“Big data” is a term that first appeared in the late 1990s and began to pick up steam in the early 2000s – long before growing numbers of mobile devices and Internet-connected objects caused the current spike in data volume. Of course, attempts to quantify the growth rate of data date back at least to the 1960s when it was called the “information explosion.” Those folks could not have imagined how big the explosion would be!
Today, big data includes the vast amount of structured and unstructured data that are constantly produced and stored in a business IT environment. While big data was originally thought to be a problem from a storage standpoint, the conversation has changed to focus on the potential value of big data. This value lies in the business insights that can be gained through historical and real-time analysis of big data.
Data analytics refers to the qualitative and quantitative techniques used to examine large data sets for the purpose of identifying patterns and drawing conclusions. These insights are then used to inform decision-making about business strategies, operations, product development and the customer experience. For example:
- Retailers analyze shopper behavior and purchasing patterns to improve in-store merchandising and promotions and maintain optimal inventory levels.
- Healthcare institutions analyze data from medical devices to monitor patient conditions and identify warning signs of health issues.
- Manufacturers use data analytics to schedule proactive equipment maintenance, optimize operations and reduce the risk of injury.
- Banks and financial services companies rely on data to prevent and detect fraud, assess risk, and improve contact center efficiency and outcomes.
- Utilities use data gathered from smart meters to measure energy consumption levels and better understand how energy is being used.
Large enterprises in these and other industries have been using data analytics for years to identify and exploit new revenue streams and improve operational efficiency. Like many IT innovations, big data analytics solutions are now making their way to small and midsize businesses (SMBs). Not only have prices for these solutions continued to drop, but more smaller companies are becoming familiar with the uses cases. Also, SMBs tend to be more agile than large enterprises, making it easier to quickly act upon big data insights instead of having initiatives stalled due to red tape.
The key for SMBs when implementing a data analytics solution is to start small. Identify a specific problem, a desired outcome, and a strategy for using data analytics to solve the problem and achieve the optimal outcome. Keep in mind that you don’t have to install a complex network of sensors to use data analytics. There is plenty of data to analyze from desktop and mobile devices, business applications, internal and external communications, social media and other data sources. Once big data analytics has been successfully applied to a single use case and you’ve become comfortable using the tools, it becomes easier to expand to new use cases.
Data analytics is no longer reserved for enterprises. With the right approach, SMBs can overcome the complexity of big data and turn it from a storage problem into a strategic advantage.
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