Maximize data outcomes by investing in people and systems
In any enterprise, digital transformation is not only a technology transformation but enables business transformation itself, driving new products, solutions and innovations. A digital transformation must be successful without compromising on data strategy. This requires careful investment in both people and systems.
“To achieve that goal, availability of good data, of the right data, and availability of that to the right people and systems is very, very critical. Sundar Shanmugam, chief architect for data- and AI services at Kyndryl says that this is the data strategy for any enterprise.
To get the most out your digital transformation investments, you need to evaluate and optimize agility within an enterprise in order to drive actionable insights. A strong data governance framework is also important for maintaining high-quality data. Data governance is often a function of regulatory requirements. He adds that effective data governance is holistic. Data usage, regulations, as well as the data themselves, are constantly changing within an enterprise. This makes data governance a continuous process.
Although tech teams often dictate how data should be used and managed, Shanmugam believes that everyone in an enterprise, including decision-makers and leaders, should be data literate.
“The people who design and develop the systems that consume data are the most important part of any system, so it is crucial to make the right investments in literacy,” Shanmugam says.
Maximizing investments across business units is another key component of digital transformation. Software development and operations are combined to create devOps. AI and machine learning are used to create MLOps. Finance and operations are used to create finOps. These merge IT disciplines with business operations. Shanmugam says that XOps are all designed to maximize automation, reusability and agility.
“Necessity is the mother of innovation,” Shanmugam says. “If we keep that data in a proper [condition], then it can expand our horizons not only internally but also for external requirements and use cases . This episode of Business Lab was produced in association with Kyndryl.
Top Trends in Data and Analytics for 2021, Gartner, February 16, 2021
EmTech Digital 2022 session, Business-ready data holds the key to AI democratization, presented by Kyndryl
Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. This session focuses on how to create a data strategy for your organization.
To be effective, data must reach beyond the tech teams to the hands of decision makers. Although it can be difficult for enterprises to finance data and machine-learning operations, there are clear opportunities.
Two words to help you. Maximizing data.
My guest is Sundar Shanmugam who is the chief architect for data- and AI services at Kyndryl. This podcast is sponsored and produced by Kyndryl.
Sundar Shanmugam: Hey Laurel.
Laurel: So, having a data strategy is critical to a company’s digital transformation, but it requires investment in people and systems. What are the best practices for getting the most from that investment?
Sundar: Digital transformation projects in any enterprise is not just a technology transformation. It facilitates and enables business transformation, regardless of industry. It drives innovation and new solutions. If this is true, then digital transformations must be able provide the insights that are actionable and drive the critical decisions that are required for the business. It should also happen on demand in today’s world. It is crucial that good data, the right data, and the availability of it to the right people and systems are available in order to achieve this goal. This is the data strategy for any business today.
When we consider it from this perspective, my recommendations would be to first look at the agility and adaptability of people and systems that can adapt to continuous change and have a strong foundation for technology and the mindset behavioral changes required to provide good insights. A strong governance and control system for new data is essential to ensure that it remains relevant and of high quality. Keep your data foundations clean, tidy, and free from errors. This makes your data strategy fresh and allows for new ideas. These are the best practices I recommend for data strategies that require the most investment.
Laurel: Great. Going back to the first, clean, usable data is one part of the story. It’s one of three main best practices. But metadata, the way data is described, is equally important. Can you please explain why metadata is so important in data strategy?
Sundar: So, we spoke about the data strategy and the data strategy is for long term and that should serve the business transformation and the innovations for the business itself. If that is required, it is important to record all details about the data, which we call metadata. The metadata contains information about the data and serves multiple purposes. One, the data changes because data is constantly changing in today’s world. It is important to know where your data originated and what changes it has undergone. This will allow you to make changes to the data as needed.
It is important to maintain consistent metadata throughout the data’s lifecycle, from the point at which it is created to the point it is consumed. This ensures that data information is consistent. This would allow users to use the data more easily and make it easier to find the right data. The metadata is an integral part of any data strategy, especially for those in emerging industries where data is crucial.
Laurel: Yeah, you’ve touched on some very important aspects here. Data governance is a critical aspect of any enterprise’s technology practices and processes. What are the best practices for leaders of companies looking to establish or rebuild a data governance structure?
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. It used to be a separate process, but data governance is now a holistic process. It should start at the source of data and end up at the point of consumption. This is one of the best practices we recommend to all our customers. Data governance is a continuous process. It is not as simple as “Okay.” I examined the requirements of data today,” whether it be regulatory requirement or consumption requirements. “And I devised an action plan for that and can now take the risk.” No.
Data governance is a continuous process. Data requirements change constantly. The data usage is constantly changing. Regulations are constantly changing. The data governance process and revisiting it are also very important. A complete understanding of what is occurring, what has changed, why, when, and how it has changed is also important. The data governance framework should be holistic. It is not a separate process. It should be constantly reviewed and tracked.
Laurel: And as you mentioned earlier, people are definitely part of this process and strategy as well. How can you view data literacy as a critical skill that all employees need to have, besides the tech teams? How can executives prepare and ensure everyone has the right skills to consume data?
Sundar: So, data is the “new oil” that is being fed everywhere. Data is a new oil. It is crucial to understand how to use it and where to use it. Data literacy is essential for any organization. It should be easy to find it and where it can be used. If we need to use any particular data, we must also know where it is located. Data literacy can be described in two ways. The first is about providing information about what data is available, how reliable that data is, how to access it, and how to process it. The second is that data has many limitations, especially in today’s modern world. It is extremely important and contains a lot of sensitive data. In today’s world, the line between sensitive information and data that can easily be consumed is very thin.
If this is the case, then literacy in data processing, how sensitive it is, and what we intend to do with that data is crucial. When executives plan for data literacy programs within their organizations, it’s important to ensure that it’s not just about data usage but also what the data is used for and the results. Data literacy and the investment in data literacy on individuals is crucial. The people who create the systems and the systems that consume data are ultimately responsible for the literacy of those individuals.
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. This was possible years ago when tech teams combined software development and operations to create “devOps”, which allowed for more agile and data-focused working methods. Gartner, a research firm, believes that this philosophy can be applied to other areas such as artificial intelligence and machine learning to make MLOps, data and finance to create FinOps. This will allow for both finance and operations. These can all be combined into one term, XOps. It’s a great way to bring together different parts of a business under one umbrella called operations. What is the value of XOps for an organization?
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. All the operations you mentioned, such as devOps and dataOps or MLOps or even finOps are all related to the common denominator. The requirement for those operations is to deliver the most value.
So what we learned from DevOps was how to manage versus develop a product, how they can be combined and how to extract that efficiency. Machine learning operations and data operations can be adapted from the same principles. From a technology perspective, automation and continuous reuse of the processes are the common factors that make an operation efficient. Gartner has merged all three, and called it XOps. This allows you to look at it as a Venn diagram with three operations that revolve around automation and reusability, while also allowing for agility.
To run all these things, especially with the data and machine learning, the data is continuously increasing, the volume is continuously increasing. The machine learning activities and resources required to process this data are also constantly increasing. This is why finOps is so important to manage all of these things. They are therefore all combined under one umbrella and work together to provide more value. This is the big difference that XOps makes to an organization. They are more agile and cost-effective for the data and EA operations they perform within their organizations.
Laurel: So how can XOps help give visibility to the success of cloud computing adoption? How can finance assist tech in making better decisions for the enterprise when they are in this type of relationship?
Sundar: As we have seen before, the finOps component of XOps is what makes the financial controls on the entire operations of data and machine learning more robust. To look at it from a different perspective, the adoption for cloud computing itself was primarily driven for cost advantage when it started 10, 15 years back with infrastructure as a service. If that is the main reason for cloud computing adoption, then it is imperative that you monitor the data consumption and the resource use by the machine learning processes.
FinOps monitors cloud computing to see the data being consumed, the models and even the extent of models decaying and how much they are consuming. They can see if they are able to achieve their goals of cloud computing adoption by combining all this information. The service of EA projects, and the results from those projects. This is how finOps (part of the XOps) helps to measure cloud computing’s success.
Laurel: So if XOps helps prove that success for cloud computing adoption, why would this be so critical for executives who are probably looking for that ROI, return on investment, for their extensive cloud computing investment?
Sundar: Yes, the cloud computing investment is part of the digital transformation and it should deliver the business outcomes in terms of the value from the innovation projects where the EA and data projects are primarily involved. The cloud computing investments are realized when the data and eventual EA projects realize the value. If the data quality is poor and we are unable to measure how the data, machine learning, and server function, as well as their resource consumption, it will be very difficult to map it to the results that result. The outcome of cloud computing depends on the finOps, the data and EA projects.
Laurel: So, when we think about data and XOps and access to data across the organization, we also need to really think about security as well. How can a holistic view of data and XOps help organizations keep their data secure?
Sundar: Yes, so that data fits into the EA and the eventual insights from dev, so the operations related to data to keep the data clean and usable also means that the data should be available to the right people in the right format for dev operations. XOps brings together data operations and machine-learning operations, allowing data to be closer to the place where it will be processed. It is possible to keep all the profiles for data consumption in one place. This makes data access and consumption much easier and more efficient. The XOps allows data to be more secure and accessible in a continuous fashion.
Laurel: You mentioned earlier that having a good data strategy helps with innovation and the adoption of emerging technologies. How do you see this happening in different companies? Is there any way to innovate?
Sundar: Oh, the innovation around data and EA in today’s world is everywhere. There are no boundaries to the innovation that is available to enterprises today. As an example, in the automotive industry, when the data strategy was being developed, it primarily focused on how to make it more useful and more relevant within the organization. The data can be expanded when it has new uses and requirements, such as the connected car. The data is no longer just for their own use. The data can be monetized by the automotive companies by being shared with others. One of our projects has allowed the insurance companies to obtain information about vehicle performance, how the driver uses the vehicle, and what the demographics are. This is the case of data that originated in the automotive industry. It is now being used by the insurance industry.
This innovation has been transferred from the automotive industry into the insurance industry. If the data strategy is correct and the data is in a good place, and if enterprise uses proper data management techniques, then innovations can and will continue to grow as time passes and the requirements change. We say that necessity is the mother and father of innovation. However, it is possible for necessity to change. That means that if the data is kept in a good [condition], it can expand the horizons not only internally but also for external requirements and use cases. The data we have here is not limited in its potential for innovation.
Laurel: It’s certainly very exciting, lots of opportunities with that innovation. How do you see the next three to five year? How will the data landscape change and how we work with data evolve?
Sundar: There are again, immense amounts of possibilities and opportunities there, especially if you think about three to five years. There are many technological developments like Meta. Meta, or augmented reality, is another example of the huge amount of data that will be generated. The use of this data and the insights that can be gained from it, as well as the potential to make significant changes in different industries’ functioning, is also very high. This is the technology side. We also consider how technology impacts people and the environment, such as sustainability. Data and insights are key to sustainability. It’s not only about measuring sustainability, but also forecasting and devising new strategies for sustainability.
In this way, I believe that the data will grow a lot. We are now seeing the data mesh. This is a technology or technological principle where the data ownership lies with the business units. They know the data better, they can share it with other based on their own regulations, and consumers can also use them more efficiently. Data will be more powerful. They will have a lot of fun coming up with new insights over the next three to five year. The metaverse and augmented realities on the technology side, sustainability in the world environment and social side, and data mesh on the data architecture principle site are all examples. These are the three major developments I see in the next three to five year.
Laurel: Excellent. Sundar, thank-you so much for being here today at the Business Lab.
Sundar: It’s really great speaking to you, Laurel. Have a wonderful day.
Laurel Ruma: That was Sundar Shanmugam, the Chief Architect for Data and AI Services at Kyndryl, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review overlooking the Charles River.
That’s it for this episode. I’m Laurel Ruma, your host. I am the Director of Insights at MIT Technology Review’s Custom Publishing Division. We were founded in 1899 at Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
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