BI at an Inflection Point (Part 1 of 2)
The BI industry is at an inflection point, and we're about to watch the whole thing change again. We examine the macro trends worth watching.
By Charles Caldwell, senior director, solutions engineering, Logi Analytics
The BI industry is at a significant inflection point. The next significant shifts are starting to hit, and I wouldn't be surprised if within the next 18 months we see a very different landscape in BI.
There are several signs that an inflection point is coming:
- Traditional "big-stack" BI players like MicroStrategy, IBM and Oracle have been shrinking over the last few years
- Visual analytics has arrived, with pure-play vendors being defined as leaders in the Gartner Magic Quadrant and big-stack vendors answering by acquiring or building their own data discovery products
- There are dozens of vendors in the space, with some long-time holdovers that have found niches, and new, emerging players that are making up for the big players shrinking and driving continued industry growth
- Analysts, most notably Gartner, are changing their formal definition of the space and its players, and are adjusting the process by which they judge the vendors; for example, Gartner split it BI Magic Quadrant into two in 2014 and the assessment criteria and process changed significantly
- There's much talk about further consolidation of vendors in the industry
Is this just a typical industry consolidation cycle or is there something more going on? I'd argue that the BI industry is at an inflection point, and we're about to watch the whole thing change again.
There are some macro trends that are driving us to that inflection point. Why, for example, would traditional BI be shrinking while the industry overall is growing? Here are some of the larger trends I believe are stirring up the BI industry.
Disruptive Power of the Information Economy
The information economy is here, but the impact of its arrival is really just starting to be felt. There are two key aspects that I think will drive a shift in the BI landscape.
First, there are many traditional business models in which the information surrounding the transactions has become worth more than the transactions themselves. In these cases, you see enterprises moving from being a seller of a product or service to increasingly selling information and analytics. Consider agricultural company Monsanto that sells seeds, chemicals, and tools, and has entered into the business of using data to tell an individual farmer what to plant, when to plant it, how much to water it, when to apply various chemical, and when to harvest it to maximize yield. This plan is based on individual plots of land, and formulated from meteorological and other historical data about that individual plot. The information drives the purchase and use of their products, but the information is increasingly the largest part of the value in that value chain.
Second, much of the innovations of the information economy have significantly decreased or nearly eliminated many of the costs of doing business. One way to view a business model is as a bundle of transaction costs. You build a business model in order to scale value production while optimizing those transactions costs. When some of those costs go to zero (or nearly so), business models fall apart. Think about iTunes and the record industry. Now forget iTunes and think about all the artists publishing music directly to their fans via YouTube. When I look around my house for a CD or DVD, none of them is less than five years old. Many business models are falling apart because they were built to optimize transaction costs that no longer exist, and the value creation process is changing significantly.
Consumerization of Analytics
Our society is becoming more data savvy overall. Some would say this is generational, others would point to computing becoming ever more ubiquitous. We see the use of data popping up more often everywhere -- journalism, activism, social computing platforms, consumer apps, and "analytics-of-the-self" (weight, sleep, steps, diet, and blood pressure).
We as individuals increasingly believe we should be able to measure and analyze just about anything. Although these expectations are largely being set outside the workplace, the best organizations reinforce these expectations by driving an analytics culture internally.
Some of the best consumer applications today are basically BI apps applied to very specific use-cases. Kayak is one big slice-and-dice to find flights and hotels. RedFin is the same application for real estate with a geospatial visualization style. Yelp, Amazon, and OpenTable are all applications designed to help drive decisions through data. In our lives as consumers, we often have better applications for data-driven decisions than we do in our lives as business decision makers.
People talk about "convergence"-- the coming together of cloud, mobile, social media, and analytics and how computing has become a ubiquitous part of our lives. Cloud provides access to nearly unlimited computer capacity on demand. "Mobile" means I have one or more computers with me at all times that are more powerful than those that landed man on the moon. Analytics have always been with us but continue to become increasingly powerful as we measure everything and have the computer power to process it. Social media reflect the fact that we humans, as social creatures, have integrated this technology deeply into our lives: business, personal, the whole thing.
We're not there just yet, but the safe assumption now is that everyone, everywhere has computer power and is connected. That changes the addressable audience. Now everyone -- I mean everyone: employees, customers, vendors, governments, even goat herders -- are all potential audiences for information and analytics.
A New Shape of Knowledge
Not so long ago, knowledge was defined by credentialed experts, published in sacred books, and meted out in a top-down fashion -- Britannica, Webster, and institutionalized expertise. Wikipedia killed Britannica, and what it means to know something has forever changed. There are still credentialed experts, but there are also non-credentialed experts, armies of passionate fact-checkers, and curious, smart people who figure things out. When you put all these people together, you get a much better outcome than the top-down model.
The world is becoming "Too Big to Know," and this type of networked knowledge is required to wrestle down the interesting problems we face today. You have scientists no longer waiting to publish results, and instead publishing their measurements and data daily so others can collaborate to try to solve the same problems. Open source leverages a similar spirit, in which people assemble to solve problems, not because they work for the same institution but because they share interests and skills.
Even in business, we see techniques such as crowd-sourcing and putting up bounties for solving problems. Many of our digital business systems reflect an old, analog way of thinking. They don't recognize the need for highly social collaboration and knowledge networks that support knowledge emerging rather than fitting into predefined taxonomies.
How do these macro trends put BI at an inflection point? I'll answer that question in part 2 of this discussion.
Charles Caldwell is the director of solutions engineering and principal solutions architect for Logi Analytics and has a decade of experience in data warehousing and BI. He has built data warehouses and reporting systems for Fortune 500 organizations such as Unilever and American Express, as well as enterprises in the pharmaceutical, manufacturing, financial services, and public sectors. He completed his MBA at George Washington with a focus on the decision sciences and has spoken at industry conferences on topics including advanced analytics and agile BI. You can contact the author at [email protected].