Part 3: Now What?

This third and final installment of “Making Big Data Smart Data” provides practical guidance to senior-level banking leaders – the head of consumer credit card, for instance – who understand the need to design and execute a data management strategy. We’ll look at the process of how we help clients put their data to work . . . making sure “all the holes line up” as they assemble a solution that enables them to leverage data and gain a competitive edge.

What have we covered up to this point? For starters, vision is wonderful, but planning is critical. Both are informed by understanding the competitive landscape. And then there’s the matter of cost. Outcomes just might be the result of your investment of time and money. As the first blog post mentioned, implementing a data management system can be a costly endeavor; but we aim to minimize cost and maximize impact right from the start. Finally, being Agile and building in sprints doesn’t guarantee success. When you construct something incrementally, piece by piece without thoughtful planning, you just might wind up with a disjointed whole. 

The first post in the series “Making Big Data Smart Data” describes data management in today’s financial services environment and encourages organizations to look “out there” across the data management landscape. The second post explains the need to look “in here,” to take careful note of what a financial services organization can do internally to prepare itself to become a data-driven enterprise.

Now what? After looking out there and in here, it’s time to look up ahead. It’s time to implement.

The Review: It Starts with Parts

Any review of data management maturity should include the key aspects of the framework. That’s where our review begins. We start by defining why we’re having a conversation about the challenges a financial services organization is facing. And then we ask the all-important question: “What are you going to do when we give you the solution?” Organizations have critical business objectives that  require smart data management to achieve – and their investment in the space should be laser-focused on achieving them.

From there, we briefly get a lay of the land. That’s about a two- or three-week exercise. We always advise clients to be on guard for anyone who would tell them what they should be doing before they know the terrain. (We’re equally skeptical of an “assessment” that takes months to complete.) We use this time to ground ourselves in where we need to start; we assess gaps and work with our client to determine remediations.

Our approach involves using a template for each element as part of our review process. We might start with data architecture, move to data development, and so forth, describing what our evaluation revealed, how we assess our findings, and where we think we can add value. We look to prioritize our findings, taking each facet of data management into consideration. We make our recommendations on how to address the biggest gaps and develop a plan and strategy for doing so.

It’s important to note that implementing our plan adds value on day one, and we enhance that value as we make incremental improvements during the course of executing a data management strategy. We’re always moving the bank forward in terms of functionality. Once we present our plan and make recommendations, it’s not a matter of waiting months to see results. Resolving gaps in data security, for example, we’ll take our client from version 1 to version 1.1 in a few weeks . . . and then to version 1.2 a few weeks later. By identifying and remediating the biggest gaps in performance first, we can make substantial progress in a very short time, and clients can begin to reap the rewards immediately. Because of our staged plan, the organization is better off every single month, and we can roll with the punches as new priorities arise, knowing that we solved the biggest problems first.

Building the Engine

So now that we have a plan in place based on our review, we start with the foundation: the tech stack. All the systems and processes we turn on have to live somewhere. Is the next step in terms of data maturity to migrate to the cloud? Is it just to make better use of an existing cluster or a SAS grid or SQL warehouse? Creating a new storage layer could be as intensive as building the client a data lake in the cloud, or as easy as being clever in the way we help clients use their existing database to do what they need to do.

This stage of building the engine may involve addressing issues such as pipelining/ETL, data governance procedures, or control design and implementation:

As we noted during our initial review stage, we start by planning and making recommendations based on a cohesive strategy, so the solution is holistic.

Fuel Up and Fire Up

With the engine built, now it’s time to prioritize. What are the most critical functions that need to happen first? Here’s a look at several functions that take priority:

Hit the Open Road

With each step – reviewing needs, building the engine, fuel up/fire up – we work with our clients to ensure their success. This commitment is built into our service model, and it is the essence of the partnership we establish with them. We’re there to help if they need us. Ultimately, though, our goal is to build a system that they can operate without having to rely on us when they hit the open road.

Here are a few of the ways we do that:

Keep Your Eye on the Prize

As you go through the process of building a data management solution with your data engineering partner, remember to keep your eye on the prize. Why are you doing this? What are you going to do when you have a solution in place? What outcomes are you trying to achieve? Intentionality is critical. You don’t want to get sidetracked pushing papers around with consultants dealing with metadata documentation, armies of people with spreadsheets churning work while stuck in “Park.”

While you’re focused on developing an effective data management solution, look for opportunities to fix your foundation. If there are too many hops between the source and use of data, reduce them. If data quality controls are insufficient, bolster them. “Lift and shift” is too often code for “shelve it forever.”

There’s no question about it. Your bank’s data can help you enhance profitability, build new products, minimize exposure to risk, and deepen customer relationships. But it has to be expertly leveraged. That begins with clear vision and defined expectations and, of course, thoughtful planning. Yet as we’ve just described, your ultimate success depends on transforming vision and planning into action. For that, you’ll need the expertise of experienced data practitioners. When it comes to execution and implementation, they’re the ones who make sure all the holes line up.