Think Like a Data Scientist

Step 1: How do I identify my Key Business Initiatives?

Key business initiatives include what the organization plans to achieve with their business strategy over the next 9-12 months; usually includes business objectives, financial targets, metrics and timeframe.

A Business Initiative supports the business strategy and has the following characteristics:

• Critical to the immediate-term business and/or financial performance (usually 9 to 12 month timeframe)
• Documented (communicated either internally or publicly)
• Cross-functional (involves more than one business function)
• Owned or championed by a senior business executive
• Has a measurable financial goal
• Has a well-defined delivery timeframe
• Delivers compelling financial or competitive advantage

Step 2: Develop Stakeholder Personas

We want to develop personas for each of the business stakeholders to understand better their work characteristics and job characteristics. Understanding this helps to capture the decisions and questions that these stakeholders must address with respect to the targeted business initiative.

A persona is a 1-2 page “day in the life” description that makes the key business stakeholder “come to life” for the data science and User Experience (UEX) development teams. Personas are useful in understanding the goals, tasks, key decisions, and pain points of the key business stakeholders. The persona helps the data science team to identify the most appropriate data sources and analytic techniques to support the decisions that the business users are trying to make and the questions that they are trying to answer. Personas are created for each type of business stakeholders affected by the given business initiative.

Step 3: How To Identify Strategic Nouns

Strategic nouns are critical to data scientists' thinking process because these are the entities from which to gain new, actionable insights, that ultimately help build analytic profiles.

Examples of strategic nouns include:

Step 4: How Do I Capture Key Business Decisions?

What decisions do the business stakeholders need to make about the strategic nouns, in support of the targeted business initiative. What data insight would support those decisions? These help to form the basis for generating an actionable analytics recommendation that can accelerate a targeted key business initiative.

Capturing and validating these decisions is critical to the "Thinking like a data scientist" process. Leading organizations like Uber and Netflix are disruptive because they build a business model that seeks to simplify their targeted customers' key "decisions.” For Uber, one of the customer decisions that they address is "How do I easily get from Point A to Point B?" For Netflix, one of the customer decisions that they address is "What content (movie, TV show) can I easily watch tonight?"

Step 5: How Do I Brainstorm Business Questions?

Brainstorm with each of the different stakeholders the decisions they need to make with respect to each strategic noun or key business entity in support of the targeted business initiative.

The Evolution of Analytic Questions

Step 6: What is "By" Analysis?

The “By” analysis technique exploits a business user’s natural “question and answer” enquiry process to identify new data sources, dimensional characteristics, variables and metrics that could be leveraged by the data science team in building the predictive and prescriptive analytic models to help predict business performance. The “By” analysis leverages a business stakeholder’s natural curiosity to brainstorm new:

• Metrics, measures and key performance indicators
• Dimensions (e.g., strategic nouns) and the attributes and characteristics associated with those dimensions or strategic nouns
• Areas for potential analytics exploration

The “By” analysis uses a simple “I want to [verb] [metric] by [dimensional attribute]” format to capture the business stakeholder brainstorming process and uncover new data and analytic requirements. The “By” analysis format looks like such as:

“I want to”

• Verb such as [see, know, report, compare, trend, plot, predict, test score]
• Metric such as [sales, margin, profits, social media posts, comments, physician notes, vibration levels, sensor codes]

“By”

• Dimension or dimensional attribute such as [city, state, zip code, date, time, seasonality, product category, remodel date, store manager demographics]

Here is a “By” analysis example:

• I want to [report] [sales and product margin] by… [product category, store, store remodel date, day of week, store demographics, and customer demographics]

Step 7: What is Score Technique?

The purpose of the “Score” technique is to look for groupings of strategic noun dimensions and attributes that can be combined to create a more predictive and actionable score. These scores are critical components of our “thinking like a data scientist” process by supporting the decisions that we are trying to make, and/or what actions or outcomes we are trying to predict with respect to our targeted business initiative. Scores are very important constructs in the world of data science, and can help to cement the business stakeholders’ buy-in to the data science process. The best familiar score example might be the FICO score, which combines multiple questions and dimensions about a loan applicant’s finance history to create a single score that lenders use to predict a borrower’s ability to repay a loan.

Step 8: Put Analytics Into Action

Facilitate the development of a compelling and actionable user experience by starting with a simple “Recommendations Worksheet.” The “Recommendations Worksheet” ties the decisions that our business stakeholders need to make (captured in Step 4) to the predictive analytics or scores that that the data science team is going to need to build. The “Recommendations Worksheet” starts with the decisions captured in Step 4, and then identifies the potential recommendations that could be delivered to the business users (or consumers) in support of those decisions. Finally, the worksheet captures the potential scores (and the supporting variables and metrics) that can be used to power the recommendations.