Analytics is the collection and interpretation of data patterns, which includes the use of computer programming, operations research and statistics. Organizations generally use analytics to quantify, predict and improve business performance, with specific areas of study that include the following:

• Fraud analytics
• Predictive analytics
• Retail analytics
• Web analytics

Analytics is typically used on large amounts of data, also known as big data, which can’t be managed with traditional methods such as a relational database management system (RDBMS). It therefore requires extensive computation and should use the most current methods available in computer science, mathematics and statistics.

This reliance on technological advances means that the use of analytics is changing rapidly, especially in the areas of deep learning and machine learning. The business value of analytics will greatly increase in the near future, accompanied by a corresponding increase in its adoption rate. The integration of analytics as a part of normal operations will therefore become essential for continued success in business.

Overview

A 2016 study by the McKinsey Global Institute shows that location-based services are currently making the greatest use of analytics in the United States. These organizations are capturing up to 60 percent of their total business data, especially geospatial data. This data helps businesses track assets, customers and teams across geographically separate locations, allowing them to gain new insights into their operations. The major barriers to the adoption of analytics in location-based services include the global market penetration of GPS-enabled smartphones. Retail businesses have the next highest rate of data collection at 40 percent, with major adoption barriers that include limited siloed data and analytical talent.

Methods

SQL will remain the standard method for analyzing data for the short term, according to a 2015 prediction by Ovum. However, tools such as Spark will be used as a complement to SQL with greater frequency. Additional analytical tools will also allow non-programmers to view business data with applications they build themselves. For example, both Microsoft and Salesforce announced such features for their analytical software in 2016.

Actionable Data

The value and speed of a data set will become more important than its sheer size. Businesses rarely use all of the data they collect, so bigger isn’t necessarily better when it comes to analytics. Making the best use of the available data by asking the right questions will allow businesses to obtain the greatest benefit from analytics, regardless of the data set.

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Privacy

Privacy concerns will present major challenges to analytics, especially in Europe. Recent legislation by the European Union will require businesses in those countries to significantly improve their controls and procedures that deal with privacy. A 2015 study by Gartner predicts that half of all ethics violations in business will be related to data.

Prescriptive Analytics

Prescriptive analytics involves the use of computational sciences and mathematics to recommend decisions based on the results provided by predictive analytics. It will become an integral capability of analytics software in the near future. A 2015 report by IDC predicts that half of all such software will perform predictive analytics by 2020.

Algorithms

The increased adoption of analytics will create a market for algorithms. A 2015 study by Forrester predicts that businesses will buy algorithms as a service and execute them against their data, rather than developing their own algorithms. Companies that already provide this service include Algorithmia, Kaggle and Data Xu.

Talent Shortage

The increased need for analytics is currently outpacing the availability of experts in this new field. A 2015 report by the International Institute for Analytics predicts that businesses will address this talent shortage through a combination of active recruiting for analytical experts and in-house training of current employees.

Data Volume

The volume of data available for analytics will continue to grow exponentially for the foreseeable future. The proliferation of smartphones and other devices with internet connectivity will be a major source of this data.

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Machine Learning

The ability of software to make predictions will become a key technology in analytics. A 2015 study by Ovum shows that this capability will be essential for businesses to progress in the areas of predictive analytics and data preparation.

Real-time Streaming

The 2015 Forrester report also predicts that real-time streaming will become a major data source for analytics. Software such as Kafka and Spark will allow users to make their own business decisions in real time.