As consumers become more aware of their data rights, data governance is becoming more and more relevant. It includes a set of metrics, standards, policies, and processes that allow companies to use the information correctly and responsibly. While targeting their objectives, their use of data must be efficient and effective. In this way, data governance encompasses responsibilities and processes for the security and quality of data, which is used by an organization. It specifies the following:

  • What action do you take on a specific set of data?
  • Which data requires a certain action?
  • What are the situations that merit an action?
  • What are the methods used to take action?
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Data Governance Strategies

An organization can avail the following advantages with a proper data governance strategy:

  • Data governance improves data quality. It offers a standard to ensure that data is consistent, complete, and accurate. 
  • Data governance enables a useful capability through which organizations can unravel the location of key entities’ data. This helps with smooth data integration.  
  • Companies can use a 360-degree view to understand clients and other commercial entities. 
  • Data governance ensures that they have an effective platform to meet government regulations such as  HIPAA and GDPR or address industry standards like PCI DSS (Payment Card Industry Data Security Standards).
  • Data governance boosts data management, introducing the human element in the data-driven and automated world. For data management, it constructs the best practices and codes of conduct. As a result, concerns that are overlooked traditionally in data—such as compliance, security, and legal—are tackled appropriately.

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The Role of AI in Data Governance 

In a bid to outpace each other in data analytics, businesses are continuously looking for effective solutions. You can extract maximum value from your data if you have set up AI with data governance policies. AI helps data management to realize which of their practices are ineffective and which are working the best. 

Different sources produce a wide range of data, based on the associated industry. Many organizational departments have been looking to utilize data and enhance their operations. For instance, sales departments that study consumer trends are able to get useful insights. Nowadays, many organizations have implemented predictive analysis to enhance the efficiency of their business operations. 

Similarly, manufacturing plants are also heavily investing in AI for analytics. By taking these steps, they aim to identify industry requirements so that they can adapt their manufacturing processes accordingly. 

AI also is used for maintenance purposes. When quality is affected due to a certain machine, analytics traces the root cause. Afterward, it is up to management to make a decision on whether predictive maintenance is needed. 

By adding AI to the mix, businesses can detect anomalies. For instance, if there is a breach in a data center, management can train an AI-based solution to identify any cyber attack. For this purpose, it goes through machine learning algorithms and consumes voluminous amounts of data. As a result, when a cyber threat emerges, AI can pick out the pattern and notify the authorities in time before data is compromised. This also means that AI can add a lot of automation in the privacy, compliance, and security of data. Hence, companies can ensure that they have a 24/7 protector that, unlike human resources, can tirelessly monitor their data transmissions. 

AI makes sure that data reaches the right user without getting intercepted by cybercriminals who may employ man-in-the-middle, spear phishing, ransomware, spyware, or any other cyber attack. Essentially, AI is democratizing data governance. For instance, AI is used in automated process discovery to analyze behavioral data that is generated during data processing. In this way, digital records are derived from behavioral data. 

The Challenges of AI and Data Governance 

Data governance is marred by the following challenges: 

  • Exponential Data Growth

There is a reason why data is now referred to as “big data.” The amount of data production and storage has grown at an exponential rate. Computer technologies are booming. The number of devices that can store data has increased. 

From desktops and laptops in the 1990s to tablets and smartphones by the end of the 2000s, these devices were mainstream. Today, with the expansion of IoT, an increasing number of devices are being connected to the internet: smartwatches, fitness trackers, refrigerators, TVs, home security systems, and even alarm clocks. While these changes are welcomed with open arms, the impact of this data growth on organizations needs serious reflection. They need a reliable infrastructure that can handle all this data. 

  • Switch from Legacy Systems

Legacy systems can’t keep up with modern data demands, so they have to go. However, the paradigm shift to newer systems is complex. You have to build a framework that can encompass scalability, security, and compliance for the new system. 

  • Adherence to Framework

The effectiveness of AI relies on access to all data sources. Data must be unbiased, complete, and your data-related departments must adhere to your framework for proper data governance.

Ethical Implications and Responsibilities 

GDPR and CCPA (California Consumer Privacy Act) are some of the modern regulations that are geared toward protecting user data. They provide a set of minimum standards that must be met. Refusal to follow them can result in serious penalties. 

Some of the leading data-driven organizations have gone beyond these regulations in regard to how they connect with users and their data. For instance, consider the case of Io-Tahoe, a company that offers smart data discovery solutions. It understood the significance of the security for PII (personal identifiable information), but now it helps its customers to work with sensitive data like PII proactively. 

However, for the majority of the companies, GDPR is still the low-bar. At present, AI has extremely low standards in terms of ethics. Despite the fact that you can find many SEC registrants raising their voice on AI’s vulnerabilities, it is still apparent that businesses are not focusing and investing in this direction. 

In regulated industries, organizations are attempting to tackle AI ethics, along with data governance. Their role has made them de-facto leaders in AI ethics. Still, the lack of data privacy and data governance in the U.S. is restricting efforts to devise an adequate standard for AI ethics. This is why some experts believe that Europe will be the first to come out with an AI ethics law framework within the next five years. 

By taking your AI ethics seriously, you can ensure that public trust in your company is heightened. Your AI systems must:

  • Treat everyone fairly
  • Engage and empower people
  • Work safely and reliably
  • Respect privacy and protect data
  • Have an algorithmic accountability
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Final Thoughts

Policies for data governance have to grow with upcoming technologies, business practices, and emerging laws. Today, companies have to think about how they are going to use data in terms of storage and processing. 

The inclusion of AI can change things for the better. With automation, they can enhance the implementation of security and compliance in their data centers. 

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