Business and Technology Insights

Top 5 Areas of Data Management Enabled by Self-Service

 
January 27, 2020

In the good old days (and, in the swiftly evolving world of Data Management, “old” translates to “recent past”), enterprise business teams would depend on IT for provisioning the required data for internal analysis. Therefore, the quintessential persona of the user of a data management software, was “IT.” Things are changing with data management software increasingly being designed to have: (1) multiple features of automation (2) less “IT-ish” way of interacting and working with the software, and, (3) support for usage in cloud. As a result, business users are now more able to work with data management software of their own instead of relying on IT. In other words, the “self-deliver” approach is gradually replacing the “request-deliver” model for data provisioning.nagement where self-service can play a role

In general, the extent of automation and ease-of-use of a feature by a person of non-technology skillset will determine that feature’s availability in self-service mode. Here are indicative areas where you can enable self-service:

  • Data Quality Diagnosis: This could be the most fundamental area where adoption of self-service would be a no-brainer. Enterprise business teams continue to struggle to judge the quality of their data, which is of heterogenous provenance — generated internally, moved over from some other organization as part of inorganic growth or received from some third-party stakeholder. Business users need a simple mechanism to feed the input data to the data profiling software and let the software provide insights of its own about the quality of data.
  • Data Preparation: While the term “data preparation” can have a multitude of interpretations and connotations, at the most simplistic level, it denotes a set of data massaging activities performed to “prepare data,” as in to “make data ready,” for some specific function. Data preparation can include a combination of data management processes such as consolidating and integrating data, improving data quality and anonymizing certain data fields on a need-basis, among others. A self-service approach will allow you to align the “data management functionality boxes”, quickly configure processing rules (preferably, policy-based) for each box and run the sequence in an automated fashion. For instance, a self-service data quality improvement process will allow a data steward to quickly setup a batch job with “frequently used” rules already configured.
  • Data Analytics: In context of a data analytics software exhibiting self-service features, it is not a matter of “could be,” but rather “should be.” It is imperative that data analytics software be self-service because key users of the software would be from the business side who would like to derive insights from the software with agility.
  • Test Data Management: You would expect most medium to large enterprises to have a dedicated central body responsible for test data management (TDM) across a multitude of DEV, QA and testing environments in a consistent and scheduled manner. Self-service can play a role in the automated discovery of sensitive data attributes through intelligent matching of data and metadata patterns. Furthermore, self-service TDM software can provide QA owners an intuitive interface to define required data masking policy in business terms, a user-friendly interface to quickly build batch processes to create masked data based on policy, mechanism to execute such batch processes on-demand or on-schedule, an ability to build test data archives and an interface to search such archives on multiple dimensions and an ability to refresh database contents to a certain point in time among others. Software could also provide some prepackaged and reusable execution templates capable of being quickly configured and executed for agile data provisioning.
  • Enterprise Reporting: A non-technical user may not be adept with writing SQL queries and even less aware of column-field metadata. Therefore, a self-service reporting software must support building queries in business terms (as in, “Customer Birth Date”) rather than its IT connotation (as in, “CST_BIRTH_DTE”). An even more sophisticated self-service reporting software can harness the power of ML, AI and NLP to build queries in “spoken language.”


Autoeads the way…

Enterprise business units are under perennial pressure to do more with less. With a focus on automation, business teams are already investing in procuring appropriate data management software and have upped the ante by expecting better usability and intelligence features from such software. On the other hand, data management software vendors are continuously innovating to meet these requirements and make increasing number of data management features available in self-service mode.

Sumeet Bhide handles the Marketing and Branding, and Learning and Development for TCS MasterCraftTM DataPlus, which is a data management software from TCS.  Sumeet has 18 years of experience in TCS and has delivered crucial IT projects in Banking, Wealth Management and Telecom areas. His current areas of interest include Data Privacy and Data Management. 

Sameer Rane handles market analyst connect and marketing aspects of TCS MasterCraft™ DataPlus, which is a data management software from TCS.  Sameer has 15 years of experience in the IT industry and has played a leading role in maintenance, deployment, and management of software products and solutions. His current areas of interest include Data Privacy and Data Quality Management.