I recently wrote about why most companies today need systems of engagement for their data — custom analytics tools where real work gets done by specialists in a company. Done right, these analytics tools complement a company’s system of record, typically an ERP solution that is the sole source of truth about a company’s data.

The problem we see often from our clients is that business analysts get forced into using inflexible, one-size-fits-all systems that aren’t designed specifically for their jobs. Companies will invest big bucks in ERP systems — but neglect to spend on the tools that truly empower their people to be better and smarter at their jobs.

Since I wrote that post, a few of my data-loving peers asked if I had suggestions for how to justify the additional cost of a “system of engagement” to decision makers. In other words, how do you frame the business case for these purpose-built data tools so that management will spend money on them?

Sometimes, the best way to get management buy-in on any business case is to frame it with a compelling story. So, here are five different stories, or arguments, to help make the business case for custom analytics tools.

“So, what don’t you know about your business that a custom data tool may reveal? You don’t know what you don’t know. But there’s no ceiling on the value of insights.”

The power tool argument

A few months ago, I bought some shelving units for my house at IKEA. Anyone who shops at IKEA knows the deal: You unbox the bigger pieces, and then you unearth a bag or two filled with hex screws and other small parts for assembly. And typically, the bag contains a little allen key that you use to screw the furniture together. Between three shelving units, I probably had close to 100 screws to mount using the same allen key.

Realizing that hand-turning the screws would probably take me about two minutes each (more than three hours total), I opted for a hex bit and my power drill. At most, the power drill took me on average 15 seconds per screw. So I saved more than two hours of my time.

Now imagine you were a business analyst, using a one-size-fits-all toolset to gather, report and analyze data. You’re of course smart enough to make the most out of these tools. But you’re spending far more of your time crunching numbers and manually synthesizing reports. That’s time and mental energy that would be been better served analyzing the data, rather than simply finding the right data.

If only you had a power tool.

The financial ROI argument

There’s no more powerful way to make an argument in the C-suite than by showing a high financial return on investment.

If you distill the data development work we do at eAlchemy down to two categories of products and benefits, they would be:

• Automating reporting that saves time
• Delivering new insights to improve decision making

In both cases, it’s pretty straightforward to build a financial business case that shows the potential ROI up front.

For example, if you’re looking for budget to create a report automation tool, you could build some assumptions on your team’s time savings and resource savings. From that, you can model the financial impact. Then, when you ask for budget, and show that financial business case, you’ll be much more likely to get the budget you want. Especially if you also show precisely how you’ll test your assumptions and measure success over a defined timeframe.

When one of our client teams measured the success of one of our projects, they found a new planning tool saved the team 35% of resources needed in each planning cycle — and increased the speed of that cycle by 30%.

So, imagine you had a tool designed to help you make the hard decisions your company pays you to make, and that tool saved you time and delivered better insight. What if the additional time enabled better decisions that led to an increase in gross margin of 0.5%? What’s the value of that? It may not be that hard to figure it out.

The user interface and retention arguments

I wrote recently about how Excel gets a bad wrap among CIOs and heads of IT departments because of data control problems that occur when spreadsheets get shared, passed around, and misused.

One of the reasons this happens is because business analysts are forced into using tools not really designed to help them do their job. So, they create their own tools in Excel — and in the process, embarking on “shadow IT” projects that aren’t approved by the organizations. These shadow IT projects can cause a series of problems — from leading to inaccuracies in data to bugs that wind up consuming helpdesk resources.

But the fact is, business analysts love Excel — because they know it. And that familiarity makes Excel, by very definition, a great user interface for them. If you’re an Excel hater, don’t misunderstand. I know it’s not for everyone. But whether or not it’s created in Excel, a purpose-built tool needs to be designed for specific users, with an interface that is friendly and intuitive.

Interface matters because if your specific target users don’t understand and enjoy it, they won’t use it. And that’ll wreck any project’s ROI.

There’s another related argument to build custom tools with great interfaces: retention. It’s difficult and expensive to find good people. And if you don’t provide your top analysts the tools they want and need, they might find another employer who will.

The missed opportunity argument

You don’t know what you don’t know. That’s both the beauty and curse of working in data and analytics. In virtually every project we’ve ever worked on, there’s a series of unknowns and assumptions to be challenged.

The known factors for a data development project are usually heavily focused on negative attributes, including time estimates and cost estimates. Typically, the costs and time it will take to build a tool are fairly predictable. Rarely do our development projects come in dramatically over budget. On the plus side, it’s also pretty easy to estimate things like the time savings for automated reporting tools, as I suggested earlier.

But when it comes to estimating the value of the insight we will deliver, that’s a big unknown. Because if you invest in a data project designed to give people new insight, you may not know what you’ll learn. Most of our projects lead to some unexpected gain. For example, one recent project showed how our consumer goods client could improve their overall margins by minimizing production setup/changeover costs. It was an unexpected piece of insight that spiked the ROI, but we had no way to estimate that value before the project began.

So, what don’t you know about your business that a custom data tool may reveal? You don’t know what you don’t know. But there’s no ceiling on the value of insights.

The natural consequence argument

As parents, my wife and I often talk to our children about “natural consequences” — the idea that their behavior (good or bad) will result in certain outcomes. For example, if my daughter has a massive tantrum because she didn’t get the breakfast she wanted, it might make her late to her best friend’s birthday party.

In business, if a company doesn’t provide the purpose-built tools that its people want and need to make the decisions they were hired to make, there could be a variety of natural consequences:

• The employees may build their own tools — unsanctioned tools that may pose data quality or (worse) security issues.
• The company might miss out on new insight that may positively impact they bottom line.
• The company might lose good people because they find other employers who will provide those tools.
• The company would save the money and resources it didn’t spend on the project — but what opportunities did it miss?

On the flip side, if a company builds more purpose-built data tools, it may:

• Find new, unexpected insights that drive unexpectedly high ROI.
• Strengthen its data-driven culture by putting slices of its data into custom views that better serve the needs of specific roles.
• Keep employees happier and more productive.

So, whether your company does or doesn’t decide to build more purpose-built data tools, there will be natural consequences. But which ones?

Building the business case for your custom analytics tool

If you need help defining the business case for a custom analytics tool, we can help. We’re happy to help you define the solution, estimate costs and define the expected ROI — with no strings attached. Contact us to set up a time to talk.