Big Data, Small Value – that says it all about the minimal value enterprises are generating after investing millions into big data.
According to one recent study, more data was created over the last two years than in all human history leading up to that point. By as soon as 2023, the entire big data industry will be worth an incredible $77 billion. Even the big data analytics industry — one dedicated to helping businesses analyze and extract value from the information they’re generating and creating – is worth $122 billion.
Yet somehow, the companies that have ownership over all that data have little, if anything, to show for it.
Big data was supposed to mark the beginning of a revolution that comes along once in a generation. The working theory is that inside all that data are hidden patterns and trends and if you just had a way to uncover that information, you could have the actionable information you needed to make the best decisions possible at all times. You could learn where your company was headed and put a plan in place today to help get it there. It was the key to better serving your customers and creating a competitive advantage at the same time.
The Problem – Big Data, Small Value
The issue is that while all of this is true in a technical sense, about 63% of employees say that they can’t extract that insight from their data in their required timeframes. Nearly half of all employees say that their companies either don’t offer any sort of data training to help or haven’t made it clear if they do.
A lot of this has to do with the fact that most companies are only capable of analyzing about 12% of the data they already have, leaving all of that “insight” to remain hidden.
One of the major issues at the heart of this has to do with siloed data – or information that is spread across so many repositories and in so many formats that it isn’t suitable for analytics to begin with. Most companies still don’t have the resources necessary to deal with the sheer volume of data they have — to say nothing of how hard it is to work with low-quality data.
This is a common issue that businesses face that still rely on spreadsheets and other legacy systems that were never really designed to work together in the first place. If valuable sales data is “trapped” inside a spreadsheet, it may be helpful to whoever can access it, but it certainly isn’t doing someone in the marketing department any favors.
Equally complicating things is the fact that businesses are dealing with more unstructured or semi-structured data than ever. Structured data is that which is already in an organized and formatted way, making it easy to search for in databases. Unstructured data has no such format or organization, making it difficult to collect and analyze without further processing.
Another study estimates that this type of data makes up about 80% of all information being collected by enterprises. Much of this comes down to the increased use of not only mobile devices but also wearables and the sensors and other components that make up the IoT (Internet of Things). When you consider that the volume of devices connected to the Internet of Things is expected to grow exponentially over the next few years, it’s easy to see how this is a problem that will only get worse as time goes on.
There are other issues at play, too — like disconnected expectations between technical and business teams, and a widespread talent shortage for data science professionals. Companies also run into issues in terms of the deployment of big data projects, in as much as they’re not sure of how they will be used in production. They know what the result is capable of, but they’re not sure how that fits into the context of their larger business objectives.
While big data is important, a lot of organizations simply haven’t cultivated the data-driven culture they need. Even when there is access to massive volumes of data, executives don’t use it because they don’t understand its value. They’re still operating on the “old school” way of doing things (like by paying more attention to gut instinct than what their information is trying to tell them), much to the detriment of their long-term business strategy.
Another major problem is data hoarding — meaning that you spend so much time figuring out how to store and safeguard your data that you’re not devoting any attention to acting on the insight contained within it. Without the right business intelligence and analytical tools, data is little more than 1s and 0s sitting on a hard drive somewhere. The fact that this data exists in a literal sense matters less than your ability to use it to understand where your business is headed and how to get there.
But regardless, the result is clear. According to the experts at Gartner, about 85% of big data projects fail. A full 87% of these projects never even make it to the point of production at all. When you also consider the fact that only about 22% of analytics insights are expected to deliver genuine business outcomes through 2022, it’s easy to see why so many have lost faith in the “promise” of big data.
Avoiding the Common Issues of Big Data Projects
But just because most big data projects fail doesn’t mean that yours has to — provided that you keep a few essential things in mind.
One of the most important things you can do involves adopting end-to-end data science automation for all of your big data projects. Through technologies like artificial intelligence and machine learning, companies can improve their transparency efforts, deliver minimum value pipelines, constantly improve through iteration, and more.
But the real benefit is that it allows them to fail faster, which is often necessary for several reasons. With data science automation, teams can test out a hypothesis and go through the entire data workflow, not in the months this would normally take if carried out manually, but in days. This allows them to identify poor projects based on a failing hypothesis so that they can be eliminated just as quickly. Not only does this help save an organization money because they’re not spending so much effort on a project that ultimately won’t pan out, but it also increases productivity in that it lets them discover a valuable hypothesis in a much faster way, too.
This level of automation can help regarding the continuous improvement of big data projects, too. Not only can teams move faster, but they’re also able to make adjustments and rebuild models with all the latest data in a fraction of the time it used to take. This can help dramatically decrease the time needed to complete artificial intelligence and machine learning projects in particular, which allows them to start seeing value from these projects sooner than ever.
Beyond that, most big data projects fail because they leap head-first into the proceedings without the right processes (or any processes) in place. First, every initiative must begin with clear objectives that everyone can agree on. You shouldn’t be trying to analyze your business’ data just for the sake of it. You should be embracing big data and analytics to find solutions to problems that generate profitability and cement your competitive advantage. Without having clearly defined objectives, and without knowing what outcomes you’re trying to reach, you’ll have no way to properly track your project’s progress — and it will likely be over before it is started.
Likewise, you need to make sure that data is always available to those who need it. This is where the issue of data silos comes into play — insight-rich data that is locked in a silo where it can only be accessed by one department isn’t valuable to the entire enterprise. It’s common for businesses who still rely on legacy technology in particular to develop silos in sales, marketing, human resources, and other departments. For the best results, those silos need to be identified and eliminated moving forward.
Are you encountering the big data, small value challenge in your company? Please share.