Why Big Data is a Big Deal
How decision-makers can leverage their data to improve their business and their bottom line.
At a time when digitization and powerful new technology makes data gathering easier and more efficient than ever before, business leaders are increasingly looking for new ways to leverage that data. This helps businesses to derive insights that can improve decision-making processes and positively impact their bottom line. All too often, however, there is a significant disconnect between collecting data and creating actionable strategies for execution.
When used correctly, big data can help business leaders better understand their companies and their customers, and ultimately help them design and deploy more effective marketing and communications campaigns. This can not only influence customer awareness, but can also generate more leads and convert more sales. The best marketing/communications programs can also be monitored in real time, and strategies can be adjusted accordingly when the data signifies a consumer preference or trend, helping businesses to constantly refine and enhance their communication efforts.
To make that happen, however, decision-makers first need to understand the power and potential of “big data” (and why it has become such a popular and important tool for so many companies). They also need to appreciate the role of data analytics providers, and to have a working understanding of how to close and even bridge that important gap between data collection and strategy creation.
Big data is actually a relatively new term referring to the large volume of data that businesses gather every day on their people, parts, processes and customers. It encompasses unmanageably large data sets that can feel difficult—if not impossible—to make sense of. In the past, that was frequently the case, as companies used inadequate and outdated tools like simple spreadsheet programs to store, track, process and report information. With those limitations, meaningful analysis was a challenge, and connecting data to key performance indicators (KPIs) was almost impossible.
The right data management techniques will ensure that critical data is collected, cleaned, organized and optimized
Today, however, new tools and technologies have emerged to help data analytics take a sizable and, in many ways, transformative leap forward. A diverse range of businesses—from automotive and retail, to education and healthcare—have used new technology and techniques to wring fresh insights from vast amounts of complex data. The best data analytics partners have become adept at working with those businesses to help them identify and capitalize on the opportunities and hidden patterns buried inside their data, ultimately transforming information into intelligence and potential into profits.
For example, for a large automotive company that manufactures or handles millions of parts, going from old school spreadsheets to a centralized cloud-based database of information with a custom-designed dashboard is like going from a Go-Kart to a Mercedes. With new tools and techniques, sophisticated analytics is able to process that data in ways that make sense. It’s a kind of decoding: translating the language of information to read the story it tells. The best data analytics experts are able to find the meaning from even the vastest and seemingly most random data sets, revealing critical connections and subtle patterns—effectively finding order in the chaos.
The Data Disconnect
Once you have all that information, the next step is to leverage it not only internally, but externally, as well. Unfortunately, this is where things can fall apart. The disconnect between data collection and strategy creation is very real, and the ability to bridge that gap makes an extraordinary difference.
Whether you are looking to drive sales, resolve supply chain efficiencies or simply improve inventory management, processing data in a way that directly connects to KPIs facilitates more informed and strategic decision-making. But digging deeper and sifting through the data on a more granular level makes it possible to do things like identify inactive clients or prospective new clients. Increasingly sophisticated ways of processing the data can directly lead to a dramatically higher ability to convert inactive and prospective customers to active customers, for example. The result is both a healthier bottom line today, and a company that is better positioned for the future.
But to know where to go (and how to get there), you need to fully understand where you are, where you have been, and how you got here. That is where data analytics professionals come into play. The most experienced specialists in the field are true data engineers: they do not just understand the technology, they understand the data itself. And those are two very different things. It’s one thing to track KPIs, and it’s another thing entirely to interpret data and recommend specific solutions to help companies leverage that data.
Partners and Processes
For organizations looking to identify a data analytics partner, it makes sense to prioritize established professionals who have specific experience within your industry. If possible, prioritize analytics prospects that can provide technology and the analysis, as well as structuring their data collection and analytics based on an individual company’s needs and priorities. Plenty of companies can provide dashboards and analytics. Fewer can deliver customized solutions: client-specific systems and data models designed to not only track and process the data, but yield the most meaningful and personalized insights for that specific company. That begins with a partner who is willing to listen and respond to your needs, integrating your priorities and unique perspectives into their work.
Another point of emphasis is speed and efficiency. Data is most relevant and helpful when it is timely. If a prospective partner takes months to crunch the numbers and prepare recommendations that is a possible red flag.
The power of predictive analytics stems from its ability to use what has happened to shape what will happen, clean data and rigorous data gathering and reporting practices are an essential prerequisite for that. Businesses should work with their analytics provider to ensure data integrity, ideally utilizing both sound data gathering practices on the front end, and the analytics provider’s ability to scrub that data on the back end. The right data management techniques will ensure that critical data is collected, cleaned, organized and optimized.
Ultimately, bridging the gap between data collection and data utilization requires the ability to identify the contours of that all-important missing piece: the ‘why’. If a comprehensive review of data indicates that sales are going down, or enrollment is dropping, the critical step is to determine why those things are happening. Sometimes that answer—or a clue to that answer—is itself hidden in the data. But often data analysis must be placed in context and connected with new information in ways that forge new connections and yields new insights. Businesses that can do that effectively will not just be understanding their data, but also understanding their companies and their customers in new and important ways.
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