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How to Join the Analytics Revolution

No matter how hard we've all tried, we seem to have gotten stuck at around 30-35 percent of potential consumers using business intelligence (BI) and analytics systems. This statistic—featured quite prominently in presentations at a recent Gartner Data & Analytics Summit—is both good and not-so-good news for analytics professionals and business leaders alike.

First, the good news: the number is growing, up from the 20-25 percent range over the last five years. But the not-so-good news is that expansion has stalled. Modern BI, built on the tenets of self-service, visualization, data preparation and collaboration, promised BI for all. Indeed, it has moved the ball forward. Yet, the basic BI paradigm remains– content developers produce the information, and business consumers consume it. Evolution of this inside-out model has gotten us so far.

Taking an Outside-In Approach

Now, it's time to take an outside-in, revolutionary approach: Make BI and Analytics work the way we work, not force us work the way the BI system operates.

Augmented analytics—where AI, Machine Learning, and natural language query, processing, and generation underpin everything you do – are key to this revolution. But it's more than that. It's about using the power of AI to adapt BI to your world… providing freedom within a framework of governance of trust.

Let's explore some concrete examples of how BI must change and use everyday metaphors to drive sharper insight:

  • Search in natural language for what you're looking for. Don't endlessly click, trying to find a nugget of insight just to get to an answer.Generate new content based on the result of your search, or surface existing content that already exists.Either way, you get to the answer more quickly in a way that's ingrained in how you think.
  • Ask a question, get an answer. Use spoken or written language (not just English, but in dozens of languages) to ask questions of the data— "Show me sales pipeline by product family and region for the second quarter of 2020."Let the system generate relevant analytic content on the fly. Interact in the form factor that makes the most sense for you – on your mobile device, through a digital assistant, on the web.You access the information you need without having to enter the BI application if you choose not to.
  • Guide you to what the data really means. Explain what influences a fact or a measure, even a data set.Don't force a user to fiddle with the data to see if they can figure it out themselves, spending time hunting and pecking and potentially setting them off in the wrong direction. Avoid BCS (blank canvas syndrome) and get a running start on understanding your data better. Let the system do the heavy lifting for you.
  • Describe the answer in words. When you spend so much time interpreting visualizations for others by writing your own narratives after the fact, let the system do it for you with built-in natural language generation—even specifying the level of detail you want to see.
  • Recommend what you should be looking at. Knowing your interests helps serve up information before you know you need it. Your role, your workgroup, your preferences all personalize the experience to you, suggesting options along the way.
  • Tell stories with your information. Consumers need context. Don't just give others a series of fancy visualizations and hope they understand it.Construct a story to get your findings to a broader audience so they know exactly what you've found and recommended
  • Act on your insights through embedded analysis. Just pointing out the facts gets you so far.Make sure you close the loop with targeted action.Embedding analysis in the context of application, role and responsibility is key.For example, you identify a shortfall in inventory at one location to fill orders yet oversupply in another.Don't just stop there.Act in the application and re-allocate goods to alleviate the shortfall before it becomes a problem.

We're Well on Our Way, But There's Much to Do

There are numerous examples of organizations using these principles to expand access to data and bring more consumers into the analytics fold. A high- tech manufacturer uses natural language query and mobile to engage operational managers with sales forecast data in the moment—opening up hundreds of new consumers in the process. A financial institution developing a digital assistant to enable access to financial metrics as easily as "Show me x, y, and z for my organization." Now, access to information is open to anyone.

Surely, there's lots more to be done, and old habits die hard. But organizations are chomping at the bit to open up Analytics and BI to more people. It's just that you'll likely have to take a forward-thinking, outside-in approach to expand beyond the 35 percent.

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