There’s no disputing that “big data” has become a buzzword in the tech world in recent years. From corporate strategy to climate change, big data has been touted as the ultimate solution to your company’s hardest problems. Unfortunately, success stories for big data remain somewhat elusive, and the dearth of examples makes it difficult to determine what works and what doesn’t.
In the past, big data faced some difficult hardware and storage-level problems. The advent of solid-state drives and in-memory computing solved many of these challenges, however, and NoSql databases—for “not only structured query language”—like Apache Hadoop filled the software-level gap. And now, storing huge amounts of data is no longer particularly difficult.
But storing large quantities of data is still expensive, so be sure you’re getting more value from your data than you’re putting in. Many companies are currently in limbo, paying to store data in the hope that it will provide value. Unfortunately, 43 percent of companies still gain little to no benefits from big data, and realize that value remains a future goal.
So we’ve created a guide to how to gain value from your “big data”:
Getting Value From Your Data
In order to gain valuable insights from your data, there are three fundamental building blocks that must be in place. Without these, any effort to work with big data will almost certainly fail:
Some aspects of big data infrastructure are fairly well-established, while others remain more difficult. Storing data is a first step, but you also need to consider maintenance and ongoing data streams.
Data tends to get stale pretty quickly, so it’s imperative that you have the infrastructure in place to continuously add new data. The ability to access this data quickly is also critical, especially as your data grows.
A good infrastructure allows you to store and maintain your data, but without the right tools to access it, your data is of very little utility. Big data tools are still rapidly evolving, so there is no cookie-cutter answer for this one.
However, the best way to find out what tools will work for you is to test them. Try a number of visualization and analytics tools, and find out which ones work best within your particular environment. Listen to your data scientists and engineers, and take action based on their feedback. The people closest to the data usually have the best sense of what works and what doesn’t.
As you look to get value from your data, having an iterative process is crucial. Data insights can be incredibly valuable, but they can’t be extracted overnight. Allow your data teams to focus on small wins, but make sure you’re guiding them toward the bigger picture.
Perfecting your process can take years, but you don’t need a perfectly refined process to get actionable results. Constant improvement is key. Give your data teams the freedom to innovate new ways to look at data, rather than repeating the same processes that may not work for your company.
But gaining insights from your data is just the first step. To get the most out of your data, you need to create a process for taking action based on those insights to derive true value from them.
Becoming A Data-Driven Company
Before you can take the necessary action based on your data, it’s important to start small and establish your models. Then, you can begin scaling. Scaling an inaccurate or unsustainable model prematurely costs valuable time and money that could be used developing further models or adapting better tools.
Remain flexible so you can take advantage of new tools and technology as it evolves. Big data tools and techniques change at breakneck speed, so it’s essential to keep on top of the latest developments.
Don’t be afraid to discard strategies or models that don’t align with your company’s long-term business strategy. The sunk cost fallacy often comes into play when deciding to discontinue a particular program, and this is especially true of big data systems.
By utilizing these strategies, your big data initiative will remain sustainable and become more efficient over time. Companies who successfully extract insights from their data have major competitive advantages that will serve them well in an increasingly global, corporate landscape.