Building a Data Products-centric Business Model

When I was the Vice President of Advertiser Analytics at Yahoo!, I painfully learned that my targeted user personas (Media Planners & Buyers and Campaign Managers) didn’t want more data in helping them optimize their marketing, campaign, and advertising spend across the Yahoo! Ad Network.  Heck, they didn’t even want analytics!

The aspirations for these personas were to become VPs of Digital Marketing or Directors of Social Media Marketing or Digital Advertising Executives.  The last thing they wanted was to become data analysts who had to churn through massive data sets to uncover the audience, messaging, and campaign insights needed to achieve their marketing, campaign, and advertising objectives.  In fact, that’s what they wanted Yahoo! to do for them!

Given their campaign objectives and goals (views, clicks, shares, comments, conversions, etc.), they wanted Yahoo! to tell them the ideal target audiences, the best websites to engage those audiences, the best times of day to reach them, keywords to buy, and what messaging and content were most interesting to them (based on views, clicks, shares, comments, and conversions). What these personas wanted from Yahoo! were decisions – decisions that optimized across these objectives to deliver meaningful outcomes that helped them attain their desired marketing, campaign, and advertising objectives.

They wanted to decisions.  What they needed were data products.

“The machine learning (ML) platform offers a flexible environment in which data science teams can manage machine learning in an efficient, governed, and scalable way. It helps data science teams standardize features, ML models, and software code; then reuse and share those artifacts as they integrate with business processes.”

The biggest opportunity for making the machine learning platform relevant to the business is clarifying how we package and monetize the machine learning platform (and the underlying, relevant data sets) by integrating the concept of Data Products (that drive specific, meaningful, relevant business outcomes) into the architecture (Figure 2).

Data Productsare a category of domain-infused, AI/ML-powered apps designed to help non-technical users manage data-intensive operations to achieve specific, meaningful, relevant business outcomes.

Well, let’s review some key requirements in building a Data Products business model.

The defining characteristic of a Data Product is its ability to leverage customer, product, service, and operational insights to “intelligently simplify” the decisions that customers are trying to make. Delivering successful data products requires an intimate knowledge of the customers’ intent, what outcomes they are seeking, and how success will be measured.

The “Thinking Like a Data Scientist” methodology provides a framework and design templates (i.e., Stakeholder Persona) to help the Data Products development team to clearly understand and align around the stakeholder’s objectives and goals, desired outcomes, current pain points, key decisions, and KPIs against which to measure decision effectiveness (Figure 3).

Another critical design template in defining the Data Product stakeholder usage requirements, benefits, and pains is the Stakeholder Journey Map.