In Analytics

Business analysts, data scientists, authors and bloggers sometimes describe data mining and analytics as “finding the story in your data.” It’s a good metaphor although I want to extend the notion from a single story that can be told to multiple stories because there are actually a multitude of stories in your data. Each story can be told based on the types of data your organization collects and by the analytics applied. Stories in your data can be about:

  • Your operations, including specific facilities, machinery and assets
  • Your sales activity
  • Your vendors and suppliers
  • Situations and events that affect or impact your organization
  • Your customers, their buying journey, their interaction with your organization and its website
  • If your organization has connected to the Internet of Things (IoT), then stories can also be told about the things, their environments, and their end-users including how they use and interaction with your products

Analytics can tell stories in many different ways. Which is to say that the types of analytics that you use will tell a different story. Knowing the particular type of stories you are interested in and want to benefit from most influence your criteria for choosing analytics. One type of storytelling, let’s call it situational intelligence, is a comprehensive fact-based story about what happened with details about where, when, why and how. Such stories contribute to understanding situations and root cause(s) that can drive decisions and actions to prevent future occurrences. Situational intelligence stories are therefore extremely valuable for understanding situations so you can take prompt and appropriate action to achieve success.

Your stories become enriched with more useful and actionable detail as more analytics are applied. The following brief high-level examples illustrate this point. Analytics enrich stories by identifying outliers, groups, patterns, and top and bottom performers. Forward-looking analytics (aka predictive analytics) enrich stories by foretelling things such as demand, foretelling what is most likely to happen. Other types of analytics identify and describe relationships between entities and enrich the stories with insights into ripple effects. Analyzing relationships enables telling detailed stories about the magnitude of situations – what else is or might be affected and to what extent. Analytics also is able to create a successful story ending by considering all possible outcomes and then choosing the best or optimal outcome. It should now be clear how multiple different analytics can be applied and combined to tell very rich and detailed stories. Actionable stories in fact, because the stories in your data drive investigation, decisions, actions and the best possible outcomes.

In addition to applying and combining different analytics to research the stories to compose compelling content (metaphorically speaking), there is another primary method of enriching stories – with data. After all, the data that is available to the analytics is the foundation and critical component. It is generally the case that the more data available, the more comprehensive of a story can be composed. And not just more data from the same source (e.g., 5 years of historical data versus 2 years) but data from complementary and supplementary sources. Your own data sources can and should include data streams from your website and/or your integration with the Internet of Things (IoT). You can and should augment your data from relevant external sources such as web services that provide weather, traffic, spot market prices, exchange rates, etc. Using multitude sources of data (referred to as broad data) is similar to how a journalist doing research for a story will seek and use all practical and relevant sources of information to tell the most accurate and complete story possible.

Let’s take this analogy one step further – consider a story about a customer’s use of a product that your organization produces and sells. The [usage] story can be told just by data captured from the customer’s interaction with your organization. If the customer is using a web-based service and/or an IoT device, your organization can receive an ongoing stream of data. If all of the data about the customer is fused with data from another system, such as the ecommerce system that captures aspects of the customer’s buying journey, then the resultant story becomes richer. Your organization can use such stories to understand and predict how the customer might acquire new products, what else the customer might need or want to buy, and how they might interact with your organization in the future. A more compelling story – a success story; one that can drive personalized experiences and sales offers that have a high likelihood of achieving customer retention and increased revenue.

More and more organizations are applying analytics to gain a competitive advantage using the actionable stories in their data. You too should embrace analytics to ensure your own success stories.

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