Data socialization consists of a data management platform that helps in uniting self-service visual data preparation, data discovery and cataloging, automation, and governance features with key attributes common to social media platforms, thereby providing companies with the ability of leveraging user ratings, recommendations, discussions, comments, and popularity to make better decisions about which data to use.

Today data analysts are struggling to respond to business requests. Already, there is an endless shortage of data scientists and business data analysts. Companies continue to grapple with data access, data blending, and data reconciliation. These data scientists and analysts are often unable to find the information they require and are mostly unaware of the self-service data prep tools that are available to improve their productivity. Moreover, the constantly advancing social technology and the addition of social features has greatly increased peoples’ expectations about the timeliness and availibility of information. Users now increasingly have these same standards of expectations for business information, regardless of where the data resides or how it’s formatted. They demand instant access to data and the ability to easily share it with key stakeholders. Data socialization is the transformation of data accessibility and self-service across individuals, teams, and companies. It is reshaping the way companies think about, and employees interact with, their business data.

What is Data Socialization?

Data socialization enables groups of data scientists, business analysts, and other users across a company to search for, reuse, and share managed data. This helps in achieving true enterprise collaboration and agility. In data socialization, any employee can easily find and use data that has been made accessible to them within their data ecosystem. This helps in creating a social network of certified, curated, and raw data sets, having controls and limitations that are defined for each individual, which helps in fostering a culture of data access, where users or analysts can learn from each other, be more productive, and become better connected as they source, cleanse, and prepare data for analytical processes.

Features of data Socialization

Some key features of data socialization include:

• Having the ability to understand the relevancy of data in relation to how it is being utilized by different user roles in the organization. For example, a sales operation or an internal auditing.
• It also involves collaboration between key users and data sets for harnessing the knowledge that too often goes unshared.
• Data socialization also allows business users to be able to search on cataloged data, data preparation models, and metadata indexed by the user, application, type, and unique data values.
• It also helps in performing data quality scoring, suggesting relevant sources, and automatically recommending likely data preparation actions based on user persona.

Business applications are increasingly inheriting social features for improving business collaboration, thereby making individuals and companies more informed, agile, and productive.

Data socialization helps in bringing benefits to self-service data preparation and analytics, by completely eliminating the common barriers to data access and sharing. For BI analysts, data scientists, and business users, it boosts their productivity and speeds decision-making.

Data socialization helps in empowering business users, data scientists, analysts, and departments throughout the enterprise to work together with their data. Moreover, it provides the right data to the right person, such as a decision maker, at the right time. This is why, implementing Data Socialization is the next big thing in data analytics as it empowers you to capture millions of data points from their worldwide user bases and put them under the microscope to learn how, when, and why we communicate/share data, with each other.