The new mission of data manager

The New Mission AI Brings to the Data Manager

by Business

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

    Predictive AI

    What is predictive AI used for in companies?

    by Business

    Predictive AI is one of the most useful tools for leveraging business data to make decisions. But what is the reason for this prevalence, and how does it differ from traditional predictions?

    The main reason is that it provides a roadmap for planning critical aspects such as sales and stock management, basing decisions on data rather than merely on individual experience or biased perspective.

    It does not mean that the prediction results are unquestionable. On the contrary, the results become an additional tool that decision-makers can use to work more reliably, securely, and conveniently. In other words, human elements like experience and intuition are still necessary for making decisions with a higher success rate.

    A key reason for AI and human experience to continue working together in decision-making is that each considers different elements of the environment. It makes them complementary, and ignoring either can lead to an incomplete view and less accurate decisions.

    How does predictive AI work?

    Before answering this question, it’s important to mention that before AI gained popularity, statistical models were used to plan sales, manage stock, etc. These models were the only data-based resource that allowed decision-makers to make informed decisions.

    Today, AI combines statistical analysis with machine learning models. Machine learning mimics how humans learn, enabling AI to perform tasks like data classification and predicting future outcomes.

    Machine learning is the set of algorithms that allows AI to learn from data. The accuracy of the prediction depends on factors like the quality and quantity of the data.

    How does data quality affect the results of predictive AI?

    To understand the impact of data quality on results, we could compare it to the learning of a student who reads books with outdated information versus a student who has access to updated information. The former is more likely to give incorrect answers on an exam, while the latter has a higher chance of success.

    We can think of predictive AI as a student learning how the data in your company behaves and developing the ability to analyze it and make predictions. Continuing with the example, the more time the student spends learning, the better their results will be in exams.

    In other words, learning is a skill that can be trained and has the potential to improve with the necessary resources for training. These resources are primarily high-quality data and time. Depending on the volume of data or the complexity of the prediction, training may require more time.

    Another important aspect is having the right data for each prediction. For example, if we want to know the average ticket value for a customer profile, we need data that defines that profile, such as age, gender, or location.

    In conclusion, predictive AI provides companies with tools to make data-driven decisions with a deeper level of analysis. However, the accuracy of the results still depends on human responsibility, such as the data provided and its quality.

    At RCM Software, we work to ensure that companies can benefit from AI throughout the entire data lifecycle. That’s why our data orchestration platform and BI both feature integrated AI.