Wednesday, February 1, 2023

Dark Side of Enterprise-Wide Data Management Strategies

By Nikhil Sonawane - September 09, 2022 4 Mins Read

Organizations need to be aware of the dark side of enterprise-wide data management strategies to make necessary changes in the ways they gather, store, and process data throughout their business.

Enterprises today generate a huge volume of data from various sources that they need to gather, store and process. Many enterprises restrict their data-driven initiatives only to structured data eliminating the unstructured or dark data from the process. There is a surge in the volume of dark data that is generated, which can have valuable insights for businesses. Organizations can leverage data-driven approaches to generate more revenue and optimize processes. CIOs should consider leveraging structured, dark, and unstructured data to avoid all the potential bottlenecks in business continuity.

Here is the dark side of enterprise-wide data management strategies that CIOs should be aware of:

Data schemas are either too lenient or stringent

Irrespective of the efforts invested by the DataOps teams, it can be challenging to determine the schema constraints. It results in too stringent or too lenient schemas for defining the values in the various data fields.

Data scientists that add airtight constraints will hamper the user experience because they were not able to get the required answer found on the narrow list of acceptable inputs. Enterprises with a lenient data management strategy will enable users to feed random values with little consistency. It can be a tricky task for the CIOs and the DataOps teams to fine-tune the schema to ensure effective enterprise-wide data management.

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Vigilant data laws

Clients and end users are becoming more cautious about the data management strategies of the organization. The users are becoming more cautious about how AI algorithms are capturing data of their every action. Hence, data privacy and protection policies are very stringent around the world, and regulatory bodies are evolving them to patch all the potential pitfalls. It can be a tricky task for the DataOps teams to determine all the applicable compliance policies and changes to them. Non-adherence to regulatory bodies like General Data Protection Regulation (EU GDPR), Health Insurance Portability and Accountability Act (HIPPA), California Consumer Privacy Act (CCPA), and others might have legal implications. Moreover, securing the data is another challenge that businesses face because a malicious actor is exploring all the opportunities to execute a data breach.

Challenging to evaluate unstructured data

The majority of the data gathered from various sources is unstructured data. Every department in the organization generates different types of data that have valuable insights to improve business performance. If the data is not structured properly, it will be challenging for the DataOps teams to evaluate unstructured data. During the entire customer lifecycle, the client interacts with multiple touchpoints at various stages to get their issue resolved. The presales, sales, and customer service teams generate valuable insights that data scientists can use to make data-driven decisions. CIOs should consider integrating robust unstructured data analytics tools that structure the data and drive actionable insights to ensure effective data management throughout the enterprise.

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Data-driven decisions can be judgment calls

Data can offer business plenty of precise, actionable insights, but how business interprets them is what matters the most. Irrespective of all the Artificial Intelligence (AI), Machine Learning (ML), and data analytics tools integrated into the IT infrastructure, the last decision is up to the resources. Organizations can set thresholds to determine the performance of the tools and other systems. However, even data-driven approaches might have gray areas that businesses might overlook. A data-driven approach can enable organizations to make accurate decisions, but if the data fed to the systems is inaccurate, all the efforts are in vain.

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AUTHOR

Nikhil Sonawane

Nikhil S is a Tech Journalist with OnDot Media. He is a media professional with eclectic experience in communications for various technology media brands. He brings his eye for editorial detail and keen sense of language skills to every article he writes.

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