Calculating Business Intelligence ROI: Four Examples
Research the topic of business intelligence ROI, and you’ll find a huge range of estimates. Some promise a whopping 1300-percent return (!), while others vaguely promise “soft benefits,” which means they have no idea.
We can only speak to the issue based on our experience as a small firm of business intelligence consultants. The cost side is fairly straightforward to calculate, and we’re working on a series of posts on how much everything we do costs. (If you’re interested, sign up for the newsletter (see the box to the right), and we’ll keep you posted.)
The question is: What do our clients get in exchange for their investment in us?
And we can say—with absolute certainty—that our clients never hire us for soft benefits. Every time, our clients have reporting issues that need to be solved and solved quickly. Now this doesn’t mean our work always leads to direct savings or increases in revenue. But when it doesn’t, there’s always still an absolute reason why the project has to be done.
That said, here are four examples of the problems we’ve solved. You’ll note, over and over again, these projects aren’t just about fixing reports. Rather, they’re about straightening out data.
As you go through the examples, keep in mind that while over the years we’ve used a variety of tools, every single problem we describe could have also been solved with Microsoft reporting tools (i.e. Excel, SQL Server, SSRS), which is our current focus.
Example 1: Messy Data Delays Reporting
Our client, a non-profit, needed to meet very strict reporting standards imposed by the state of New York to get reimbursement. Because the client’s software wasn’t set up properly, they spent literally months in Excel, missing deadlines and paying penalties.
Through a combination of better interfaces, proper setup, and well-designed reports, we reduced their reporting time from six months to six WEEKS.
In this case, you have direct cost savings—no more penalties and better collections. But beyond that, the reputational gain with their government partners was substantial. Not to mention, staff now had time to address other challenges.
Example 2: Software is Too Slow
In this example, the client’s data was fine. They could run basic reports. Debits equaled credits, etc. But, while they could run reports, they took forever. As in, wait an hour and the consolidation would eventually appear. So, during the last days of the close, our client had to wait an hour after every series of adjustments to see how the numbers changed. Not good when you’re trying to go public.
The business intelligence solution here is a data mart. By combining existing financial data (which is clean and organized) into a new database and creating SSRS reports, we cut the client’s cycle time by over 90 percent.
Did it result in a direct cost savings? Not obviously. No one was fired. On the other hand, our client couldn’t go public without this improvement. That’s a pretty hard benefit.
Example 3: Consolidation in Excel is Okay—Until It Isn’t
In this example, the client managed their financial close in Excel. Sure, most of the company was in their NY ledger. But they had acquired other companies abroad, and no one had brought together all the different systems. And it worked okay—until the auditor started asking questions. Things that made sense six month ago didn’t make any sense now.
Here, we did the hard work of bringing all the different ledgers together. When we were done, every transaction, from every company, eventually made its way into the same system. (For more on this, see our post Using the General Ledger as a Data Warehouse.) Once that hard work was done (and meshing five chart of accounts—one of which had Swedish account descriptions—certainly takes work) writing the financial statements was easy.
Again, is there an obvious savings? No. Is there a hard benefit? Yes—our client could pass the audit and know, with confidence, where things stood.
Example 4: Customer Profitability Varies Widely
For another client, a 3PL (third party logistics provider), the profit on every customer varied widely. Our client knew this because during contract negotiations they had to go through detailed exercises to figure out what to charge. Pulling these numbers together took days. Staff had to go through three systems and then conduct detailed discussions about what kind of overhead allocations to apply.
The exercise often resulted in unpleasant surprises. Again, the challenge was getting the data together. Our fix had several steps:
- Review all systems to insure costs are charged to customers as directly as possible.
- Integrate all systems into the ledger.
- Create Customer Level Profit and Loss statements.
This business intelligence example brings a real possibility of profits (if the client follows the data and goes after the profitable customers). The benefit is hard, but the value will depend on how well the client can negotiate profitable contracts based on what they find out.