One thing we constantly learn in digital transformation is that nothing is as simple as it appears. Most of us learned the hard way that transforming our companies would take more than new technology. It would take new mindsets, new relationships, and new business models, as well. It should come as no surprise, then, that the issue of “analytics” is proving more complicated than most companies could have imagined. Namely: how to divvy up the data analytics budget in a way that is most beneficial to the customer and company alike.
First, I want to acknowledge that all companies will vary widely in terms of their IT budgets, as well as where they are in the digital transformation journey. Small and medium-sized businesses will be harder pressed to make hard decisions about how to break up the pie in terms of collection, analysis, people / staff, and programs simply because they have less to work with. That said, looking at your budget with a sense of purpose, rather than line items and buzz words, can help companies of any size or budget make better data analytics budget decisions. What works for your company will be unique to your own business goals. The following are a few options of how different IT departments are using their data analytics budget.
Data Analytics Budget: Time-Savings on Core Functions
Many companies, especially those in digital transformation infancy, may opt to focus the core of their budget on automation strategies and tech that could help save time on core business functions. This could be anything from digitization and managing financial records and timesheets to HR functions like hiring and employee engagement surveys. While this type of “analytics” is toward the low-end in terms of sophistication (think robotic process automation vs. machine learning) it can pay off huge in terms of creating time and headspace for employees to do more valuable work for their teams. If you’re in early digital transformation, this is a good place to start. What’s more, when you do it well, you will set yourself up for huge time and budget improvements down the line. (I’m sure some companies out there could give a testimony…)
Data Analytics Budget: Getting On Board with Business Goals
Similar to the above, an important area where it’s worth spending time and money in your data analytics budget is simply aligning and clarifying your business goals and how the data you are collecting will support those goals. There is truly no more wasted money than that spent to gather data that is never used or updated. Before throwing money to jump on the data train, use it to consult, organize, streamline, and communicate. Make sure everyone in the enterprise knows the business goals you are trying to accomplish with your data strategy. Skip this step, and your entire budget will be wasted.
Data Analytics Budget: Predictive Analytics
For some companies, such as tuition-based institutions, mortgage lenders, and other financial organizations, predictive analytics may prove to hold the most ROI for your budget. These companies may find that the most value comes in being able to determine who is most likely to enroll; to skip a payment on their mortgage; to benefit from certain financial training or services; or gain from certain course offerings or assistance. It can be used to score leads or indicate potential lifetime value. Most importantly: it can be used to replace data experts you may be paying to find these insights. If you have a small budget in terms of being able to hire and manage a full team of analysts, or even entice people to join your company, predictive might give you the biggest bang for your buck.
Of course, there are lots of other more complex use cases for analytics—personalization, cross-selling, omnichannel optimization. But if your company is still struggling to figure out exactly what to spend money on when it comes to your data analytics budget, I recommend starting with these foundational options. Chances are good that even companies far down the AI continuum have missed some or all of one of the steps above. The good thing is, with analytics, you can always re-write code, change course, and begin to do it the right way. You owe it to your customers—and your business—to understand your data analytics budget and how it can best work for all of you.