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How big data can help reduce profit bleeding

Many clients believe that spending millions of dollars on the latest apps, customer relationship management and enterprise resource planning software will help improve internal efficiencies and optimize operations. They view these systems and platforms as the golden ticket toward achieving maximum workforce productivity.

Implementing these increasingly complex systems and platforms, however, can silo critical data and stymie productivity. It can block companies from having complete visibility into all the data they produce, which results in incomplete analyses and poor strategic planning.

This is why it is critical for your clients to have visibility into all the data generated by their various systems and platforms. Not only because it allows their various departments — from finance and accounting to operations and IT — to operate more effectively, but it creates a centralized, company-wide resource for data utilization. Teams can then use this opportunity to create standards for data use and reinforce their role as defenders of the company’s bottom line.

Of course, it takes more than hiring additional staff to compile and centralize all of an organization’s data. Even though centralization is a key step towards better data analysis, using big data without context or focus is unproductive at best, and misleading at worst.

Instead, clients should be advised to have hypotheses or goals in place before analysis begins. These hypotheses serve as guide-rails for data use and allow for more advanced, sophisticated data analysis over time. Once companies adopt this strategic approach, they can create more efficient, high-performing teams that create smarter finance and market strategies and help maximize company profitability.

Big data trends chart

Creating a data mountain

Finance and IT departments are storing, tracking and analyzing more data than ever. Many finance and IT leaders believe the more data they track, the more insight they’ll have into how their business operates and where to make improvements. Systematic improvements, though, depend on how this data is put to use.

Companies often refer to the term “data lake” when discussing big data and where all of it is stored and analyzed. These lakes are filled with structured and unstructured data from across an organization, and companies will attempt to analyze and test data to identify operational inefficiencies or losses.

Instead of a lake, however, consider the idea of a data mountain. Rather than dragging an entire lake to identify improvements, analysts start at the bottom of the mountain and use some of the most readily available data to create well-informed business decisions. From there, users build upon that foundation to track and analyze increasingly complex sets of information and data.

From this perspective, analysts build upon their data to create more informed business decisions and strategies. Instead of starting with the most complex data sets at the top, which can create data paralysis, they identify which data sets provide actionable intelligence first. For example, retailers will oftentimes overcomplicate their data sets by analyzing at the product level, instead of first analyzing products by segment or supplier.

Making better use of data

Once this foundation is built, businesses can work to analyze their “long tail” of data that is much smaller and more difficult to analyze. This data tail grows exponentially over time as analysts and companies centralize increasingly disparate data sets and identify what information matters most to their businesses. At the end of the day, analyzing the long tail is what helps companies achieve that never-ending goal of having 100 percent of their customers drive 100 percent of their revenue.

This data can help companies better recognize where revenue is coming in, or out, and then make systematic changes that maximize revenue from all possible sources. Retailers, for example, can use data to identify which products are performing over others in specific stores and regions. Likewise, manufacturers can leverage such data to better understand their supply chains and pinpoint which products are profitable. Additionally, financial institutions can use data to minimize erroneous costs or purchasing decisions.

Creating the next generation of spend analytics

Even as businesses take advantage of improvements in big data analytics, new technology will allow them to further build upon their success and analyze broader data sets. Technologies such as artificial intelligence and machine learning can help business leaders analyze their data faster than ever, and perhaps, create systems that improve upon themselves over time.

At the end of the day, it depends on how businesses develop their hypotheses and use cases for big data. By taking a strategic approach, companies can focus on what matters most: high-value tasks that further optimize their businesses and help them achieve their strategic mission.

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