But are users any closer to better decisions?
Today IDC released two reports pointing to SAP's Business Objects portfolio as the business intelligence (BI) leader by revenue and market-share with figures around $1.5B and 20% respectively. This gorilla continues bulking up through acquisitions such as the $5.8 billion deal for Sybase approved today by the EU.
More interesting is the fact that IDC confirmed the continued growth of the BI market in an otherwise lackluster IT spending environment. There's still quite an appetite for the clarity of analysis and reporting promised by BI software on top of the scarily complex SAP Business Suite.
Big systems, like SAP, are so complex that an entire industry of system integrators and trainers has appeared to help make sense of it all. Ironically, the same is true for BI software. The high skill levels required for wrestling, crunching, and analyzing data has created BI trainers and consultants who help your people understand their own BI software.
Therein lies the value of Klipfolio Dashboard to the enterprise. Most of your people aren't SAP gurus. Very few of your people are BI professionals. They're important but they could all sit around one boardroom table. What about the other 95% of your workforce that makes operational decisions all day? Even if you could afford to train them all, not everyone is able or willing to plumb the depths of SAP.
Instead, Klipfolio Dashboard presents a streamlined view of operational KPIs to 95% of your organization without any need for training. It acts as the operational bridge to get information out of the complex reporting environment of SAP using Open Hub and J2EE web services.
As the tag-line goes, SAP will run your business. And BI will let power users crunch it and query it once it’s in order. But when these systems and experts succeed in producing actionable KPIs, the way to get them out of the lab and into the hands of people who can use them is with Klipfolio Dashboard.
ROI calculations make for compelling sales tools, and an important way for the vendor to truly understand the value their product delivers. Here’s the catch though: these calculations are almost always focused only on cost-savings, as opposed to demonstrating revenue increase. In other words, the more challenging question is how do you accurately attribute revenue growth back to your dashboard or BI deployment?
Naturally, at Klipfolio, we are interested in understanding the two sides of an ROI scenario better. Here’s what we’ve done so far, and where we need your help.
It’s relatively straightforward to calculate the costs (investment) of your dashboard project, taking the licensing, support, and labor – and if necessary, hardware and vendor services such as integration and training – and adding these up for Year 1, and then through to Year N. OK, so far so good.
Now, we can tackle the less ambiguous of the benefit aspects: cost savings. For Klipfolio Dashboard, the greatest cost savings will be on labor. For example, a reduction in the time your users require on a daily basis to search for, consolidate, and digest critical data; or how much manual time and effort can be saved for your BI analysts whose job it is to produce reports. This calculation is a fairly simple [number of users benefiting] X [time savings] X [average salary equivalent].
There may be additional cost savings, such as bandwidth reduction (although even substantial bandwidth reduction has been found not to have much of a cost impact), and some one-time savings, such as being able to cancel current software.
OK, so there we have the easy stuff. You can now understand your investment and cost-savings on a Year 1 and Year N basis. Of course, as with any ROI calculation, you want to run it through a sensitivity analysis, weighting success, and partial success and failure, so you have a range of worst case to best case.
This is where we need your help. Here is a quote from Gerard Banaghan, Director at Premier Stationary, who summed our thoughts up quite well: "Klipfolio gives us a minimum of an hour a day saved, but the more important benefits are harder to measure. They're to do with quality and efficiency."
Measuring productivity or revenue gain is difficult. And indeed, you’ll be hard-pressed to find good examples of this type of measurement. Klipfolio Dashboard delivers increased data visibility; it improves information awareness. Customers enthusiastically tell us Klipfolio Dashboard is performing to or above their expectations. For us, this is the missing ROI piece. Have you calculated ROI using performance or revenue gain? If so, how have you done it?
We’re looking forward to hearing from you. Based on these discussions, we will work with Lyndsay Wise at Wise Analytics to expand our understanding, survey all of our customers regarding the ROI that Klipfolio Dashboard is delivering, and then share the results with you.
If you’re like me, and subscribe to a number of BI and Dashboard-related newsletters and industry reports, you've probably noticed the increased discussion around Master Data Management (MDM).
For those of you still wondering, MDM is the name given to the collection of processes required to create and maintain consistent and accurate data. It's a recent term, defined in Wikipedia for only about two years now. And it describes what is an altogether daunting task for most organizations.
So why is it important? And why the discussion now?
Certainly the importance of an MDM initiative is clear—it's a wish list that almost every business intelligence analyst and CIO shares: If only all of our data and fields and naming conventions were consistent.
Lack of consistency is an ongoing challenge in a world of ever-increasing reporting, compliance and data governance requirements. And I believe the sudden increase in discussion has its roots in both the increase in SaaS adoption and generally tighter integration between most enterprise systems. BI and dashboards used to be single-source, homogenous reporting platforms. But that is changing, as customers want to consolidate and understand their data from a holistic end-to-end view. With this trend in mind, you can see where MDM comes into play.
Because MDM is most effective when applied enterprise wide, it’s not an easy task. And it can be very time consuming. It’s also fraught with future risk: How can you accurately predict what your future data and reporting requirements will mean for your fields and data-naming conventions?
Here's another interesting point, and possibly cause for some of the recent interest. Generally speaking, Extract Transform and Load (ETL) is a process that acknowledges and deals with the fact that various data warehouses and enterprise applications have different naming conventions and definitions. MDM, on the other hand, deals with the issue of data trust head-on, ensuring data that conforms right from the start. In other words, could MDM eliminate much of what ETL is doing today?
At the end of the day, we all just want “one version of the truth”. And to me, that’s buzz worthy.
