If you're like countless small businesses, your most pressing question about data is, "what do we do with it?" For many companies, that means job one is developing a viable data storage plan.
But taming the data monster is one thing. The real trick is figuring out how to turn that mountain of data into mountains of cash. Recently, TechRepublic writer Alison DeNisco Rayome, drawing from a new book about monetizing your data, shared five ways to do just that.
What's your problem? Ironically, the best way to set the stage for leveraging your data is to set it aside. Instead, think about any challenges facing your organization. If you could solve these problems, what kinds of opportunities might open up? This early strategic phase sets the stage for everything that follows - and will give you a sense of direction on the best ways to marshal data resources.
Create a roadmap. Put some meat on the bones of the problem you plan to tackle. This will help your staff bring a systematic approach to the process, testing assumptions and identifying action steps as they go. (Helpful hint: Don't forget to flush out any biases that may pre-determine an outcome! This happens when a team decides what the solution is before completing research - and goes looking for data that will "prove" their concept!)
Are you on the money? Now that you understand the problem and have explored potential solutions, the next step is to determine if the solution can really enhance your business. Put the proposed action steps under a microscope and see if they will help the bottom line. In other words, use analytics to test assumptions.
Seek value. The goal is to help managers tap data at a micro level - not just the usual generalized reports that don't help much. Data modeling enables drilling down in a way that can produce measurable results for your marketing, IT and business initiatives.
In data we trust. Value-driven agile analytics results in data that's capable of supporting a monetization strategy. Simply put, the goal is for data to be both useful - and trusted. Why? Two reasons: First, the data is deemed to be accurate. And second, your team is genuinely involved in the process.
To further explore the endless possibilities of intelligent data management, look here.
