When you think of big data, you may imagine the billions of rows and petabytes of data many companies are struggling to manage and process on a regular basis. You may also think about the challenges of handling diverse unstructured data such as audio, video, image and text-based files coming from an ever-increasing number of sources. In terms of the three V’s of big data, the volume and variety aspects of big data receive the lion’s share of attention. However, you should consider taking a closer look at the velocity dimension of big data – it may have a bigger impact on your business than you think.
In terms of velocity and big data, it’s easy to fixate on the increased speed in which data is pouring into most organizations today, especially from “firehose” data sources such as social media. However, velocity also underscores the need to process the data quickly and, most importantly, use it at a faster rate than ever before. These velocity-related challenges are typically viewed as technical ones, but there’s often more to it than just technology. People, process and cultural limitations can hold your company back from a speed and agility perspective – no matter how fast you collect and process data.
While the volume and variety aspects of big data receive most of the attention, the velocity dimension may be the most critical to your business success. (Photo: Shutterstock)
Data is said to age like wine – meaning the longer it’s kept, the more insights you’ll be able to glean from it. While this may be true for some forms of data, this analogy doesn’t apply to all situations. Many types of data have a limited shelf-life where their value can erode with time – in some cases, very quickly. For example, in retail it’s better to know which products are out-of-stock in terms of seconds or minutes rather than days or weeks. The more quickly a retailer can restock its products, the faster it can return to generating product sales.
Using real-time alerting, Walmart was able to identify a particular Halloween novelty cookie was popular in most of its stores – except two locations where it wasn’t selling at all. A quick investigation at those two locations revealed a simple stocking oversight meant the cookies weren’t yet on the store shelves. If Walmart discovered this stocking problem after Halloween, the value of this insight would have already vanished. Data velocity doesn’t just apply to the retail industry – it can apply to many diverse business models and functions.
High-velocity decision making
If your organization is still grappling with how to become more data-driven, the thought of operating at an even faster pace with data may be disconcerting and intimidating – especially when it comes to decision making. Traditionally, business decision makers have been accustomed to waiting days, weeks or even months to have ample information before they can make a high-quality decision based on past business performance. For fast-paced organizations like Amazon, the traditional approach to decision-making is far too slow.
In CEO Jeff Bezos’ recent letter to Amazon shareholders, he emphasized how “speed matters in business” and focused on the importance of “high-velocity decision making.” Bezos suggested, “Most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow.” Nobody wants to make bad decisions; however, constantly waiting for near-perfect information can lead to atrophy and missed opportunities. For Bezos, it is critical that companies are good at rapidly recognizing and correcting bad decisions. “If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.”
Fried pickles spur a quick, data-driven decision
Recently, Freddy’s Frozen Custard & Steakburgers found it needed to make a quick decision on a new limited-time offer – its tasty fried pickles. Throughout the year, the fast-growing restaurant chain features various limited-time offers such as special burgers, custards, or appetizers that run for 6–8 weeks. The company rolled out a new fried pickle offering, which had a decent response in its Kansas test market but was nothing out of the ordinary. Typically, a limited-time offer will spike in sales over the first two weeks and then drop off significantly as the novelty wears off.
Freddy’s Frozen Custard & Steakburgers is an American fast-casual restaurant chain with more than 260 locations nationwide. (Photo: Freddy’s)
However, Freddy’s was surprised to discover in its new data platform (Domo) that sales of the fried pickles were doubling across all of its locations after only the first couple of days. After double- and triple-checking the data, Freddy’s IT Manager Sean Thompson realized his company would be in a real pickle (sorry, I couldn’t resist) if it didn’t quickly embrace the new offering’s surging popularity. Freddy’s management team made the quick decision to turn fried pickles into a regular menu item, which meant securing a more robust supply of pickles from its distributors and featuring the product more prominently in Freddy’s social media campaigns. By the time positive feedback poured in from its restaurant owners about the fried pickles, the company was already ahead of potential supply problems that had been an issue in the past. More importantly, Freddy’s valued guests could enjoy the popular side dish without interruption.
Real-time data requires agile execution
Real-time data is only as helpful as your ability to execute on it quickly. While high-velocity decision making is important, fast execution is equally critical. Many organizations experience costly delays when the downstream processes and systems are slow and rigid. For example, a high-tech firm discovered it had a technical issue with its online checkout process where using a particular payment option caused a customer’s entire shopping cart to be emptied. Rather than quickly addressing this poor user experience, it took the company nine months to fix the issue due to bureaucratic IT processes and inflexible backend systems. If speed matters to your business, you’re going to need to iron out these kinds of issues that can limit your organization’s ability to respond to insights in a timely fashion. You must create an agile business environment where data insights can thrive – not stumble.
Today, executives place a heavy emphasis on having real-time key performance indicators (KPIs) at their fingertips. However, what good is minute-by-minute updates to these metrics, if your organization takes weeks to make decisions and then months to implement any needed changes? Instead, real-time KPIs must be combined with high-velocity decision making and agile execution. Following the example of companies like Amazon and Freddy’s, data-driven success will be increasingly defined by how organizations turn real-time data into real-time decisions and actions.