Sundar: In my experience, formerly an architect and manager and providing consulting services to many of my clients, data governance is being looked at primarily to service regulatory requirements. determined in the past. So it used to be an independent process level, but for any effective data governance there, it has to be a whole process. It has to be done right from the data source to the consumption of the response. It’s one of the main best practices we recommend to all of our customers. In addition, data governance is an ongoing process. It’s not, “Okay. I looked at the data requirements today,” whether it’s a regulatory requirement or a consumer claim, “And I’ve come up with a plan for that and I can take the risk now.” No.
So data governance is an ongoing process. Data requirements are constantly changing. Data usage is constantly changing. Regulations are constantly changing. So the data governance process and that review is also very important and full understanding of what is happening, what has changed, why it has been changed, when it has changed and It’s also important to take notes. That’s why a data governance framework should have an overall process. It is not a shutdown process and it must be continuously reviewed, and it is also continuously monitored.
Laurel: And as you mentioned earlier, people are definitely part of this process and strategy as well. What do you think about data literacy as an important skill that everyone needs in the organization beyond tech teams? How should executives start thinking about preparing and ensuring everyone has the right skills to use data?
Sundar: So data is the “new oil” that is being made available everywhere. If data is the new oil, understanding how to use it, where to use that data becomes very, very important. How it is used and where it is used form the main part of knowledge about data in any organization. Also, if we have to use any given data, then we should also know where the data is available. So data insights are addressed at two levels. Firstly, provide information about what data is available, how well that data is available, how to access that data, how to process that data. And the second thing is, especially in today’s world, data has many constraints too. It is very important and contains a lot of sensitive information. The line between sensitive information and data that can be easily consumed is very thin in today’s world.
If that is the case, it is also important to have an understanding of the data we are dealing with and how sensitive that data is, what we want to use it for, and understanding that information. So, as executives plan data literacy programs in their organizations, it’s important to ensure that not only data usage, but data usage What is the data and what is the result of the data? So that’s why data literacy and the investment of data insights in people become so important. Ultimately, humans are the system designers and developers of data consuming systems, so proper investment in literacy is paramount in that respect.
Laurel: So those are very important parts of data literacy, especially across the organization, but we also find that another part of digital transformation is streamlining and maximizing investment. into business units. For example, years ago, tech teams did this by combining software development and operations to create devOps, enabling more agile and data-centric ways of working. The research firm, Gartner, argues that this philosophy can also be applied to other areas of the business, including artificial intelligence and machine learning to generate MLOps, data to create dataOps, and finance to create MLOps. finOps creation, finance and operations. Collectively, they can be lumped into a single term known as XOps. It’s a fun way to take different parts of the business and bring it all together under one operating umbrella. What value can XOps bring to the entire organization?
Sundar: Well, as you are right, Laurel, XOps is an umbrella that brings a variety of activities that drive innovation through technology to address business requirements to take business to the next level. Having said that, all three operations, for example, that you mentioned, whether it’s devOps, dataOps, MLOps or even finOps, the fourth, everywhere, operations of the common denominator and the requirement for such activities to deliver value in the most efficient manner.
So what we learned from devOps is managing versus developing a product, how to combine them and exploit that efficiency. Similar principles are applied to machine learning and data operations. Again, from a technology perspective, the common factor is automation and continuous re-usability of the processes to make the whole operation efficient. That’s why Gartner combined all three and they call it XOps, so you can think of it like a Venn diagram of three different operations, revolving around automation and reusability. quickly.
