We have been at the point at which data pervades business practices for some time now. Every company holds huge amounts of data, and every company is looking for ways to exploit it for maximum competitive advantage. As a result, data analytics sees a new ‘next big thing’ just about every quarter, some of which embed themselves but most of which fizzle out. The latest of these unpredictable fads is one that has been revived in a Forbes piece by Bernard Marr: Data Socialization.
To understand data socialization, it’s important to first understand data democratization. The huge volumes of data collected were once incomprehensible to the layman, and the ability to draw anything from these huge sets rested solely with a particular class of data scientists and analysts. Today, though, data silos are being broken down and access to data is going company-wide. Organizations are increasingly realizing the benefits of a collaborative, holistic approach to data analytics and the subsequent decision making, with improved data visualisation techniques opening the data up to all.
Data socialization takes data democratization and builds on it. Social media’s impact on end user expectations is such that even in a business environment, users expect social capabilities. Regardless of how it’s formatted, users want free access to information and the ability to share data with others. Adding social functionality to data can not only put it in front of the right people in a timely fashion, but ensure that the data can be assessed through similar metrics to social - reach, engagement, etc. Organizations using a data socialization platform have the opportunity to leverage ratings, comments, and discussions to give everyone in the organization the information they need to use the right data.
Another element of data socialization to be aware of is the ability for users within an organization to understand how a set of data is being used by other teams. Socialization allows for teams to leave their mark on data sets, add comments, show amendments and the reasons behind them, etc. It’s an environment organizations hope will foster collaboration, demystify the data sharing process, and make it easy for teams to select data that is relevant to them.
Jon Pilkington, Chief Product Officer at software company Datawatch, said: ‘Data socialization is the next step in the evolution of data accessibility and self-service analytics—it’s a new way for organizations to think about, and employees interact with, their business data.’ He explains that users are able to ‘search for, access, share, and reuse prepared, managed data, as well as leverage user ratings, recommendations, discussions, comments and popularity to make better decisions about which data to use in analytics processes.’
Companies have learned that rather than imposing centralized, top-down data directives, it can be more effective to embed data and analytics into the organization as a whole. Marr uses the example of shop floor sales staff having instant access to personalized insights about customers, or giving engineers the data to detect when a machine is likely to fail. Now imagine if this these workers had information on previous uses of that data and how best to leverage it, as well as indications as to which parts of the data were significant. The hope is that, through data socialization, employees with little experience of analyzing data sets will no longer be left in the dark when it comes to exploiting it.