Business intelligence (BI) has matured a lot over the years. It’s become increasingly sophisticated and nuanced, reflecting the technology trends of the time, and the evolving needs of users.
The BI trajectory can be divided into various generations, according to how BI is generally perceived and valued, and what the prevailing market demands and trends are at a particular time.
BI 1.0 – Big BI
The first generation of BI — or BI 1.0 — was the time of legacy mega vendors like SAP, Oracle, and IBM.
BI 1.0 was all about being large: big vendor names, big solutions, big BI. Many smaller companies were consolidated under the umbrella of the giant mega vendors. Unfortunately, these large enterprise platforms were too cumbersome and unwieldy for the ordinary user to understand and, in any case, users didn’t have direct access to the data.
Instead, BI was centralized, and data access lay in the realm of the IT department, which had deep technical expertise. These legacy solutions offered neither the agility nor the ability for users to readily handle or analyze the data. Users couldn’t walk up to the table and quickly help themselves, so to speak.
Although the centralized solutions maintained and preserved one version of the data — as opposed to having disparate versions scattered throughout a company — they compromised on time-to-value and ease of use to the business.
In the perennial build vs. buy option, companies can decide whether to build a BI solution from scratch or buy one that’s ready-made. Mega vendor, pre-packaged solutions were costly, complex, rigid, and restrictive. And the proprietary architecture that vendors imposed brought a degree of risk and uncertainty to buying these pre-packaged solutions.
BI 2.0 – Big data and big insight
In BI 2.0, the second generation, big data was everywhere in the BI landscape. The trendy term meant different things to different people, though. A Techworld article What does Big Data mean? Term causes concern and confusion from 2012 has some interesting revelations:
“49% of organisations are somewhat or very concerned about managing Big Data, but 38% don't understand what Big Data is and a further 27% say they have a partial understanding. Additionally, the survey found that 59% of organisations lack the tools required to manage data from their IT systems, instead turning to separate and disparate systems or even spreadsheets.”
Along with big data came the tremendous hope of big insight, given the riches of information available.
While some deemed it to be mere hype, others considered big data as far from just being hype. (Of course, the current, potential uses of big data abound, particularly where it converges with today’s Internet of Things.)
In addition to the massive volume, variety, and velocity of data, the BI 2.0 generation also had many specialized platforms that didn’t necessarily fit into the model of a standard, end-to-end platform.
BI 3.0 – Big analysis
BI 2.0 gave way to BI 3.0, the age of the “desktop darlings.” Users could now download BI tools to their desktops (even without the IT department’s knowledge), and self-serve, for good or for bad.
On the plus side, these solutions gave users the agility they had wanted, and that wasn’t possible with the more complex, mega vendor solutions. Big analysis was now viable for the average, ordinary user. Tools were relatively easy, fast, and users could independently do what they wanted from the comfort of their desktop, without having to depend on the IT function for everything.
Alas, although these desktop tools offered personal productivity that users wanted, it came at the expense of scalability, data governance, security, and consistency. No longer was there one version of the data. Consequently, true collaboration was difficult, and any conclusions derived from the inconsistent data became questionable.
BI 4.0 – Big results
Pyramid Analytics closes the generation gap in BI 4.0.
It gives everything you need for trustworthy, mature BI in an enterprise platform — big scale, end to end, governed, easy to use, and easy to integrate data sources (big, small, on-premises, cloud, and mobile).
And because Pyramid Analytics combines the best components of generations 1.0, 2.0, and 3.0, you also get big results.
- Why Gartner Dropped Big Data Off the Hype Curve
- What does Big Data mean? Term causes concern and confusion
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