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Big Data Analytics
Submitted by supriya.malineni-sm on 15 November 2022

Role of Big Data in Digital Transformation

Data is one of the most critical aspects of any digital transformation strategy. No investment in enterprise digitalization will produce a return on investment if there is insufficient or unreliable data to support digital services or processes. Beyond data availability and quality, is the issue of accessibility and integration. Data which sits in a silo may hold tremendous promise but may be practically worthless if it cannot be accessed easily and integrated with other data sources to create a deeper understanding of a process or opportunity. As such, every digital transformation strategy should include a significant focus on data.

Does the data exist?

An important issue to consider when planning a digital transformation should be consideration of whether the data needed actually exists. To answer this question, it is often best to start at the end. By understanding the types of analyses and reports that will be required, organizations can work backwards to determine the types of data that are needed to support the desired analysis.

If the data exists, it is important to consider the following:

  • Is the data of good quality and reliable?
  • Can the quality be improved?
  • Where is the data housed?
  • Is the data usable and accessible to the extent needed to support the analysis?

Conversely, if the data does not exist, can the data be generated? If the data can be generated, the same four points above should be considered for the newly generated data. If the data cannot be generated, enterprise digitalization does not have to be abandoned. There may be ways that a missing dataset can be replaced with a close approximation created through data modelling of a related dataset, or based on the market experience of subject matter experts.

The importance of data management

Creating a data management strategy is a critical aspect of any digital transformation initiative. Data management includes a series of processes to help improve the quality of data, ensure it can accessed to the extent needed for proper analysis, and to create integrations with other datasets which are needed to support planned analyses.

Improving Data Quality. The first step in data management is improving data quality. This assessment should include consideration of the relevance, accuracy, format, and completeness of the data. To address issues that might be identified here, data cleaning and manipulation is often needed. It is critical that when data manipulation is required that it does not introduce bias into the dataset, and that all data manipulation is done objectively and consistently. Artificial Intelligence (AI) can be an excellent tool for improving the data quality of large datasets. A well planned AI can quickly and reliably perform data manipulation on vast amounts of data making datasets which previously impractical to use, a valuable resource for organizations.

Ensuring Accessibility. A reality of legacy technologies and systems are data silos. Many organizations continue to use old software and systems that are no longer supported by the original software vendor and lack the integration APIs needed to be able to easily work with the data in conjunction with other data sets.

The best way to remove the data accessibility barriers created by legacy systems is to replace or upgrade the them. Where budgets allow, a modern cloud-native replacement for a problematic legacy system could not only help resolve data-related challenges, but also provide many operational advantages to the organization. Because system upgrades or replacements are often not feasible within available budgets, data integrations must sometimes be relied upon to create work-arounds to improve data accessibility.

Integrating datasets. The majority of digital transformations that fail, do so because of problems with the data integration. Creating an unified data layer is a key tactic for eliminating data silos, data fragmentation and ensuring data quality. A unified data layer is an architecture that, when used in conjunction with a data strategy, integrates disparate datasets in a disciplines way to create the holistic views needed to power digital transformation. A strong digital transformation vendor can guide organizations on creating an unified data layer that can reliability overcomes data integration and quality issues.

The benefits of digital transformation can be many. Those benefits are only possible if the digital transformation strategy is supported by high quality data. For more information about Trigyn’s digital transformation and data management services, contact us.