Before Big Data’s rise in popularity, the grandfather of the lean startup, American statistician W. Edwards Deming, had this to say about its efficacy: “In God we Trust; all others bring data.”

This tongue-in-cheek reference to Deming’s own work for the American government’s census efforts, among other projects, highlights just how foundational and core data analysis is to decision-making. And, it also seems to prophesise the need for knowledge or data points from various different sources, wherever it can be harnessed.

For today’s organisations, data-based tools such as GTM tracking scripts, heat maps, prediction models, and even keyword trackers allow the use of data analysis for business purposes, both online and offline. However, this sort of data collection and analysis also heavily influences decision-making. Big Data, then, is data “multiplied” in terms of function and its continuous and consistent analysis allows us to learn more from it, thereby helping us realise its full potential.

It’s All about Learning and Consistency

Big Data is the term given to large data sets from sources all around us which can be analysed to reveal key insights about products, people, places, and even events. The volume of data, the speed at which it comes in, the variety of formats in which it is collected, and the sources it is collected from are all integral as these form the foundation for data analysis.

Data analysis involves different processes such as discovering patterns, making correlations, seeing market trends, and looking at customer preferences—all of which help businesses make more informed decisions. And, “to be data-driven” is the new buzz-phrase many businesses, from behemoths such as American Express and AiG to smaller freelance agencies like CopyHackers, use to describe their approach to decision-making and operations.

What is obscured in this glossy, top-shot view of Big Data, however, is the actual practice of collecting, analysing, and actually utilising and learning from data. Missing-in-action is the sense of its reality: that Big Data analysis is all about building a process around learning constantly and with consistency.

What makes Big Data so intriguing is the infusion of technologies that magnifies the velocity, volume, and variety of data that we’re receiving. In the hands of businesses, Big Data spells the ability to launch a product—even at a prototype stage—then rapidly iterate and responsively design its next version as a far more enhanced and much better successor.

In this case, data collected could be customer reactions, responses, feedback, and patterns in actual use, which would be gathered, monitored, and then analysed constantly by platforms dedicated to constant learning and analysis data in real-time. And it’s not just products. Decisions about internal processes, where and how to get more efficient, the re-allocation of resources, whom to form strategic partnerships with, or which projects, if taken, might give the best ROI—these are all part of Big Data’s scope.

Obviously, Big Data analysis allows businesses to offer solutions and gain insights for a variety of uses:

• To build a better, more viable or profitable product
• To learn from mistakes
• To better forecast the future
• To understand how the present circumstance came to be as a result of past actions

But there are two glaring caveats that determine the quality and usability of Big Data analysis:

• Constant analysis
• Effective analysis

Variation on these two factors is what separates the “good” businesses from the great businesses.

Learning from HiPPOs

In many offices lurks a “HiPPO” or what the Harvard Business Review calls the “Highest-Paid Person’s Opinion.”

One of the most significant benefits of Big Data is democratising and decentralising decisions within a business so that they’re not tied to only one position. Instead, they’re tied to real-time, measured actions, behavioural metrics, and clearly defined data points that can provide proof, justification, and direction.

Big Data doesn’t eliminate the need for humans at the core of decision-making (we’re the ones who make the final decision, after all). And this is true, even if the actual analysis can be handled or streamlined by intelligent platforms designed for deep learning. However, the process of data gathering, management, and analysis are better delegated to data platforms for faster, effective, accurate, and even more affordable performance.

When businesses decide to rely on Big Data, it calls for the constant analysis of data coming in with a corresponding set of decisions going out, based on the insights derived. The worst thing is to be presumptive, however, and make a decision with blinders on, and then suffer the consequences: lost profits, customer attrition, negative bottom lines, or wasted time, money, and resources.

The antidote to this is what researchers like Carol Dweck call having “the growth mindset” or a commitment to a learning culture. As HiPPOs are paid less for their opinion and used more for their ability to make decisions based on the data in front of them, there is a clear shift from simply knowing something to being able to back this knowledge up with data.

One of the biggest challenges—or conversely the greatest opportunity—that Big Data analysis adoption faces is moving from a culture that depends solely on heuristics for making decisions to one that’s focused on making decisions that are “much more objective and data-driven.”

Are You Part of the Big Data Movement?

In 2015, the percentage of companies adopting Big Data analysis was only around 17%. Less than two years later, in 2017, this stat shot up to 53%. According to the 2017 Big Data Analysis Market Study, “reporting, dashboards, advanced visualisation, end-user self-service and data warehousing are the top five technologies and initiatives strategic to business intelligence.”

In this case, being strategic when it comes to using data becomes an absolute necessity for developing a competitive edge. In fact, data analysis and the need for constancy in gaining insights, (along with the ability to learn to make better decisions) has built a stage that is absolutely filled with business success stories. And this is not confined to a single industry, nor is it unique to the private sector.

A couple of noteworthy examples:

• Dickeys Barbecue Pit: A restaurant chain focusing on barbecued food which utilised a Big Data analysis firm partner to increase sales and provide an in-depth understanding of their customers, citing a “reaction to the numbers every 12 to 24 hours” in order to “course-correct” rather than rely on “months-old data.”

• Beth Israel of Deconess Medical Center: The CIO of the medical centre credits Big Data with the survival of his wife who was suffering from breast cancer in 2011. Using Big Data, doctors and researchers were able to take her genetic data and analyse that against a set of 10,000 other comparable individuals with the same tumour, devising a better treatment plan. As of 2015, his wife is completely cured.

So, the question you’ll want to ask yourself is, are you part of the Big Data movement?

Harnessing and learning from Big Data bring us back to those two caveats: the constancy of analysing and the efficacy of the analysis. What’s evident from these success stories is that behind these decisions, insights and learning, there are intelligent platforms responsible for data analysis.

This is precisely where tools like Latize‘s Ulysses, an intelligent data management solution, enter the scene. Ulysses is able to harmonise disparate internal and external data sets and produce a web of data about things that are relevant to your goals. Through this, you’ll be able to extract the needed insights you can use to improve your business and learn from, helping you to develop a competitive advantage and prepare for the future more effectively. If you want to learn more about how your business can benefit from Big Data, please don’t hesitate to contact us today.