Without Data, You're Just Another Person with an Opinion!
Vivek Awasthi, Chief Information Officer, Currencies Direct
How has the data analytics marketplace evolved over the course of the last two decades?
From my perspective, data analytics has taken the role of data fanaticism, providing companies with the capability of radical decision-making. Back in time, data was just a derivative providing post-event insights into the business, while proactive decisions were still taken by biases based on experience. Over the decades, data has evolved into a precursor of decision making. Although every company has initiated the use of data to power progressive business decisions, the caveat is that different companies are at unalike levels of maturity on how to use the data.
What are some of the hurdles or difficulties in the data analytics space that some of your clients face? How do you think they can overcome the same?
Although there is an abundance of data that can be used to make decisions, one of the problems that all financial businesses face is to comply with the regulations of data-handling. Data orchestration is difficult, and almost every organization with a lot of big data encounters trouble in feature engineering the data to get its actual value. A large proportion of these problems are solved if the organizations understand the collection of data and how to drive useful insights from it. Besides assigning people to look at the data insights, orchestration has become a significant focus of the organization in this day and age. Moving under the umbrella of machine learning, companies are driving value from engineered data to acquire insights for either cost reductions or revenue upliftment. If your organization is starting to nail all of these, that is where you can start calling yourself a data-driven organization.
What are some of the newer technologies infusing a lot of vigor into the data analytics space and helping you as a CIO in your business objective?
For me, one of the key technologies driving the whole data usage process today is machine learning that facilitates the self-government of data and provides the organization with invaluable insights. Theoretically, it sounds like a cakewalk, but its practical implementation is what holds the main weight. The problem is not making organizations use modern technologies in several ways to derive insights but to make them believe in the insights they are generating through machine learning and data orchestration. So the crux of the solution is not to use the automation technologies for data usage but to change the organization’s mindset to believe in the technologies. I think a lot of organizations have gone ahead and embraced this, and we are already starting to see them pulling away from the rest of the competition.
When we talk about data and its transition from legacy infrastructure to the cloud, we see a lot of organizations still using the legacy infrastructure, and that has emerged as one of the challenges in the data landscape. So what is your take on how organizations should embrace the cloud transformation?
I believe organizations need to be careful around what they are using the cloud for and how they are sufficiently protecting their assets by being in the cloud. With the innumerable offerings of the cloud, most companies have started embracing it. Though the cloud means different things to different people, financial institutions can exploit the public cloud to acquire hyper-scalability and hyper-visibility like never before. Linking up around-the-globe data centers of an organization is no more a struggle with the cloud infrastructure. Notwithstanding that large financial institutions were the first ones to go into virtualization across disparate data centers, they are at present slightly wary of the new technologies revolving around the use of public cloud services. The accessibility of cloud technology has reduced its implementation and failure costs while opening the door for experimentations.
Does any collaboration come to your mind where data analytics has been the secret sauce for you, in terms of overcoming some of your organizational objectives, while interacting with the clients?
With a strong focus on data analytics and data fanaticism, the two things that we do to overcome the organizational challenges are—first, we use the data to make decisions in a lot of manual forecasting analytics, and second, we utilize it for self-governance decisions. While the first is mainstream, even today, self-governance is performed only where the cost of failure is slightly lower. As an organization, we have some collaboration with industries that can help us predict adverse customer behavior, paving the way to radical decision making. With the kinds and modus of fraud changing and shifting with time, our fraud prediction engine, from a data standpoint, is our best collaborative use case to date. While some organizations are still using the traditional rule-based tangents to predict fraud, almost every other is using augmented intelligence or artificial intelligence to circumvent fraud and accumulate the information necessary to make clear decisions.
Can you please add the cherry on top by giving a piece of advice to some of the newer organizations, startups, and entrepreneurs in the data analytics space?
There are two things that spring to my mind quite immediately. First, as you use technology to make decisions to run organizations, I believe you have to get into a mindset of continued collaboration because technology cannot run the business alone.
The second consequential need is to prove value creation and not work on the basis of a hypothesis, to show the organization how the correct data usage with the right technological expertise can help create value and drive the best business decisions. One of the biggest hurdles that people in my position face is to harmonize the perfect organizational culture before rolling out solutions as a financial technology organization. You can have hyper-relevant people in a data-focused team, but if they are totally removed from the rest of the organization, they will never be able to harness the true worth of the data’s insights.
For me, one of the key technologies driving the whole data usage process today is machine learning that facilitates the self-government of data and provides the organization with invaluable insights. Theoretically, it sounds like a cakewalk, but its practical implementation is what holds the main weight. The problem is not making organizations use modern technologies in several ways to derive insights but to make them believe in the insights they are generating through machine learning and data orchestration. So the crux of the solution is not to use the automation technologies for data usage but to change the organization’s mindset to believe in the technologies. I think a lot of organizations have gone ahead and embraced this, and we are already starting to see them pulling away from the rest of the competition.
When we talk about data and its transition from legacy infrastructure to the cloud, we see a lot of organizations still using the legacy infrastructure, and that has emerged as one of the challenges in the data landscape. So what is your take on how organizations should embrace the cloud transformation?
I believe organizations need to be careful around what they are using the cloud for and how they are sufficiently protecting their assets by being in the cloud. With the innumerable offerings of the cloud, most companies have started embracing it. Though the cloud means different things to different people, financial institutions can exploit the public cloud to acquire hyper-scalability and hyper-visibility like never before. Linking up around-the-globe data centers of an organization is no more a struggle with the cloud infrastructure. Notwithstanding that large financial institutions were the first ones to go into virtualization across disparate data centers, they are at present slightly wary of the new technologies revolving around the use of public cloud services. The accessibility of cloud technology has reduced its implementation and failure costs while opening the door for experimentations.
Does any collaboration come to your mind where data analytics has been the secret sauce for you, in terms of overcoming some of your organizational objectives, while interacting with the clients?
With a strong focus on data analytics and data fanaticism, the two things that we do to overcome the organizational challenges are—first, we use the data to make decisions in a lot of manual forecasting analytics, and second, we utilize it for self-governance decisions. While the first is mainstream, even today, self-governance is performed only where the cost of failure is slightly lower. As an organization, we have some collaboration with industries that can help us predict adverse customer behavior, paving the way to radical decision making. With the kinds and modus of fraud changing and shifting with time, our fraud prediction engine, from a data standpoint, is our best collaborative use case to date. While some organizations are still using the traditional rule-based tangents to predict fraud, almost every other is using augmented intelligence or artificial intelligence to circumvent fraud and accumulate the information necessary to make clear decisions.
Can you please add the cherry on top by giving a piece of advice to some of the newer organizations, startups, and entrepreneurs in the data analytics space?
There are two things that spring to my mind quite immediately. First, as you use technology to make decisions to run organizations, I believe you have to get into a mindset of continued collaboration because technology cannot run the business alone.
The second consequential need is to prove value creation and not work on the basis of a hypothesis, to show the organization how the correct data usage with the right technological expertise can help create value and drive the best business decisions. One of the biggest hurdles that people in my position face is to harmonize the perfect organizational culture before rolling out solutions as a financial technology organization. You can have hyper-relevant people in a data-focused team, but if they are totally removed from the rest of the organization, they will never be able to harness the true worth of the data’s insights.
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