Over the last year, data analytics has evolved significantly, and CDW is seeing new trends that will shape how businesses use their data in the years ahead, says CDW’s Tom Leinberger. Looking at the next 18 to 24 months, Leinberger, a business development manager who builds solutions around high-performance computing and deep learning, offers three predictions:

• General-purpose quantum computing is still far off, but specialized applications of quantum subroutines will likely appear in the near future. This will unlock a new era of massively parallel computing and empower organizations to take advantage of vast stores of data

• Orchestration tools will continue to mature, enabling organizations to easily shift workloads between public clouds, private clouds and traditional on-premises data centers. The ability to rapidly shift workloads will improve the efficiency and effectiveness of analytic computing

• Natural language querying will become widespread, enabling non-technical users to rapidly gain intelligence from business data. Imagine a district manager for a large retail chain who oversees dozens of stores. If a trend breaks out on social media, such as interest in a particular pair of sneakers, the manager can quickly ask, “How many shoes are in stock in each store in my region?” and receive a response without waiting for someone to translate it into a SQL query 

CDW’s Joe Starofsky explores natural language processing (NLP) further. Starofsky, a senior field solution architect on CDW’s analytics team, provides a few examples of how business analytics (BA) tools use NLP to enable employees to work smarter:

• NLP frees people to use vague language in their queries. For example, suppose a user types, “Tell me about sales,” into a BA solution. The tool may respond with a variety of answers, such as the overall amount, trends by product or region, weather-related spikes and so on. The user can then click on each answer to learn more

• NLP can mitigate another common problem: Two people can look at the same results and draw markedly different conclusions. BA tools provide answers in everyday language, with recommendations such as, “Investigate the inventory peak that has occurred the third week of every month over the past two quarters.”
  

For more data-related insights, check out more blogs from Leinberger and Starofsky.