Looking back over the past year, it is clear that self-service solutions have become widely adopted by organizations across industries, as users want to access and analyze information immediately without having to wait for IT to run a report or provide a database. This trend will continue into 2017, and when it comes to self-service Analytics, tremendous opportunity lies ahead.
As companies wrap up 2016, it is imperative that they prepare now for the future of self-service Analytics. Specifically, they should be aware of and ready to address the following trends:
- Data socialization: In 2017, data socialization will take the Analytics world by storm. Social Media platforms have dramatically increased peoples’ expectations about the availability and timeliness of information. Users increasingly have these same expectations for business information, regardless of where the data resides or how it’s formatted.
Data socialization combines self-service visual data preparation, data discovery and cataloging, automation and governance features with key attributes common to Social Media platforms.
This new capability will empower Data Scientists, Business Analysts and even novice business users with the ability to search for, share and reuse managed data to achieve true enterprise collaboration and agility, resulting in better and faster business decisions. Companies, departments, and project teams will be able to optimize data sharing, and achieve greater innovation and information curation.
- Smart Data: Machine Learning or algorithmic analysis occurs before a business user or analyst uses the data. Applying intelligence to data (hence the term “smart data”) before it is cleansed, prepped and analyzed results in better data sets. And the Data Quality will only continue to improve, as people work with it more and more and Machine Learning kicks into full gear. With Smart Data, users can obtain insight into what others have done with the data and how it complements other data sets, which will significantly improve Analytics processes.
- Data storage and access: Data is now distributed all over the organization, and it’s often managed in isolation. This means data is uncontrolled and unpredictable. Poor information governance increases security and compliance risks and results in Data Quality issues. As a result, business analysts and self-service Analytics users often lack trust in their data sources and confidence that data is accurate, timely and valid.
Many companies have Data Lakes to act as a storage repository for all data to address the issue of Data Silos and accessibility. However, Business Analysts have a difficult time in finding the right data sets to use in these large systems. Thus, we will see a rise of certified data sets in 2017, which validate groupings of disparate sources and allow for easy access by the business user. Sharing these certified data sets across departments will ensure Data Quality, and enhance trust in data, Analytics processes, and results.
- Streaming data: The mainstream deployment of real-time infrastructure has prompted organizations to figure out the best way to leverage the data it produces. To yield maximum ROI of real-time data, business users must prep, enrich and blend it with historical information. The result is the most up-to-date and accurate data for Analytics processes and a complete 360-degree view of the business for better decision making.
- Cloud implementation: Cloud Computing and virtualized infrastructure have been all the rage these past several years, but 2017 will take their prominence to the next level. Next year, we’ll see more data accessed from and stored in cloud-based repositories for data discovery than on-premise systems. Additionally, Data Virtualization will emerge stronger than ever in the Analytics world, as it cuts costs because organizations don’t need to create warehouses; helps with real-time analysis because data doesn’t need to be moved; and increases agility, enabling users to analyze more sources faster.
- Data drowning: Data Analysts – and companies as a whole – are drowning in their data. In companies’ attempt to manage Big Data, we’ll see Data Lakes, Hadoop, and the use of Apache Ignite and Spark take a stronger foothold in 2017. These Big Data solutions work well for specific use cases such as customer micro-segmentation, multi-channel marketing and real-time Analytics.