The BI industry is at a significant inflection point. The next set of significant shifts are starting to hit, and I wouldn’t be surprised if within the next 12 months we see a very different landscape in BI.
The signs are all out there 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 a ton of vendors in the space, with some long-time hold-overs that have found niches, and new, emerging players that are making up for the big players shrinking and driving continued industry growth.
The 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 the BI MQ into two recently and the assessment criteria and process changed significantly. And there’s a lot of talk about further consolidation of vendors in the industry.
So, is this just a typical industry consolidation cycle or is there something more going on here? 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 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.
As an example, agricultural companies like Monsanto have entered into the business of not just selling seeds, chemicals, and tools, but using data to be able 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 maximise yield.
And this plan is based on individual plots of land, and formulated from meteorological and other historical data about that individual plot. That 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 optimising 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 are less than five years old. Many business models are falling apart because they were built to optimise transaction costs that no longer exist, and the value creation process is changing significantly.
Consumerisation 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. But you 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, blood pressure, etc).
We as individuals increasingly believe we should be able to measure and analyze just about anything. And while these expectations are largely being set outside the workplace, the best organizations reinforce this 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 visualisation 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.
A lot of folks talk about 'convergence', the coming together of Cloud, Mobile, Social and Analytics, and the fact is that 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. And Social reflects the fact that we humans, as social creatures, have integrated this technology deeply into our lives: business, personal, the whole thing.
While we’re not there just yet, the safe assumption moving forward 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 institutionalised 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, and armies of passionate fact-checkers, and curious, smart people who figure things out. And when you put all these folks together, you get a much better outcome than the top-down model.
And frankly, 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 also 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.
And even in business, we see techniques like crowd-sourcing and putting up bounties for solving problems. But many of our digital business systems reflect an old, analog way of thinking. They don’t recognise the need for highly social collaboration and knowledge networks that support knowledge emerging, rather than fitting into predefined taxonomies.
Sourced from Charles Caldwell, senior director, Global Solutions Engineering, Logi Analytics