What’s Your Data Analytics Strategy?

It’s a question we hear often these days. Companies are leaning on technology’s ability to pull in customer data at every point, and that massive amount of data necessitates a strategy for processing it. The way businesses use customer data to create personalized experiences will determine their ability to compete in today’s market, but without a clear plan, they will flounder.

Yet 62% of companies don’t have a strategy for how they’ll be analyzing all that data. It seems that we’re still struggling with the best way to understand and act on the data we have in our systems.

So how do you create a data analytics strategy that actually works? And once you have one in place, how can you be sure that the insights you see are accurate enough to base decisions on?

A quick Google search for “tips on creating a data analytics strategy” will give you solid advice on data sources, data modeling, data cleaning and data warehouses. But most of these articles overlook a core factor: whether your systems are giving you good data.

Often, analytics strategies assume that your systems are fine, and you just need to find the right technology to help you manage and use the data you’re gathering. This ignores the reality that legacy processes built on unconnected systems and data silos leave you open to holes in the data, replicated data points or other kinds of inaccuracies.

The question you need to look at first is, How do I get better data? What most companies miss is assessing their systems through the lens of data quality. Data is key to acting on business opportunities and making good decisions. Improving your systems will give you better data to base these decisions on.

Three Steps for Improving Systems

How you improve systems depends, of course, on the unique situation of your business. However, there are still general guidelines that everyone should keep in mind when reviewing systems in relation to data acquisition and management. These three steps should help you get started with the process of improving your systems for more useful data.

Step #1: Identify What Questions You Need to Answer

Most businesses have long lists of broad, general questions that they’re hoping data analytics can answer. By asking different business units to commit to a list of specific, targeted questions they want to focus on, you can determine the data you need to accurately answer them.

If you do it the other way — gather as much data as you can, then figure out what questions it can answer — you might waste time gathering data you didn’t need. Worse, you could overlook part of the data, giving you inaccurate answers later on. Which brings us to the next point.

Step #2: Audit Your Business Processes

Process by process, sit down with each department and look at how employees get things done. When you actually watch them go through the process, you’ll notice gaps where data isn’t being transferred to systems. For instance, the sales team might keep notes from customer visits or conversations in their own desktop files. That’s valuable data that the system doesn’t have access to.

US Bank recently went through this process for the design of their securities services portal. It turned out the core system deal data, like cash, holdings and trade information, was being sent via email and thus not captured in any of the systems they drew data from. Exposing this allowed them to figure out a way to bring this information into their portal, as well as connect it to the rest of their business systems.

Not every piece of data you find will be useful, and that’s fine. You don’t have to bring it all into a data warehouse. The purpose of a thorough audit is to ensure you know what’s available, and assess its value from there.

Step #3: Find Solutions for the Gaps

After you finish your audit, you should compare the data you do have against what you need in order to answer the questions from the first step. This will likely leave you with some gaps that need to be filled in.

For example, if one of your marketers needs to know which whitepapers are linked to closed sales opportunities, you need a tracking system in place to capture that data. You might need integration with blogs, with gated assets, with user profiles and with your CRM. At this point, it becomes a question of technology.

Work with other departments to plug in the necessary digital tools in order to get the missing data. This may mean replacing a system altogether, such as moving to a new blogging platform. Business intelligence experts can ease the process by identifying their technology requirements and making sure those are included in future purchase decisions.

Conclusion

Reviewing business systems with an eye for better data inspires long-term projects, not quick fixes. By focusing on how your data analytics strategy relates to the organization as a whole, you can bring in the right technology to integrate and connect key tools that provide long-term value. Data analytics doesn’t exist in a silo; it involves buy-in from the entire organization, whether that’s standardizing certain terms across departments or convincing your sales team to fill out the CRM the way you need them to. Taking the time to identify the right systems and strategy early on can only help.

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