My previous blog, “The Death of Why – Part I,” discussed how big data analytics is negating the need to understand “why” things happen. With the vast amounts of detailed data available and high-powered analytic tools, it is possible to identify what works without having to worry about why it worked. This can enable us to “think different” about the user experience and the context in which we present business insights to our users.
A New Analytics Approach
The capabilities of big data analytics to identify significant, material, and actionable insights buried in the data, without having to understand why these insight occur, provides the foundation for an entirely different user experience. Instead of presenting an endless series of reports and dashboards in hopes that users can discover what’s important to their business, we can instead reverse the traditional analytic process and present to the users only that which is important to their business. We do this by starting with the interesting insights, and then provide the relevant data, reports, and dashboards required to support those insights (see chart below).
User Experience Ramifications
The user experience can be greatly simplified to present only the information or insights needed to optimize the business. The user experience could focus on two types of insights:
- Observations are “unusual” behaviors or performance (e.g., 2 standard deviations outside normal performance, 200% above/below predicted performance) that might require further investigation by the user. Observations would leverage simple analytic models to identify performance situations operating outside of normal boundaries. These would be potential starting points for a more detailed investigation by the user. For example:
- Did you know that product A’s sales to the [Soccer Dads] customer segment is 150% of what it was last period?
- Did you know that marketing campaign [2011 Back To School Sale] is 50% below forecasted conversions with only 2 weeks left in the campaign?
- Did you know that the variance level on machine [Charles City Plant Turbine #120] is 20% outside the normal control boundaries?
- Recommendations are specific actions that are generated based upon a detailed analysis of the current state of the business. Recommendations would leverage advanced analytic modeling and real-time feeds to score performance, analyze the key business drivers and variables, update models, and make specific recommendations. For example:
- We recommend marking down the product category [Christmas Lights] by 25% starting December 9, and increasing the markdown to 50% on December 16.
- We recommend increasing the media budget by 22% on display ad [Chevy Suburban] and decrease media budget 33% on display ad [Chevy Volt] for the remainder of the campaign [Holiday Season].
- We recommend repairing your [Maytag Model 3200] washer’s drum engine within the next 5 days because there is a 95% probability of product failure.
The user experience could start with a series of Observations and Recommendations, priority ranked by their potential impact on the business. If the user then wanted more details on the Observations and Recommendations, they would select the [More] button to get the supporting details (see example below using the iPhone as the delivery platform).
The analytics that underpin the Observations and Recommendations could be quite complex. The analytics could be personalized and self-learning so as to be constantly fine-tuning the analytic models based upon the users’ feedback on what they like, didn’t like, and why (think Pandora).
Massive, detailed data sources, coupled with more powerful analytic tools, provides the capabilities to identify significant, material, and actionable insights in the data without forcing users to have the analytic skills or training to quantify why things happened. It allows us to provide a completely different user experience – one that is focused on providing greatly simplified Observations and Recommendations – to help the business users optimize their key business initiatives.