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Sumeet Bhide

Data processing and delivery is witnessing a sea change with the ’shift-left‘ approach, where problem resolution and similar activities are carried out as close as possible to the end beneficiary (the enterprise business teams). Further, data management continues to reel under the avalanche of perpetually growing data. Data management software makers continue to upgrade and reimagine the software features to address these challenges. During the planning of upgrades, software dimensions such as self-service, scalability, technology-support, and so on, invariably draw attention and stories related to them enjoy priority in the product backlog. Undeniably, these primary dimensions are critical to the overall success and are the differentiation factors of the software.

However, a deeper analysis reveals that the primary dimensions are not the sole factors that determine the success of the software. Several ‘supporting’ features play a silent yet significant role in elevating the utility levels of the software. These features may not appear as important to a software architect, as those like scalability or support for technology-heterogeneity. Nonetheless, when implemented, they have a significant bearing on the software’s value and the effectiveness of its primary features. The key supporting features include:

  • Ease of Installation: The users’ initial apprehensions are addressed if the software is easy to install. A well-designed installation process ensures conformance to hardware/software pre-requisites early on, with relevant alerts being sent to users. Good installation practices are applicable for various scenarios across which a user must interact with the software, including modular installation. While one-click installation sounds fictitious, the installation approach can certainly be designed to minimize user intervention. Furthermore, fail-safe and accommodating approaches provide the flexibility to arrange the required resources later, while progressing with the current instance of installation.
  • Ease of Accessing Help: Given the complex nature of software data processing activities, the User Interface (UI) can seem daunting. In general, a friendly UI design is desirable, however, it is a subjective parameter. Accessibility of help information is the primary and the most common requirement that should be addressed well by a user-friendly software. It is not possible for users to manually search voluminous product manuals for help on a particular topic, rather, contextual help needs to be available when needed. Accessible help can come in various forms, such as a handy help-screen, hover-on tool tips, help-buttons for specific information and such. Futuristic instruments for enabling help include self-running ’how-to’ videos and chatbot services.
  • Understandable Error Messages: When the software fails to perform the intended action, a meaningful message must be conveyed to the user, mentioning the reason for the error, steps for recovery, and any alternate action that the user can undertake (where feasible). The message should also display the location of application logs for error-debugging. Overtly technical, esoteric and ambiguous error messages can confuse non-technical users and interfere with their experience.
  • Searchability: Software should enable comprehensive intra-software searching. When the value of any entity, such as data sources, policies, data processing jobs, is provided, the software should search its internal repositories, return all related entities and recent actions that were performed on them. This enables users to   resume work from where they had left it last.
  • Collaboration and Alert Features: Data management is performed by a team and not an individual. So, the software should facilitate channels of collaboration. Examples of such features include identification of team-members currently logged in; short messages and notifications about someone’s unavailability; provision for process configuration-frameworks that can be saved as drafts to enable multiple team members to work on a deliverable; logging of user actions to define ownership and accountability for deliverables; and triggering of alerts on mobile devices to keep the team abreast of the latest updates and enable quick reaction.
  • Ease of Interoperability: Any data-driven enterprise requires not one, but a multitude of software, for different data processing activities. Multiple and varied software must work in unison for enterprises to meet their overall data-related goals, by exchanging data with each other. The synergy arising from multiple software working together greatly amplifies the value of each individual software involved. Buyers prefer such software that can seamlessly interface with the larger external systems, rather than those that operate in isolation.

Looking Beyond the Obvious

While the primary dimensions of data management software clearly need continuous improvement, we must not lose sight of the other ancillary features that have a major impact on the success of the software. These features play a significant role in enhancing the usability of the software. While the must-have features for data management are prioritized for immediate market demands, it is important to consider the key supporting features that can lend a competitive edge to the software. While many claims offer the high-end primary features, software that offer pragmatic, user-centric ones can often prove to be the game changers.

About the author

Sumeet Bhide
Sumeet Bhide handles Marketing, Learning and Development for TCS MasterCraft™ DataPlus, which is a data management software from TCS. Sumeet has 19 years of experience in TCS and has helped deliver crucial IT projects in Banking, Wealth Management and Telecom verticals. His current areas of interest include Data Privacy and Data Management.
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