Industry Size and Growth Stats
When you shop online everything from your customer journey to your buying decisions will have been captured and added to a larger data set filled with other customers data. Data engineers will then use data analytics platforms to try and extract insights. For many enterprises, these datasets are the key to creating a more efficient service that delivers a more targeted customer experience. This is just one example of how data analytics is shaping the new business frontier.

IDC forecasts revenues for big data and business analytics (BDA) solutions will reach $189.1 billion this year with double-digit annual growth through 2022. Digital transformation continues to be the key driver of BDA spending as companies work to meet the demands for better, faster and more comprehensive data access and related analytic insights.

Let’s take a look at some recent trends driving this industry growth.

IoT + Data Analytics
IoT is short for Internet of Things. The Internet of Things refers to the ever-growing network of physical objects that feature an IP address for internet connectivity, and the communication that occurs between these objects and other Internet-enabled devices and systems. Gartner anticipates that there will be 20.4 billion IoT devices by 2020 and everyone one of those devices will be generating data that an organization can monitor with data analytics platforms that can help data scientists realize the potential of this data.

Growth of Augmented Analytics
Predictive analytics is the practice of using data mining, predictive modelling and machine learning to identify patterns and attempt to predict a future outcome. Amazon uses predictive analytics to recommend products based upon your search and buying patterns. While predictive analytics uses machine learning to predict what will happen, augmented analytics works to boost human intelligence and provide insights into the why, so we can work to better understand even broader data sets. Gartner estimates that over 40% of all data science tasks today will be entirely automated by 2020.

Data Quality Management (DQM)
Many organizations today are having to reengineer their data practices in order to reap the benefits of today’s data analytics capabilities. In fact, Gartner estimates the average financial impact of poor data quality on businesses at $15 million per year. Data quality management (DQM) refers to a business principle that requires a combination of the right people, processes and technologies all with the common goal of improving the measures of data quality that matter most to an enterprise organization.