One of the really nice things about the new crop of cloudy accounting applications is that the vendors have (mostly) thought through what business people need in analytics. By that I mean they have taken the time to think about outputs as an integral part of the design. I particularly like the way (for instance) FreeAgent shows the small business person everything they need to know in a clear and obvious way. On their latest blog post, the company says it will be showcasing:
…some of our thinking on other ways we can expose meaningful insights on businesses for multiple audiences – including some awesome new ways of reporting business data.
How cool is that? The same cannot be said of the enterprise space.
A wee while back, Jim Holincheck and I were having a back and forth on the topic of business intelligence, the so-called ‘big data’ story and how we’re all going to need data scientists (however that’s defined) in order for the business to make sense of the oceans of data swilling around. Jim’s position is the data scientist argument is a ‘cop out:’
My belief is that business intelligence/analytic applications have not been easy enough or valuable enough to the layperson to gain wide adoption.
Jim used the analogy of Turbo Tax which
…significantly increased the number of use cases where you did not need a professional to help you prepare taxes. I do not think we have seen the equivalent of TurboTax for business intelligence/analytic applications – yet.
As a side note, I have long held the view that the SME cloud accounting players are doing exactly that. It is an argument I have used when talking to large vendors about where they need to take their cues. He observes that:
One of my greatest pet peeves as an analyst at Gartner was watching demos of reporting tools that showed how you could drill-down to find the data or exception you needed to know or act on. The demo person would effortlessly drill-down four or five levels and get to the result. Most business leaders are not going to take the time to do that kind of exploration (because they may not know where to look to find this nugget like the demo person does). However, those business leaders would be quite interested in the results of that exploration.
Jim also believes that when you can add intelligence into an application so that it delivers information contextually, then you’re onto something:
What if instead you had intelligence in the system that would interrogate all of the drill-down paths and report back interesting findings based on your role
I kind of agree/disagree with Jim on this. I”d like for instance to understand what Jim means by ‘interesting.’ Today, much reporting is based upon the idea of exceptions. Most of the work he describes can be done through the building of dashboards but then you need a small army of dashboard builders to get you to that point. Even then, I’d be surprised if such dashboards remain current because needs change. On the other hand, back in the day, Comshare as it then was, had the idea of being able to visually spin information in a virtual three dimensional world. It was insanely clever but, as with much that company tried to do, there wasn’t enough marketing muscle to make it stick.
I sense that the problem goes back even further. Large systems were never designed to get information out but to meet compliance needs and automate (as far as possible) data going in. Reporting, budgeting, forecasting and planning were all after thoughts. Hence the BI industry as it has evolved, backfilling something that in hindsight should always have been there.
The question is ‘where to next?’ The problems Jim sees are already in the wind.
Last year, I recorded a fascinating video with a technical lead from Camelot, operator of the UK’s National Lottery (see above.) At the time, they were experimenting with HANA, SAP’s high speed database. The problem they saw was that the speed at which they could return answers to queries against massive data volumes was likely to create a situation where they would be hard pressed to work out which questions need to take priority. That doesn’t require data scientists but a partnership between IT and the business to better understand how the need for certain answers best fits strategic objectives. Longer term, you can envisage a situation where questions evolve and become more nuanced.
I suspect that Jim has an answer up his sleeve somewhere. His employer, Workday, has been working on embedded analytics in its HR and financial solutions from the get go. The results I’ve seen so far are impressive. We shall have to wait and see how this story develops but it is a strong opening challenge. What do you think? Talk back in comments.