Five Data Preparation and Analytics Predictions for 2017
Believe it or not, 2017 is fast approaching. And while 2016 was a breakthrough year in the self-service data preparation and analytics world, 2017 will bring just as many – if not more – innovations that continue to transform how data scientists, data analysts, and business users harness insights to deliver business value and improve operational processes.
Here are five data prep and analytics predictions to watch for in 2017.
1. Data Quality and Data Prep Will Begin to Converge.
Today, data quality and data preparation are two separate and distinct functions. But, as they evolve, data prep solutions are starting to incorporate many data quality capabilities. Data prep pulls information from a variety of disparate sources and then blends and manipulates it so it’s clean and accurate for analysis. Similarly, data quality vendors are starting to address data prep issues. 2017 will be the year that data quality and data prep converge, and organizations will better understand how to implement capabilities from both for the best analytics results.
2. Internet of Things (IoT) Data Will Drive Demand for Time-Series Databases.
The IoT era is upon us, and more companies are beginning to leverage data from these devices for analytics. But, what is the best way to access and use this data? It’s no longer effective to put this information into a “dumb” repository, or one that does not have the capabilities to efficiently analyze data coming from IoT devices.
Users collecting disparate data from across the organization and from different parts of operational processes need the ability to maintain timestamps and then assemble, aggregate and play back information over time for a holistic view. Thanks to IoT devices and the data they produce, next year, we’ll see a rise in demand for time-series databases, along with real-time data prep functionality.
3. Advanced Analytics Will Become More Pervasive.
Advanced analytics processes have traditionally been delegated to data scientists. But more vendors are starting to add advanced analytics capabilities into their solutions, enabling everyday business users to tackle this process and obtain predictive insights. In 2017, we’ll see advanced analytics transform from a novelty to a core capability that drives company operations.
4. Data Virtualization is Back.
Data virtualization will become more popular for analytics processes. Data virtualization is an approach that, instead of moving data from a source into a data warehouse for analysis, leaves the data where it resides and creates a virtual data warehouse. Data physically resides and stays in transactional systems, but these virtual warehouses let users see information in a logical order. It’s a technique with a lot of promise. It cuts out 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.
While more than 10 years’ old, data virtualization had its share of barriers that prevented it from being widely used for analytics processes. And though challenges still exist, we’ll see renewed interest in this technology throughout 2017 – a trend largely driven by vendors bringing data virtualization together with data prep to create an information architecture that delivers self-service agility at a lower cost.
5. Data Socialization Will Take the Data Prep and Analytics World By Storm.
Self-service analytics empowers data users to make business decisions without having to rely on IT. But, for all of its benefits, the technology has also caused the data landscape in many companies to become like the “Wild West.”
Data is now distributed all over the organization, and it’s often managed in isolation. Because data is so dispersed, users don’t know where to find all of the information they need for analysis, let alone how to access and use it. Users are wasting valuable time sifting through volumes of extraneous data and still aren’t able to obtain the right information. Because of this complexity, users are turning only to documents that reside on their desktop. Not only does this cause analysts to make valuable business decisions based on incomplete information, but it also forces them to work in seclusion. Data and analytics outcomes aren’t being shared and reused for the greater good of the business; rather, users are starting every analytics project from scratch without the benefit of repeatable data modeling.
In 2017, data socialization will truly revolutionize self-service data prep and the analytics experience. This transformative new capability will integrate traditional self-service data prep benefits with key attributes common to social media platforms. The powerful combination will enable data scientists, business analysts and even novice business users across a company to search for, share and reuse prepared, managed data to achieve true enterprise collaboration and agility, resulting in better and faster business decisions.
As chief product officer, Jon Pilkington brings more than two decades of business analytics experience to Datawatch, including 18 years in the business intelligence market. Jon joins Datawatch from Sonian Systems, a public cloud email archiving vendor, where he served as vice president of marketing and product management. Jon helped that company raise more than $20 million in venture funding ...