The arrival of artificial intelligence in the business world is having a gradual yet steady impact, particularly in areas like data analytics. That’s why we want to present some of the challenges it brings, how to face them, and how the role of data manager can truly make a difference.
What are the main changes AI brings?
The data manager’s main mission used to be summarized as keeping data organized and available. However, today’s landscape introduces new demands; here are some of the most relevant ones:
- Data quality and preparation: Keeping data clean, well-prepared, and properly governed becomes more important, as the success of AI relies on the quality of the data it uses.
- Technological and cultural adaptation: This includes integrating new formats such as images, videos, and text. It means also adopting new architectures, processes, and mindsets.
- Governance, traceability, and ethics: Being prepared to comply with legal frameworks and uphold principles of ethics, privacy, and transparency.
- Taking on a more strategic and bridging role: Strengthening collaboration between business, technology, and data teams. This helps build the synergy needed for a company to remain competitive.
- Enabling self-service without losing control: Carefully assessing which self-service options align with the company’s needs without compromising data governance.
Best practices to take on these challenges
Govern your data: Now is the time to review how your company defines and enforces its data governance policy. It’s essential that it includes clear roles, policies, and monitoring mechanisms. Adjust the plan to your company’s current reality so it is ready for the future.
- Automate tasks: Identify and automate routine data quality, cleaning, and lineage tasks to save time and improve efficiency.
- Store the right data: Evaluate whether the data being stored and processed serves a business purpose. Sometimes data is managed without a clear goal, leading to unnecessary resource consumption.
- Promote a data culture: Everyone who interacts with data in your company should understand their role. Internal training programs can help strengthen data governance and improve results.
- Don’t forget ethics and privacy: These must be integral components of your data strategy to prevent bias, ensure transparency, and comply with regulations.
Common myths about AI
Along with following best practices, it’s important to avoid decision-making based on unfounded beliefs. Here are some of the most common myths:
- AI doesn’t make mistakes: This is one of the most widespread misconceptions. Human oversight must remain a key part of the process.
- The data manager is only a technician: Thinking of this role as purely technical disconnects it from the business and limits its impact.
- Rigidity equals good data governance: In reality, balance is the key to maintaining both governance and agility.
What are the benefits of the data manager’s evolving role?
The main opportunity this evolution brings is to rethink the role as a more integrative position. This paradigm shift enhances the potential of data analytics and AI, while driving better-informed decision-making and stronger organizational structures.In conclusion, there are many opportunities in this space; this is why at RCM Software, we’ve developed solutions and tools designed with this integrated vision in mind. If you’d like to learn more about our ecosystem of business-oriented solutions, get in touch with us, and we’ll reach out to you