Value Engineering: Secret Sauce For Data Science Success

Bill Schmarzo

The business, economic, and social good that can be delivered courtesy of data science is almost unbounded; it has the potential to improve healthcare, public safety, transportation, education, environment, manufacturing, communities, and the overall quality of life. If what your organization seeks is to exploit the potential of data science to power your business models, then your next question is “How do I achieve that?” And that’s the topic of this blog.

The how: data science value engineering

Let me introduce you to the data-science value engineering process (see Figure 1).

Figure 1: Data Science Value Engineering Framework

Let’s drill into each of the steps of the data-science value engineering framework – the “How to do it” framework.

Step 1: Identify a key business initiative

As Stephen Covey discussed in his famous book “The Seven Habits of Highly Effective People, “Begin with an end in mind.” We have found that the How conversation must start with a focus on the organization’s key business initiatives — that is, what is important to the business over the next 12 to 18 months. Your organization might have business initiatives such as:

  • Reduce inventory costs
  • Improve supply chain reliability
  • Reduce unplanned operational downtime
  • Improve customer retention
  • Improve yield
  • Improve “first-time fix”
  • Improve supply chain reliability and quality

These are all excellent initiatives. You just need to invest the time to understand and research them thoroughly, including the business, customer, environmental, and operational benefits, and the metrics and key performance indicators against which progress and success will be measured.

Step 2: Identify key business stakeholders

Once you identify the targeted business initiative, identify the business stakeholders who either impact or are impacted by the targeted business initiative. This should be at least four different organizations because you want diverse perspectives on how these organizations plan to address or support the targeted business initiative.

We use personas (a design thinking tool) to help us understand the stakeholders with respect to their work objectives, work environment, key decisions, questions, and impediments. Check out “Refined Thinking like a Data Scientist Series” for more on personas.

Step 3: Identify, validate, value, and prioritize the decisions

Now the money step! Yep, once we know the targeted business initiative and the key stakeholders, then we want to drive facilitated collaboration across the different stakeholders to identify, validate, value, and prioritize the decisions that these stakeholders need to make in support of the targeted business initiative. We use the prioritization matrix to drive consensus across the different stakeholders on the top-priority decisions (see Figure 2).

Figure 2: Prioritization Matrix

The prioritization matrix is the most powerful business alignment tool I’ve ever used. It works every time … if you do the proper preparation work and are willing to put yourself in harm’s way. See “Prioritization Matrix: Aligning Business and IT On the Big Data Journey” for more details.

After completing Step 3, everything else is “easy.

Step 4: Identify supporting predictions

For each of the top-priority decisions, identify the predictions that each stakeholder needs to make in support of those decisions. Sometimes it is easier, when working with the business stakeholders, to ask them what questions they need to answer to support their key decisions. Then it’s a simple process of converting those questions into predictions. To help organizations frame or understand the potential of data science, I start my customer conversations with a very simple question:

How effective is your organization at leveraging data and analytics to power your business models?

Figure 3 shows some questions and supporting predictions from an agriculture example.

Figure 3: Transitioning Questions into Predictions

For example:

  • “What were revenues and profits last year?” (the question) converts into “What will revenues and profits likely be next year?” (the prediction).
  • “How much fertilizer did I use last planting season?” (the question) converts into “How much fertilizer will I likely need next planting season?” (the prediction).

See, pretty simple process.

Step 5: Identify potential data sources and instrumentation strategy

The next step is to work with the business stakeholders to identify what data might you need to make those predictions. The trick for fueling data brainstorming builds upon the “predictions” identified in Step 4. We simply add the phrase “and what data might you need to make that prediction?” to the prediction statement.

For example:

  • What will revenues and profits likely be next year, and what data might you need to make that prediction? The data-source suggestions might include commodity price history, economic conditions, trade tariffs, fertilizer and pesticide prices, weather conditions, fuel prices, and more.
  • How much fertilizer will you likely need next planting season, and what data might you need to make that prediction? The data-source suggestions might include pesticide and herbicide usage history, weather conditions, crops to be planted, pest forecasts, soil conditions, and more.

In the end, we’ll get a matrix of data sources mapped to each key decision (use case) that we can use to prioritize our data and instrumentation (IoT sensor) strategy (see Figure 4).

Figure 4: Data Value Assessment Matrix Example

Step 6: Identify supporting architecture and technologies

Finally, we’ll need a Big Data and IoT architecture and technologies upon which we can build the solution that delivers the business value. For example, in an IoT architecture, one will need to consider the architecture and technology choices at the edge, platform (sometimes also referred to as “fog”), and at the enterprise (or cloud) level.

While the architecture and technology choices and integration are never easy, at least you’ll understand what technologies you will need and what you won’t need.

By the way, I’m happy to announce the release of my third book: “The Art of Thinking Like A Data Scientist.” This book is designed to be a workbook – a pragmatic tool that you can use to help your organization leverage data and analytics to power your business and operational models.

This article originally appeared on LinkedIn and is republished by permission. Hitachi Vantara is an SAP global technology partner.

Join the webinar on September 11th to learn about the guiding principles and the skills needed to operate SAP S/4HANA Cloud

Bill Schmarzo

About Bill Schmarzo

Bill Schmarzo is CTO, IoT and Analytics at Hitachi Vantara. Bill drives Hitachi Vantara’s “co-creation” efforts with select customers to leverage IoT and analytics to power digital business transformations. Bill is an avid blogger and frequent speaker on the application of big data and advanced analytics to drive an organization’s key business initiatives. Bill authored a series of articles on analytic applications, and is on the faculty of TDWI teaching a course on "Thinking Like A Data Scientist." Bill is the author of “Big Data: Understanding How Data Powers Big Business” and "Big Data MBA: Driving Business Strategies with Data Science." Bill is also an Executive Fellow at the University of San Francisco School of Management, and Honorary Professor at NUI Galway at NUI Galway J.E. Cairnes School of Business & Economics.