Contrary to popular belief, the big data process isn’t a funnel. For whatever reason, we’ve come to think the more information we dump in from the top, the more actionable, quality insights will come out through the bottom. That just isn’t the case. We’re creating more data every day than any other point in history, but not all of this information is a golden opportunity. Most of it’s just noise. The real key to success is being able to navigate through the clutter and determine what pieces of information are pertinent.
The Role of Analytics and Visualization
Instead of a funnel, think of the data stream as an assembly line with different stations. Data flows in the following order: Collection, analysis, visualization, and strategy. We seem to have gotten the handle of data collection, but then try and immediately interpret this mess to build effective strategies. Instead, organizations need to focus their attention on the next two steps, analysis and visualization.
Big Data Analytics
Most businesses aren’t struggling with how to collect data, but what to do with it all. Data is useless unless you learn what to do with it. Fortunately, tech advancements have produced quality platforms capable of handling and managing big data analytics. Whether an organization is looking to increase sales and marketing results, find business opportunities, improve customer service or reduce risk, analytical software can help. Simply, it’s what takes raw, unstructured data, and turns it into actionable information. Companies need to invest in these tools because basic spreadsheets can’t handle the volume. When it comes to the competitive nature of business, time really is money. The first to market is often the first to succeed, so the ability to process information quickly and make fast decisions is vital. The real-time power of these platforms processes information quicker so you can make decisions faster.
Importance of Visualization
Data visualization, at its best, uncovers patterns invisible to the naked eye. Meaning, as great as the analytical abilities of your software might be, the results often make little sense to most of us unless we can really see what they mean. A simple example of this concept is weather forecasting. A meteorologist calculates changes in barometric pressure, humidity, moisture, but doesn’t give us those numbers. They wouldn’t mean anything to most of us. Instead, they provide us with nice visuals so we know whether to wear a t-shirt or cancel our outdoor plans. The same goes for businesses. Analysts don’t just hand numbers and figures over to decision makers. Managers, directors and VPs usually lack the technical skills to understand and decipher what the analytics mean. Instead, like meteorologists, analysts provide descriptive, but simple visuals that decision makers can use to build strategies.
The Role of Talent
At the end of the day, people make business decisions, and people interpret the data, meaning data platforms aren’t the final solution to data success. Think of today’s data platforms as the latest race car. It has the potential to outpace any vehicle on the track, but without a driver, it’ll just sit at the starting line. Even with an inexperienced driver, it’ll likely end up in flames.
Technology Needs Talent
Data analysts are responsible for collecting, sorting and studying different information sets. They’re the ones with the skills to know what tests and processes to run in order to get the right information. They understand when to run predictive analytics or an ad hoc analysis. They know what information is useful, and what’s just clutter. When a manager is looking for specific insights, like what people to target for a specific marketing campaign or if a product will be successful, the analysts will know what to look for to get those answers. The better the analyst, the better the result. A strong, experienced professional will better understand the big data tools and platforms at his or her disposable, allowing him or her to maximize their potential. In addition, analysts can use these tools to create better charts, graphs and models that visualize their findings.
Addressing the Talent Shortage
In closing, it’s important to mention there’s not an infinite supply of qualified professionals. Despite the recent actions of academic institutions to increase the number of data focused programs, there’s still a major shortage of talent available. Meaning, if you expect to compete, you’ll need to act now, or risk hiring unqualified talent. If you have a big data strategy, and have invested in the proper tools without investing in the talent to match, don’t expect success. Only will the right people help you get the results you need.