The relationship between big data and business is well past its honeymoon phase. Big data is no longer a new, innovative gimmick struggling to find its place in the modern commercial arena. Now, it’s more or less a given for companies to know how they can use data-driven analytics in the most profitable way possible. If you know that you could be getting more from your data resources, here are some tips for maximizing the value of big data.
1.Start With a Problem in Mind
Exploring huge amounts of data through sophisticated, expensive tools certainly has a lot of potential, but those tools will amount to nothing if you’re not using them for something that will solve tangible business problems. Spend some time identifying projects that are both practical and promising, and understand the various issues that big data analytics can solve for an organization.
The most important source of data for a lot of modern businesses is their customer transactions, which tend to yield fairly structured data. Loyalty program transactions, for example, produce huge and timely streams of data. They’re also filled with the smaller details of what, when, who, how much, and other details of individual spending.
Bottom line, you need to establish the kind of business challenges you can overcome with the data that’s made available to you.
2. Think Ahead and Tie Insights into Business Functions
To achieve any real value, you’re going to have to tie the results of your analysis into actual business functions. As obvious as this sounds, far too many businesses let their planned projects gather dust because they fail to come up with findings that provide tangible value. Teams that are in charge of embedded analytics need to spend some time thinking carefully about how their models are going to be published and used by marketing, customer service, operations, product development, etc.
Any models that rely on intensive data processing can cause serious problems when you come to implementing your big data model. Advances in tech are gradually helping businesses to avoid these and similar issues, and speed up analytical processes. However, by thinking ahead, and deciding on how you’ll implement big data on a practical level, you’ll ensure a much smoother ride.
3. Leverage Cloud and Productivity Platforms
These days, using big data analytics no longer requires pumping massive amounts of money into expensive infrastructure and niche skills. If you leverage the right cloud services, you can pass all the underlying services and systems onto a dedicated third party, paying only for the capacity and services that you need. You can also use open, hub-based architecture to achieve a faster, cheaper method of improving cross-functional coordination and visibility compared to the usual one-to-one model for system integration.
Unless your analytics capabilities are only going to be used with software, you’ll also need tools for packaging analytic services for the end user. Modern high-productivity platforms will give you everything you need to engineer complete apps, including user forms and workflows powered by your data.