In recent years, “big data” has become one of the most commonly used buzz terms of the technology world. As the amount of information online explodes, startups and small businesses are on a hunt to develop more efficient and streamlined ways to process these massive data sets and arrive at actionable insights.

Today, the focus on big data has shifted from informational capacity and workload to the systems used to analyze and understand them. The field has grown to encompass its own server architecture solutions, programming languages and visualization methodologies. However, big data only looks at one part of the equation when it comes to parsing information.

While big data’s ability to make sense of large information sets is incredibly valuable, the field tends to produce more questions than answers. But today’s business leaders need their data to provide actionable insights, not questions. This has led to the expansion of business intelligence suites. Although some still see these two disciplines as competing ways to analyze massive sets, the reality is both are two sides of the same coin.

Expanding the Data Set

Big data remains a question mark with a murky definition. There’s disagreement over whether the term refers to massive data sets or to the technology used to process them. Still, everyone agrees that we need to find better ways to quickly aggregate, interpret, and respond to the information being collected.

E-commerce sites must find trends in consumers’ purchasing habits and preferences. Social media platforms must parse through millions of interactions to find trending topics and publications. Security companies must analyze millions of data points to find potential threats, breaches and vulnerabilities. It all has to happen on the fly, with minimized lag times.

“As data and analytics become more widely adopted than ever before, the potential for business growth is truly exponential rather than just cumulative,” Gartner PR manager Rob van der Meulen wrote in a blog post this past January. “Those who fail to act today will suffer not just in 2017, but also hugely limit their potential for growth in 2018 and beyond, as the returns from increased insight, responsiveness and efficiency snowball.”

Business intelligence is based on a slightly different concept. Instead of beginning from scratch, as big data collection does, business intelligence systems examine massive data sets as part of the search for answers to specific questions. This may seem like a matter of semantics, but it can make a major difference in how each of these methodologies approaches analysis and research.

Using the Right Analytics Tools

The risk of misusing big data analytics is asking the wrong questions and finding the wrong answers. To truly benefit and experience the value of these technologies, it is essential for companies to work with the correct data and to build out dashboards with the most relevant sources and queries for their operational and strategic needs. Many BI platforms can be scaled to consumers’ specific requirements, regardless of the amount of data they handle.

“For a good visualization, you may need to work with the underlying data that is being analyzed,” noted Sisense’s Gur Tirosh in a recent blog post about democratizing analytics dashboards. “If you are working with one or two simple spreadsheets, you may have everything you need in your spreadsheet application.”

Nowadays, businesspeople often need to use data that arrives in a variety of formats, from a variety of sources, so it needs to be prepared and mapped out using advanced tools. It’s only at that point that “meaningful analysis and visualization can take place,” Turosh notes.

Instead of rushing to implement big data languages such as Hadoop and Scala, which are best used for massive sample sizes, companies must ask themselves if they’re necessary. A small retailer with 100 customers may have all the computing power it needs with Excel. A cafe doesn’t require a database capable of parsing through a million data points per second, but a massive chain of restaurants across the U.S. could benefit from it.

With business intelligence, companies can take the results of big data analysis and convert them into actionable recommendations. Whereas big data is focused on untangling large sets and finding patterns and trends, business intelligence is concerned with providing value vis-à-vis reporting. More so, business intelligence systems are based on the idea of streamlining the discovery and research processes.

Small businesses can benefit from powerful business intelligence suites, too. You can use them to find leaks in your customer acquisition funnels, identify supply chain optimization opportunities, and learn about your clientele’s preferences.

Big data is more than just a tangled web of information to be plowed through. By applying smart, efficient analysis to large data sets, companies can uncover new strategic directions and provide better services.

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