Artificial Intelligence (AI) has caught the attention of every industry, and there seem to be no signs of slowing down.

According to IBM’s Global AI Adoption Index 2023 survey, over 59% of surveyed enterprises have explored or deployed AI and are planning to increase their investment. AI is expected to grow annually at 37.3% by 2030. 

There are various benefits for enterprises using AI.

 

  • Automate tasks:

    AI can help enterprises automate repetitive and complex tasks, enabling employees to save time and focus on high-value work. 

  • Enhance customer service:

    Solutions like AI-based chatbots and conversational AI can address routine and basic queries to free up the employees’ time and enable them to focus on building strong relationships with customers. 

  • Improve maintenance:

    AI can analyze data patterns, identify potential problems that could lead to unexpected downtime or safety issues, and ensure they are fixed before breakdown. This will help the company save money on unexpected repairs and downtime. 

  • Personalize experiences:

    The marketing and sales teams and the customer support teams can use AI to understand the customer’s behavior patterns and tailor ads and experiences according to the customer’s likes and preferences.

  • Strengthen cybersecurity:

    AI can play a pivotal role in detecting unusual patterns, such as a security breach, and flagging them on time to prevent cyber threats. 

While AI has been helping enterprises in all aspects, certain concerns and challenges have slowed the adoption. For example, according to IBM’s survey, 43% of enterprises are concerned about data transparency, while 25% worry about its complexity. Ethical use of AI and data security and privacy are also responsible for slow AI adoption. To add to woes, enterprises are also worried about AI hallucinations (a situation where AI produces unreliable, biased, and inaccurate results), which makes the AI model less trustworthy. 

Addressing these challenges requires improving the quality of data and managing and governing it properly. Let’s understand how enterprises can do that. 

 

The Significance Of Data Management And Governance In The AI Age

Enterprises must focus on improving the quality of their data to build accurate and reliable AI models. Poor quality, biased, or incomplete data can negatively impact AI output and result in inaccurate predictions. Furthermore, outdated or irrelevant data can lead to decision-makers missing significant trends that could have given them a competitive advantage. 

 

Together, data management and governance can help enterprises improve data quality.

  • Data management:

    Enterprises collect vast amounts of data, such as customer data, sales figures, and market research data. This data is used to train AI models and provide decision-makers with actionable insights to make data-driven decisions. While it might seem like a straightforward solution to building successful AI models, enterprises face challenges managing the data. The data is either available in an unstructured format or silos across disparate platforms. The lack of accessibility and consistency makes data unusable. That’s why data management is so crucial. Data management is the process of collecting data from different sources, organizing, and storing it to make it accessible and usable for all users. It ensures the data is consistent, complete, and accurate enough to be fed into AI models. 

  • Data governance:

    Enterprises need to establish proper procedures, policies, and standards to ensure the quality of their data. They must also adopt data cleansing, normalization, and validation procedures to improve their data’s accuracy, consistency, and completeness. By implementing these data governance practices, enterprises can generate high-quality data and enhance the performance and reliability of AI models. 

Data governance is also crucial from the perspective of AI security and ethics. As the risks of biased outcomes and security threats emerge, data governance frameworks can help enterprises mitigate them. It can also help enterprises protect sensitive information, prevent unauthorized access, adhere to AI and industry-specific regulations, and promote ethical and responsible AI practices. 

At a time when enterprises worry about unreliable and biased AI outputs, a disciplined approach to data management and governance can help them improve outcomes and build trust among customers and stakeholders. It can also help them foster innovation without compromising on ethics and security. 

Let’s look at how enterprises combine both to improve the quality of AI models. 

 

Steps To Improve Data Management And Governance

The potential of AI cannot be undermined, especially as 92% of Fortune 500 enterprises are already using solutions like OpenAI APIs to transform work. 

However, managing and governing the data is vital for improving quality and AI outcomes. Here are a few steps to improve data management and governance.

  • Standardize unstructured data to improve consistency and accuracy and make it valuable for the enterprise.
  • Ensure the data is trustworthy, as AI models will be trained based on that. Check the AI results regularly to assess the data quality.
  • Train the workforce on AI trends and best practices to improve data quality and prevent problems like AI hallucinations.
  • Identify key stakeholders who can formulate data governance programs and policies and implement rules and workflows to improve data quality. Ensure they actively work towards managing data quality.
  • More importantly, work with a trusted technology partner to improve data management and governance.

 

At Trinus, we help enterprises integrate data from disparate sources to provide valuable information, audit and monitor data quality to ensure accuracy and consistency, and establish data governance programs within the enterprise. 

Contact us to learn more about how we can improve your data management and governance initiative in the AI age.