use them. Watch this video and think about all the dashboards you see every day. Are there some that make no sense to you?

This video shows how small businesses should leverage Excel (a free tool) to track their KPIs, such as ad performance and conversion rate for ads, emails, Instagram posts, and other marketing channels. Which of the seven methods in this video might work for you?

This article revisits the managing project requirements initially discussed in Unit 2. The project team and their clients or other stakeholders must agree on what is expected when the project is complete, or someone will be left dissatisfied. An important aspect of this shared understanding is whether the requirements are expressed as outputs, outcomes, or benefits. This can depend upon the type of project and the perceived needs of the decision-maker that has requested the project. Misunderstanding the basic nature of what the client or stakeholder wants can derail even the most elegantly managed project.

Have you ever received coaching or mentoring from a manager? Was it didactic and condescending, or was it valuable? Did it enhance your knowledge or skills? Did it result in crashing or fast-tracking the project? Did the decision-maker consider the project outcomes valuable?

A key takeaway from this article is that "the purpose of operations is to keep the organization functioning while the purpose of a project is to meet its goals and conclude. Therefore, operations are ongoing while projects are unique and temporary". Why is this such an important distinction? Projects have a beginning and an end. Operations always hum along in the background while everyone else works on projects. Which category does BI fit into? What does the BI analyst do? List up to 10 activities. Which of these consists of persistent monitoring? Maybe there is an aspect the business decision-makers want to observe daily, weekly, or monthly. This could be production levels, hiring rates, training costs, or anything else. These would be considered operational activities. These are normally almost fully automated via dashboards with little input from the analyst once the program is set to run. There may be some analytic process you add before you submit the regular report, but you are not creating something unique and new. If this is all a firm uses its BI capacity for, it wastes a valuable resource that should be constantly put to work on long- and short-term projects to answer strategic-level questions.

This is a detailed how-to guide to ensuring your requirements are fully captured. It is a useful guidebook with processes you will tweak and adapt to each project plan. Is anything surprising or new to you? You will not use all of these approaches for every project, but having them at hand is useful when planning your next project.

In a famous 1964 U.S. Supreme Court case on obscenity and the limits of free speech, Justice Potter Stewart tried to explain "hard-core" pornography, or what is obscene, by saying, "I shall not today attempt further to define the kinds of material I understand to be embraced... [b]ut I know it when I see it"... This failure to effectively categorize and describe in a standardized way has been used since as a common witticism on lazy thinking. We are all often lazy in our thinking when we take for granted what we know. 

Think briefly about the phrase "vanilla is better than chocolate". Students have an immediate, passionate response to this statement, but when I ask them to define vanilla, then chocolate, we hear crickets in the classroom. Once they do a little research and learn that vanilla is a key component in the delicious, sweet-tasting chocolate we all love and that chocolate by itself is bitter, they all have to agree, many despite the earlier insistence of personal preference, that without vanilla, there is no chocolate as they know it, thus vanilla truly IS better than chocolate, as it is also delicious on its own.

Consider the phrase "cats are better than dogs". Do you approve or disapprove of this statement? Describe both animals' physical, behavioral, and other characteristics. Is this an appropriate statement like "vanilla is better than chocolate?" Or, do we need to add something? Should we say "cats are better than dogs at X"? or "cats are better than dogs for Y"?Getting the question right is the key to effective analysis.

Read the text of the memo. This is from the August 6, 2001, Presidential Daily Briefing, a document produced daily by senior intelligence analysts and presented to the top official of the U.S. government. When it was released in 2004, it was an instant indictment of how President George W. Bush and his national security team had failed to protect the United States from the 9/11 terrorist attacks in New York and Washington, DC. Do you agree? What does the memo tell the president specifically? What actions should he have taken based solely on the information in that memo?

The problems with the memo are legendary. The findings are completely inconclusive and are not actionable at all. They are awash in conjecture and talk about Osama bin Laden's intentions and desires. They do not tell about his capability to act upon those intentions and when, where, or how he will do so. The document also does not provide the top U.S. decision-maker any information about the analysts' confidence level in their analysis. A useful intelligence product will provide all of this and be actionable.

This is a nice guide for improving or "tightening up" your writing, ensuring clarity, concision, and directness, as described in the article. This is useful for any information exchange writing. Over the next week, note how often you use passive voice, are too wordy, or use cliché expressions or qualifiers.

This was the world's first style manual for intelligence analysts in the civilian world in 2008 and is still one of the best. Without standardized language within your organization, communication among analysts and decision-makers will remain ambiguous. It is important to agree on a confidence scale, so your decision-makers know how confident you are of your findings, the calculation of which will include consideration of source. 

Kristan Wheaton was a Professor of Intelligence Studies at Mercyhurst University for 16 years until he became the Professor of Strategic Futures at the U.S. Army War College in 2019. His blog posts explore the detailed aspects of producing intelligence estimates and thoughts about the field. His context is typically the national security arena more often than business, but his posts are valuable as they focus on the process, not the product types. This post uses the example of an Iran National Security Estimate (NIE), the most respected intelligence document in the U.S. national security context. He emphasizes the need for a shared lexicon for expressing analytic estimates, particularly careful, standardized use of words of estimative probability (WEPs).

What does confidence mean? Are you confident in your abilities as an analyst or project manager? What makes you confident? Your overall confidence in your ability is one kind of confidence. Analytic confidence is something else entirely. It calculates how much faith you have in the estimate(s) you provide to your decision-makers. Confidence can be affected by many factors, which should be included in your evaluation of how seriously your decision-maker should take your findings.

These factors include considering whether you had sufficient time to make your estimate and were working under optimal conditions for evaluating sources and making determinations based on them. It also includes your assessment of source availability and reliability. In addition to developing accurate, concise estimates, agreeing on a confidence scale within your organization is important. Hence, your decision-makers know how confident you are of your findings. This article goes in-depth about the importance of conveying analytic confidence to help your decision-makers take action with confidence themselves.

This post is about getting to know your decision-maker, not formalizing requirements. You will have to use your intuitive skill to observe how your decision-maker takes in and processes information to communicate in a way that makes them listen. Think of a time when you have had difficulty communicating with a boss. Did you misunderstand this communication gap with something else, such as organizational culture or a bad boss? How did you resolve the gap? Did you quit in a huff or get fired or sidelined? Or did you figure out the communication problem and realize that you were the one who needed to change your style?

Using evidence-based business decision-making is derived from the scientific method and the medical field. A highly prolific writer on intelligence, Prof. Steve Marrin, lately of James Madison University in Virginia, has written extensively over the past decade about using medicine as a model for developing the discipline of intelligence. This article offers a simple description of the process and its essential pros and cons.

This article discusses the traditional process by which teams build cohesion and working relationships.

Optimal analytic teams are diverse, with varying perspectives and skill sets, with a healthy respect for each member's area and level of competence. Depending upon their competence, they will quickly learn what tasks should naturally fall to which member. Even with the best members, teams undergo a growing process, which, if well managed, they form by getting to know each other, the norm, creating healthy work patterns. They perform well, getting positive feedback from the manager and the decision-maker, which makes them stronger for the next project. The storming occurs at any point in the project, sometimes early as they scope out their turf and become aware of others' strengths and weaknesses and sometimes later when project pressures increase. In any case, research shows that when bonds appear to be fraying, the skillful manager will get out of the storm so the team can work out their issues, then re-engages to ensure the team can effectively reconcile and proceed apace. Two analyst teams were working on government research projects with three-month timeframes. To enhance the performing process, the team leaders created innovative competitions between the teams to help form identities for the individual groups. One way was a dance competition between the two teams; another was a scavenger hunt. When the storms came at the end of the first month of the project, they started playing kickball twice a week. They would come to work the following days sore, bruised, and happy to share stories of their legendary triumphs the evening before. These were two of the university's best-performing teams in ten years of contracting with this government agency.

Have you been part of a team that went through all four stages? How rough was the storm? Was the team able to reform afterward? Some observers believe a team that does not go through a storming phase performs poorly. Perhaps the storm is an indication of their level of dedication. What has been your experience?

This article discusses three types of leadership styles. What kind of leader do you prefer – democratic, authoritarian, or laissez-faire? What kind of leader will you be when you have an analytic team to manage? 

The article also delves into the psychology of teams. Conformity is the enemy of imagination. The need for conformity of thought can devolve into groupthink or a situation when the lowest common denominator of thought prevails. This environment stifles creativity and diversity and causes teams to concentrate on getting a job done, not doing it in the most innovative ways.

This article effectively describes the tug-of-war in which the team manager must constantly struggle between external and internal forces. Consider the discussion questions about the politics of managing external stakeholders. This is where the manager must serve as a force field, holding back the external forces that desire to place the pressures of their influences and agendas on the team, which may include tight timelines or budgets or competing demands of the manager's immediate boss and that boss' boss. Once you add in the client's internal or external demands, the struggle becomes even more multifaceted, and the manager must become an expert juggler.

The study in this article provides reasons why BI is not fully implemented in some organizations. The thematic approach to determining why the technological tools are or are not used by managers provides an actionable set of areas where improvements can be made. One aspect that may relate to our discussions on asking the right questions is, "do managers really need to know how to use BI tools?" In many organizations, managers have enough knowledge about specialized data and tools to know how to task their specialists (BI analysts, in this case) to use them to answer their requirements. This can give analysts too much power in organizations with poor systems, whose data quality is questionable, or with authoritarian cultures where analysts fear giving the "wrong" answer. But in a highly effective environment, managers manage, and analysts analyze.

This brief talk by a champion poker player discusses the importance of analytic reasoning and information in making the best decisions and why we need to leave our intuition at home to rest up for small determinations with little consequence when we have big decisions that require data and analysis. What are the three lessons? When have you followed these lessons? Did they work well or not?

This page provides a quick view of how humans perceive the world around them. It points out how "no two people have the same worldview" because how we sense the environment depends upon our unique experiences. For instance, people who have spent time in combat may be averse to loud noises. Thus, this experience skews their initial information-gathering process in a loud, noisy environment. They may be so attuned to the discomfort the noise causes they may have difficulty noticing anything else in a new environment. This will cause them to later categorize that environment as little else than "noisy". 

Maybe it was a candle factory, and the colleague they were there with came away with a mainly olfactory perception that it was very smelly, even overwhelmingly so, because they have a perfume allergy and do not like strong smells. They did not even notice the noise. Another colleague, a visual artist, was positively engaged with all the colors used to create the candles. For them, the sound and the smell of the factory barely registered. 

This is the problem with going with your gut, especially if you are a boss. Your pre-existing perceptions will color your view of using data to improve decision-making. If your boss comes from an environment where "whatever the boss says goes", they may have little faith in the process or product your data analysis derives for them. They may think they know the answer to their or the client's question, and your findings would only confirm or question their anticipated outcome. A boss that insists on their gut as the final arbiter will only value your analytic findings when they confirm their view. 

This wastes time and resources for the organization if it supports a robust BI capability. Suppose your boss has an open mind or experience with data-driven processes effectively supporting decisions. In that case, your life will be easier, and your organization will benefit from your work, rather than only being supported by your boss' predetermined decisions that do not take the data into account. 

Sometimes the data takes us to places we do not expect. We must follow it to the end, and if the outcome is surprising, replicate the process to be sure the findings are accurate. If they are, the key will be in communicating them to an equally surprised decision-maker so they can be accepted as valid. When might you have gone with your gut and realized late that your perception took you in the wrong direction? Did you have an opportunity to correct the decision or interaction that resulted from starting with a misplaced perception? Did your realization come from absorbing and processing additional information or data?

This article presents a different perspective on learning organizations, more focused on the individual and how the organization best serves them. How can an organization avoid "losing out on its learning abilities when members of the organization leave"? What are the six factors related to time? These relate not only to constraints on learning but also to operational and project activities of organizations writ large. Keep these in mind whenever you plan a new project or for your current projects or operational support roles, and make sure they are considerations for defining your scope. Managing up is something analysts do all the time. This happens when you work with your managers to refine requirements, develop your TOR, and define your scope. It is also a key skill for ensuring your analytic findings find a receptive audience, despite expected results. Have any of these tips helped you to effectively "manage up" in the past? How could you apply some of them in the future to communicate more effectively with your decision-makers?

Read this article about personal and organizational learning. Do you believe McCall's statement that "leaders are made, not born, through the trial and error learning that occurs through actual work: adversity, challenge, frustration, and struggle lead to change"? Does your organization provide those kinds of learning opportunities? Or does it punish mistakes? Does it embrace other kinds of learning? Do you agree that "too many organizations focus on learning the wrong things"? Have you had this experience? How do you think an organization can be sure that its learning offerings or plans align with its strategic priorities? Do you have a mentor? If not, where and how can you find one? Does this person have the life experience to teach you what you need to learn? Are they approachable and ready to listen when you need them?

This article presents a nice overview of risk planning and mitigation implementation common for most organizations and projects. In the case of BI projects, the risk planning process can be similar, but the pitfalls are unique to BI. They are most likely to be related to technology and data.

This article revisits managing project requirements. The project team and their clients or other stakeholders must agree on what is expected when the project is complete, or someone will be left dissatisfied. An important aspect of this shared understanding is whether the requirements are expressed as outputs, outcomes, or benefits. This can depend upon the type of project and the perceived needs of the decision-maker that has requested the project. Misunderstanding the basic nature of what the client or stakeholder wants can derail even the most elegantly managed project.