10 Predictions for the Data Analytics Market for 2017

The data preparation and analytics space has seen tremendous growth in 2016, including the rise of self-service tools. What’s in store for the space next year?

Self-service analytics has unintentionally caused the data landscape in many companies to become like the Wild West. Data is now distributed all over the organization, and often it’s managed in isolation. Data and analytics outcomes aren’t being shared and reused; rather, users are starting every analytics project from scratch without the benefit of repeatable data modeling. Additionally, IT struggles with governing and securing this information due to the distributed architecture.

This transformative new capability will integrate traditional self-service data preparation benefits with key attributes common to social media platforms to enable data scientists, business analysts and even novice business users across a company to search for, share and reuse prepared, managed data for better business decision-making. And firms will have peace of mind regarding data governance and compliance through use of centralized, sanctioned data sources.

Because data is distributed all over the organization and users often work in seclusion, information has become uncontrollable and unpredictable. Poor information governance increases security and compliance risks and results in inferior data quality. As a result, data analysts and business users often mistrust their sources and lack confidence that data is accurate, timely and valid.

Many companies have implemented data lakes in an attempt at central storage, but the approach has proven largely unsuccessful. Data users often have a difficult time finding and accessing the right data for analysis. In 2017, we’ll see a rise of certified data sets created by IT and data analysts, which validate groupings of disparate sources and allow for easy access by business users. Sharing these certified data sets across departments will ensure data quality and enhance trust in data, analytics processes and results.

Data quality and data preparation now are two separate and distinct functions. But as they evolve, data preparation solutions are now incorporating many data quality capabilities, and data quality vendors are addressing data preparation issues. In 2017, data quality and data preparation will converge and organizations will better understand how to implement capabilities from both for the best analytics results.

More companies are beginning to use data from internet of things devices for analytics. But they’re finding that it’s no longer effective to put this information into a repository that does not have the capabilities to efficiently analyze data coming from IoT devices. Users collecting disparate data need the ability to maintain time stamps and then assemble, aggregate and play back information over time for a holistic view. Thanks to IoT devices and the real-time data they produce, next year we’ll see a rise in demand for time-series databases along with real-time data preparation functionality.

Machine learning or algorithmic analysis applies intelligence to data before it is cleansed, prepared and analyzed, resulting in better data sets. With smart data, users can obtain insight into what others have done and how it complements other data sets, improving analytics processes. In the new year, we’ll see more organizations leveraging smart data for analytics and to improve operational processes.

Advanced analytics processes traditionally have been delegated to data scientists. But more vendors are adding advanced analytics capabilities into their solutions, empowering business users with the ability to tackle this process to obtain predictive insights. In 2017, we’ll see advanced analytics transform from a novelty to a core capability that drives company operations.

Data virtualization will become more popular for analytics processes. It’s a technique with a lot of promise. It cuts costs because organizations don’t need to create warehouses; it helps with real-time analysis because data doesn’t need to be moved; and it increases agility, enabling users to analyze more sources faster.

Data virtualization had its share of barriers over the years that have prevented it from being used for analytics. Though challenges still exist, we’ll see renewed interest in this technology throughout 2017, driven largely by vendors bringing data virtualization together with data preparation to create an information architecture that delivers self-service agility at a lower cost. In cloud computing, we’ll see the technology’s prominence reach a whole new level in 2017, with more data accessed from and stored in cloud-based repositories for data discovery than on-premises systems.