BUS607 Study Guide

Unit 2: Transforming to a Data-Driven Decision-Making Enterprise

2a. Differentiate between the continuums of the data-driven decision-making implementation process and recognize the milestones that must be completed along each continuum

  • What does the term analytics mindset mean?
  • What are the 3 continuums in the Data-Driven Decision-Making Change Model?
  • Why should each stage of the continuums of the Data-Driven Decision-Making Change Model be implemented at the same time?

To make informed decisions based on data, managers must have an analytics mindset that enables them to understand how the data is derived, interpreted, and communicated. Therefore, they need to develop an analytical skill set that helps them know what makes sense and understand where analytics adds value. This enables them to be confident enough to ask pertinent questions of the analyst. Read this article to learn the benefits of managers developing an analytical mindset.

The three continuums in the DDDM change model are:

  1. Organization / people
  2. Data / technology
  3. Process / workflows

All three of these continuums must be completed in stages for a successful DDDM implementation. If a continuum surpasses or lags behind the others, the implementation may not have the proper processes in place from the other continuums to support effective decision-making.

Implementing DDDM requires more than just data and technology. It requires 2 other continuums, along with data and technology, to be successful.

To review, see Data-Driven Decision-Making Change Model.

2b. Evaluate the critical success factors for building an analytics-focused organization

  • What are the six critical success factors for implementing DDDM?
  • Why are these success factors important for DDDM?
  • What are the differences between a business problem that must be measurable and the operation repeatable over a specific time period?

There are a number of critical success factors in order for the DDDM program to be effective and have the proper impact on the organization. These include:

  1. Executive support for the mandates required for the analytics, sponsors, and champions
  2. A well-defined business challenge or query
  3. Lots of data from internal and sometimes external sources
  4. The right team and skillsets supporting the initiative, including a champion, technical resources, and subject matter expert
  5. Integration into the organization's overall operations and processes to capture the right information
  6. The ability to track results and update models to determine if predicted outcomes were supported by the analysis

For a business problem to be well-defined, it must be measurable and the operation repeatable over a specific time period. To be measurable, the results must be able to be measured or counted to determine if the prediction was accurate. A repeatable operation requires that the chosen attribute to measure must occur regularly and have a repeatable pattern. A specific time period requires the variable to have a specific beginning and end, (week, month, quarter, etc.).

To review, see Critical Success Factors.

2c. Examine how management uses data outcomes to guide organizational decision-making

  • What are the differences between the 4 different analytical models to frame tactical and strategic questions?
  • What are the various analytics domains that could be deployed in an organization?

Analytics can be used to assess and visualize decisions, describe the implications of historical data, predict and model future expectations, and optimize internal processes. The ability to navigate and derive value from big data is critical to successful organizational management.

There are four primary tactical and strategic analytical models managers may use to frame analytics:

  1. Descriptive analytics: what happened
  2. Decision / diagnostic analytics: why it happened
  3. Predictive analytics: what will happen
  4. Prescriptive analytics: how we can make it happen

As the organization progresses from one stage to another, the benefits of the analysis increase while the implementation complexity increases. The focus also shifts from what happened in the past to what we can do to impact the future. Stage 5, cognitive analytics, includes artificial intelligence analytics.

Analytics can impact nearly every domain in an organization, including finance, marketing, talent, customers, risk management, transportation, and sales.

To review, see Developing an Analytical Mindset.

Unit 2 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

  • analytical model
  • analytics
  • analytics mindset
  • business challenge
  • change model
  • cognitive analytics
  • continuum
  • critical success factors
  • data / technology
  • descriptive analytics
  • diagnostic analytics
  • measurable
  • organizational / people
  • predictive analytics
  • prescriptive analytics
  • process / workflow
  • repeatable
  • specific time period