Focus on the business need, not the technology. As many partners can attest, oftentimes clients don’t know what they want. They don't have a solid grasp on the end game. Customers hear the latest sexy tech jargon like analytics or base data and know they can probably drive some value from them, but they don’t know how to go about doing that. At that point, it becomes a technology project, not a business project, says Recor.

“We [at Grant Thorton] tell clients to start at the end first. In other words, what is it that you would want to see from a report standpoint, or a dashboard standpoint? What information would help you make better decisions and help you do your job better? Start there.” Once partners understand what the customer really needs, they can back into the architecture.

“I've seen a lot of big data projects fail because they try and build the best data lake possible, and they get lost in the technology aspect of it, rather than trying to make sure that all of the consumers that are going to be using it end up getting what they want.”
 

Don’t try to eat the elephant in one bite. Pulling off a successful big project requires total customer confidence. Recor says it’s critical to show momentum through a series of “quick wins.” A decade ago, the IT team would work for months behind the scenes, shrouded in mystery, and then unveil a whole new system. But when you’re running a channel services firm, you can’t afford to not show progress. Recor says the “quick win” strategy provides two major benefits. First, it shows that what the organization wants to do is viable. Second, it gives people the ability to actually get something from the effort, and can create momentum internally for other people to get on board.

That component of getting others on board is vital to the success of a project, says Recor, showing his anthropological roots. “Because again, the technology component exists, but then there's also the culture component of it. And like any type of solution that a partner is trying to drive for a client, there usually is a specific problem that's being addressed to get you in the door.” You won’t always learn that problem right up front. Sometimes it takes being accepted into the culture in order to really see what’s wrong in an operation.
 

Engage with the end users early and often. Recor says he recently met with a very large financial firm with a massive data lake, but there was something missing. “The firm had all the data it could possibly gather, but it was missing business intelligence from the business units—from the customers themselves,” says Recor. The firm just didn’t know what it needed.

Just like in typical software development, it’s important to engage end users throughout the entirety of the project. That way, you know what you’re building will offer value to the people who really need it. “You've got them adding value in the process rather than just trying to build a better mousetrap and presenting it that way.”
 

Build a flexible architecture. At the end of the day, if the solution you build for a client isn’t flexible enough to adjust to changing business needs and organizational goals, it’s going to be a flop. The bigger purpose is trying to devise a solution that can figure out what information adds value to the stakeholders and whatever point in time they need to get to it, no matter when that is. “It moves and it changes. And so, if you can build a framework or a model that allows people to have the flexibility they need to get the insights they need, then you've got something that I think everybody would want.”
 

Hire people who understand business. In a services-centric channel ecosystem, partners have to be knowledgeable enough to provide the right insights to clients at the right time. It isn’t enough to have the best technology. Service providers have to know how to wield a data analytics tool to provide value to their customers. Otherwise, it won’t scale. And if it doesn’t scale, it doesn’t make money.

“That's kind of the cycle that we've been in in the services profession for a long time. When you're talking about this big data problem, it becomes an integration problem and a culture problem very quickly. Meaning, you have to have people that understand what the business does, that the clients that you're serving does, and what they need from data,” says Recor. “So the might tell you that I need x, but really in the eventuality of it is that they need y plus z.”