I’ve been working with Business Intelligence (BI) tools for a number of years now, and my collection of vendor water-bottles is slowly building up. BI vendors are good at keeping their analysts hydrated, but where is this industry actually headed?
When we look back over 2019, we see some major acquisitions in the Business Intelligence (BI) vendor tool space, which suggest a certain coming-of-age, re-shaping or maturity for this industry. For example, Tableau was acquired by Salesforce for $15.3bn (June, 2019), Looker was bought by Google for a reported $2.6bn (also June, 2019), Qlik snapped up Attunity for $560 M (Feb, 2019) and Logi Analytics made two purchases of Jinofonet (Feb, 2019) & Zoomdata (June, 2019). The list goes on... In short there has been some significant merging and conglomeration going on in the BI tools space in 2019.
But as history teaches us - when markets mature, it is usually a prime time for something else to come along and disrupt everything. For example the MP3 player market was reaching peak maturity just before the iPhone came along and changed everything, for good. As email did to the fax machines back in the 1990s or as Henry Ford did to the horse and cart back in the early 1900s, and so on...
Only a year or so ago, I was working with BI tools, using typical company datasets, containing data that was several days, if not weeks out of date. This is not an uncommon situation for an analyst, but whilst I laboured through this report, I looked down to the watch on my wrist which was collecting various health signals about my time spent moving, heart-rate and so on... rich datasets... and all in real-time.
This got me thinking there is an obvious, gaping, huge disconnect between how typical business data is used in the real world and how it could be used. Surely, if a ‘business insights report’ is going to be valuable, doesn’t it need to be based on live data, so we can act now?
Adding to this, the fact that business transformation requirements and their associated appetite to move data to the cloud, along with similar demands in the consumer space, means we can see an entirely new IT landscape beginning to form. Storage has become cheaper, faster, larger and more-secure. In fact, it would be fair to say that never before has IT infrastructure been so scalable and available to all.
Compound this with the plethora of applications we all now use to run our daily lives, work, play, health, shopping and so on... then it's not difficult to see the trajectory of explosive growth world data sizes are now on.
For these reasons I believe the future of Business Intelligence isn't another Business Intelligence tool. The future of Business Intelligence sits within an Operational Intelligence Platform. And this belief has guided our architecture for our new product... Stratiam.
But let me first pedal back a little, and explain the difference between Business Intelligence (BI) and Operational Intelligence (OI).
Business Intelligence (BI) is a bit like looking in the rear-view mirror. It tells you what has happened, in detail, on historic data. Data analysts or data scientists will typically work with BI software tools to find insights from their historic data and they will use these insights to suggest how things could have been done better in retrospect, so that the organisation may learn from this and adapt future planning sessions accordingly.
However, for a long time data analysts/data scientists and other users of BI tools, have complained that a large portion of their role is involved with gaining basic data-access, finding the data dumps, extraction files, CSVs, obtaining permissions and passwords etc. etc. Often they’ll also have to get around their respective internal gatekeepers in order to do so. So there can also be time-delays if key people aren’t in the office. Even when they do have access, the typical analyst will have to process data-transformation work on the data to get it into a consistent format. Dates, currencies, and many other fields need to be converted into a common form before meaningful work can be undertaken on the data. In fact many data analysts will argue that it is not uncommon that the data collection aspect to their role taking up to 90% of their time.
Furthermore, due to this process being manual it becomes unrepeatable. This can mean you get examples where two or more analysts, working on the same datasets from the same organisation, are producing different reports. This may happen because there is no common or single source of truth on any blended data sets, which may cause data-paralysis. Even if there were consistency to their reports, one big issue with business intelligence is that the insights go quickly out of date. So the analyst can be spending their time reporting last month data, or last quarter data or even worse last year's data. Which causes data-staleness. Paralysis and staleness don’t sound good? No, they aren’t.
Business intelligence tools typically have been desktop-based with a large upfront cost. Use in-memory for any processing and live on the desktop or internal folder system. This approach has collaboration limitations and it certainly doesn’t offer any source or version-control. Furthermore, if the laptop containing the business intelligence software and its associated reports happens to be left on a train, it is either gone forever or is left open for unwanted eyes to explore.
By comparison, Operational Intelligence (OI) takes live-stream, real-time data and will surface signals from the noise, to tell you what is happening right now. Or will predict future events based on known signals. So if BI is your rear view mirror, then OI is more like your front windscreen or binoculars.
Operational Intelligence reports are set up by data-experts for their end-users but thereafter can be explored and configured by almost anyone in the organisation. This is a key difference, because unlike BI which requires highly skilled data engineers, data analysts and data scientists. Operational Intelligence allows anyone in the organisation with access permissions to explore the data sets. This is an entirely new way to democratise data.
Operational Intelligence (OI) leverages the best of the modern web, and so is browser-based, responsive, works on mobile devices and tablets and relies on lightweight front end browser-based technology to run. Operational Intelligence doesn’t require you to install large chunky desktop software applications. Operational Intelligence can update to the latest version in the background without the end-user knowing they’ve had a version update, and can be highly secure with various multi-factor authentication steps in place. When you work with Business Intelligence you’re likely to be using BI ‘tools’. But Operational Intelligence’s home is on a ‘platform’.
For all these reasons, the Operational Intelligence Platform lends itself extremely well to automation, machine learning and predictive analytics. The nature of the Operational Intelligence Platform also makes it very suitable for a monthly subscription-based service, rather than the high set up cost you’ll be hit with, when using business intelligence tools. This means there is a lowered barrier to entry for organisations wanting to get themselves set up on Operational Intelligence Platforms.
As a more digestible summary, I’ve displayed the key differences between Business Intelligence Tools and Operational Intelligence Platforms into a simple table view.
To experience the joys of an Operational Intelligence Platform, then why not test drive Stratiam on your data? To do this simply request at trial using the form below - and we will be back in touch to set up a demonstration.
