Being an architect in the past and managing and providing consulting for a lot of my customers, data governance is being looked at primarily to serve the regulatory requirements in the past. For any effective data governance there, it should be aholistic process, because it used to be a stand alone process. All the way to the consumption of feedback should be done right from the start. We recommend that to all of our customers. Continuous data governance is something that happens. It's not that, right? I devised a plan for that and I can take risk now that I know the requirements of the data. It is not possible to say yes.

Data governance is a constant process. Changes in the requirements of data are constant. The use of the data is constantly changing. The regulations are constantly changing. A complete understanding of what is happening, what has changed, why it is changed, and a record of that is very important. The data governance framework needs aholistic process. It is not a single process and should be continually reviewed and tracked.

People are definitely involved in this process and strategy. What do you think about the importance of data literacy to everyone in the organization? How should executives think about making sure everyone has the right skills to use data?

Data is the new oil that is being fed all over the world. Understanding where to use data is very important if it is a new oil. Data literacy depends on how to use it and where to use it. We should know where the data is available if we need it. There are two levels of data literacy. What is the data that is available, how good that data is that is available, how to access that data, how to process that data is one of the topics. Today's world has many constraints and the data is no different. There is a lot of sensitive information in it. Today's world has a thin line between the sensitive information and the data that is easy to consume.

If that is the case, the literacy of what data we are processing and how sensitive it is is very important. When the executives plan for data literacy programs in their organizations, they need to make sure that it's not only about the data usage, but also what is happening with it. That's why the investment of data literacy on people becomes very important. The people who design the systems and the systems that consume the data are the ones who should invest in literacy.

One part of digital transformation is streamlining and maximizing investments in operations across business units, which is an important part of data literacy, especially across the entire organization. Tech teams used to combine software development and operations to create devOps, which allowed for a more data focused way of working. This philosophy can be applied to other areas of the business, including artificial intelligence and machine learning, data to create dataOps, and finance to create finOps. These can be bundled together into a single term. It's an interesting way to bring different parts of the business together under one roof. The value can be brought to an organization as a whole.

There is an umbrella that brings in various operations that drives innovation through the technology to address the business requirements to take the business to the next level. All of the three operations, whether it is dataOps, dataOps, or even finOps, are required to deliver.

We learned how to combine development and management in order to extract efficiency. Machine learning and data operations have the same principles. From a technology point of view, the common factor is automation and continuous reusability of the processes to make that operation efficient. If you look at it like a diagram of three different operations, you'll see that they pivoted around automation and reusability with agility.