Companies know there is value in their data — that is why they have saved it. The volume of event data (any data that triggered by an event, like a keystroke, transaction, or sensor reading in products such as websites, mobile phones, sensors, and IoT devices) has grown enough to warrant the “Big Data” reference. The tools for extracting the value from this data are rapidly evolving, and the latest ones democratize access to insights across all employees of an enterprise.

In a series of blog posts, I wish to explore why and how self service analytics will democratize access to insights within big data. Here is an overview of key points:

  1. Data exploration is a process rather than a singular event [more…]
    • Similar to web browsing, every new question is informed by the previous question’s answer
    • Discovered insights are frequently not related to the first question, but the path of exploration, from question to answer to next question and so on…
  2. Self-service analytics help overwhelmed data scientists and frustrated data-driven employees
    • Data scientists get a lot of simple questions; instead they should work on the hard problems they got their PhDs for
    • Data-driven employees are frustrated by the long turnaround times associated with human workflows and bottlenecked resources
  3. Technical features that enable flexibility and remove dependencies
    • Flexible MDM via ingest-time schema
    • Flexible ETL via read-time derivations
    • Flexible Sampling via large volume capabilities
  4. Data re-discovery: capturing and sharing of insights
    • Knowledge captured so as to enable re-discovery
    • Network effect from leveraging co-workers’ insights
  5. Customer behavior is the closest proxy for customer needs
    • What Customer Did > What the Customer Said
    • Customer Behavior Data > Opinions about Customer Needs
  6. Self-service analytics enable nimbleness: keeping up with customers
    • Self-service = always staffed
    • Self-service analytics insights compliment external marketing research
  7. Boosted productivity and better products with self-service analytics
    • Democratized analytics is the missing piece to fully unlock the information age in the enterprise
    • productivity gains from understanding end-customer needs and behavior could be bigger than productivity gains from personal productivity software

Across industries, across job roles, self service analytics will empower employees of varying skill levels to dive into the data, and enrich their own understanding of the customer and the business. How many customers do what, in what order? What patterns are emerging? What anomalies can be detected, and what seems to be the root cause of them? How to provide more value to customers, and in return extract more value from them? Better understanding of historical behavior empowers better understanding of future behavior, or in other words the understanding of customer needs.

Tell me what you think!