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Maximize data outcomes by investing in people and systems


Sundar: In my experience, 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. So, it used to be a standalone process level, but for any effective data governance there, it should be a holistic process. It should be done right from the source of the data all the way to the consumption of feedback. That is one of the key best practices that we recommend to all of our customers. Also, data governance is a continuous process. It is not that, “Okay. I looked at the requirements of the data today,” whether it is regulatory requirement or the consumption requirements, “And I devised a plan for that and I can take risk now.” No.

So the data governance is a continuous process. The requirements of data continuously change. The usage of the data continuously changes. Regulations are continuously changing. So the data governance process and revisiting that is also very important and a complete understanding of what is happening, what has changed, why it is changed, when it has changed and keeping a record of that is also very important. That’s why the data governance framework should have a holistic process. It’s not a siloed process and it should be continuously revisited, and it is continuously tracked as well.

Laurel: And as you mentioned earlier, people are definitely part of this process and strategy as well. How do you think about data literacy as a critical skill that everyone needs to have across the organization outside of the tech teams? How should executives start thinking about preparing and ensuring everyone has those right skills to consume data?

Sundar: So, data is the “new oil” that is being fed everywhere. If data is a new oil, the understanding of how to use it, where to use that data becomes very, very crucial. How to use it and where to use it forms the major part of data literacy in any organization. Also, if we have to use any given data, then we should also know where the data is available. So, data literacy is addressed at two levels. One, about providing the information on 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. And the second one is, especially in today’s world, the data also has many constraints. It is very critical and it has a lot of sensitive information. The line between the sensitive information and the data that can be consumed easily is very thin in today’s world.

If that is the case, then the literacy of what data that we are processing and how sensitive it is, what we want to use with that, that literacy of that information is also very critical. So when the executives plan for data literacy programs in their organizations, it is also important to make sure that it’s not only about the data usage, but also what is the usage of the data and what is the outcome of the data? So, that’s why data literacy and the investment of data literacy on people becomes very critical. End of the day, the people are the ones who design the systems and who develop the systems that consume the data, so the right investment on literacy is paramount in that aspect.

Laurel: So, those are very important parts about data literacy, especially across the entire organization, but we’ve also seen that another part of digital transformation is streamlining and maximizing investments in operations across business units. For example, years ago, tech teams did this by combining software development and operations to create devOps, which allowed for more agile and data-focused 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 create MLOps, data to create dataOps, and finance to create finOps, so finance and operations. As a whole, these can be bundled into one single term called XOps. It’s an interesting way to take various parts of the business and bring it all together under an umbrella of operations. What value can XOps bring to an organization as a whole?

Sundar: Yes, as you rightly said, Laurel, XOps 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. Having said that, all the three operations, for example, that you have mentioned, whether it is devOps, dataOps, MLOps, or even finOps, the fourth one, everywhere, the common denominator’s operations and the requirement for that operations is to deliver value in a most efficient way.

So what we learned from devOps is managing versus developing a product, how to combine them and extract that efficiency. The same principles are taken into machine learning operations and data operations. Again, from the technology perspective, the common factor there is automation and continuous reusability of the processes to make that entire operation efficient. That’s why Gartner has combined all three and they call it XOps, so you can look at it like a Venn diagram of three different operations, which pivoted around automation and reusability with agility.



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