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Data Integrity – Why It Matters

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Julian Jackson's picture
Staff Writer, Energy Central BrightGreen PR

Julian Jackson is a writer whose interests encompass business and technology, cryptocurrencies, energy and the environment, as well as photography and film. His portfolio is here:...

  • Member since 2020
  • 501 items added with 186,021 views
  • Jan 10, 2023

Good, robust data can be a major competitive advantage for utilities in the coming age of digital transformation. In the age of Big Data, organizations that harness data effectively and promote data integrity can make better business decisions, improve analysis quality, and reduce the risk of data loss, corruption or cyber threats.

A 'data-healthy utility' ensures that its data is consistent and secure across the entire life-cycle of that data.

The importance of data integrity for energy and utilities organizations is central to future business best practice. Now companies are deploying advanced technologies like AI, machine learning, Internet of Things (IoT), robotics, blockchain, and cybersecurity to improve operations. Chatbots and cloud computing are being implemented to enhance and streamline the customer experience. These technologies generate huge amounts of data. This means that organizations need to ensure 
data accuracy and integrity to function effectively.

The challenge is how to make this data useful and generate actionable insights so that critical business decisions are optimal. How can today’s utility organizations ensure data integrity while quickly making sense of their data from multiple sources and in diverse formats, to make meaningful decisions within a rapidly changing market?

The Dangers Associated with Data Integrity Issues

Data integrity is a complex and multi-factorial issue. Data professionals must be alert to the various risks that can affect data integrity and quality. These include the following:

Human Error

Human error is of course a major risk factor for data integrity. Human errors can occur when data is wrongly input, processed or analyzed. Where companies rely on multiple data sources from a variety of inputs, there can be data integrity issues.


Misconfigurations and Security Errors

If access is not configured correctly — for example, if incorrect user permissions have been set — the data may be more vulnerable to cybercriminals or security breaches. Similarly, if data is not effectively secured with encryption and access controls, it can also be compromised by unauthorized individuals or hacking programs.


Compromised Hardware

Hardware can fail or be damaged, data can be accidentally deleted or overwritten, it also can be corrupted during data transfer and storage, and information may be unintentionally accessed or overwritten by other data users. A critical time is a data migration. It is vital for a company to assess data quality and integrity on these legacy systems before making the shift.


Unintended Transfer Errors

When data is migrated between different data systems, the information may be accidentally lost or corrupted during the transfer process. This situation can be a significant data integrity risk, especially if data is input from different teams or sources.


Malware, Insider Threats and Cyber-attacks

Data integrity can also be compromised by malware and viruses that corrupt the information. It’s important to have protections in place against malicious actors seeking to steal data and cyber-attacks that target data repositories or data infrastructure.


Managing Data Integrity Through Data Governance

To mitigate the many data integrity risks, data managers need to put in place a robust data governance strategy that includes data integrity checks throughout. This process may involve:

  • Data integrity assessments

  • Cyber security and data literacy training for data users

  • Practical improvements that reduce data errors

  • Data redundancy and excellent backup practices to safeguard data reliability

  • Data encryption for data security

  • Data auditing for detecting data integrity issues

  • Robust cybersecurity measures

Physical integrity and logical integrity are the two types of data integrity which need to be secured to ensure that the data will be uncompromising. The first is they physical security of that data, that it can't be damaged by an earthquake or a server fire. The second involves ensuring data remains unchanged. This helps to keep data safe from both human error and malicious attacks.

Utilities will need to keep healthy their data integrity as they move through the digital transformation.


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