CDO Battlescars Takeaways: Creating a Data Strategy & Self-Service Data Platform in FinTech
In this episode of CDO Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Keyur Desai, CDO of TD Ameritrade. They discuss battlescars in two areas: Building a Data Strategy and Pervasive Self-Service Analytics Platforms.
Keyur is a data executive with over 30 years of experience managing and monetizing data and analytics. He has created data-driven organizations and driven enterprise-wide data strategies in areas including data literacy, modern data governance, machine learning and data science, pervasive self-service analytics, and several other areas.
He has experience across multiple industries including insurance, technology, healthcare, and retail. Keyur shares some really valuable lessons based on his extensive experience. Here are the key talking points from their chat.
Building a Data Strategy
The Problem: Disconnect Between Business Goals & Technical Infrastructure
- A data analytics strategy is never singularly built by a data analytics organization. It is absolutely co-created between the business and the data analytics organization.
- To the business, a data and analytics strategy is a set of data initiatives and analytics initiatives that will be brought together to help them achieve their business outcomes.
- However, building your data analytics initiatives based on desired business outcomes doesn’t guarantee that your technical infrastructure would be neatly built as well.
The Solution 1: A Data Economist
To solve this problem, Keyur felt that there was a need for a new role, called a data economist.
- The data economist’s job is to create a mathematical model of the outcome that they’re trying to achieve with the business and backtrack that into attributes of data, the analytics system, and the technical system.
- Through these mathematical models, you can estimate not only whether you’re going to be able to meet the business objectives or the business outcomes, but also model out the impact within the company, measured in earnings per share.
- You can use this model to propose a fact-based plan and engage with cross-functional executives in a conversation about why you’re proposing it that way.
The Solution 2: Data Literacy
Keyur also stressed the importance of establishing data literacy on the business side.
- A lot of companies will try to create a data literacy program that is overarching across the entire company. However, Keyur has found a lot of success in segmenting the business user base, as well as the technical user base, based on the types of capabilities each segment will need when it comes to data and analytics.
- A fluency program needs to enable the right segment to translate what they’re seeing from the tool at hand to an insight, and then translate that insight into some kind of implication.
- Literacy is not just about understanding the data; it’s also about practices to keep it safe and private, as well as being able to effectively tell a story with the data.
- Establishing data literacy across the business allows them to determine what types of outcomes are even possible with data and to begin to figure out which outcomes they want to chase.
Pervasive Self-Service Analytics Platforms
The Role of the Self-Service Analytics Team
- The self-service analytics team’s role has shifted from creating reporting assets to enabling data fluency across the organization and watching what data sets are being used by whom to solve what types of problems.
- There is a balancing act between wanting to provide self-service to everybody while, at the same time, making sure that everyone is doing it in a secure way that does not open up a risk for the corporation.
- It comes down to making sure that you’ve got a corporate-wide access framework where all subsystems that have data of any sort have the potential to store, move, or, share the data.
Data Prep Environments
Up to now, the missing link to developing an integrated, end-to-end self-service model was a self-service data preparation environment that is as easy to use as Excel. We’re now getting there!
- Data prep environments now allow non-technical business users to be able to get past some of the big bottlenecks they had in the past, like lacking the technical skill to clean up the data.
- The AI running in the background of data prep environments, combined with what the users around you are doing, is now smart enough to basically propose to you some of the cleaning actions you should be able to perform.
- Through the data prep environment, you can get dashboards on what data is being used for what purposes, or what kinds of metrics are being created, across the organization.
The Role of a Business Leader in the Self-Service World
This new self-service world is one that revolutionizes how we go about sharing data and even accelerates the sharing of data.
- With the unified access framework, you can now ensure that people are not only sharing, but are seeing the things they should. More importantly, business intelligence leaders can now watch and see what people are doing and ensure that everything is going smoothly.
- All leaders must be aligned around the concept of respect. Respect allows for a team to get to a level of interaction where they can easily bounce ideas off of each other. This breeds innovation.
- A leader needs to ensure that they trust the people they hire on their team and their colleagues enough to be able to give them enough autonomy to get the job done.
- In addition to laying out the end goal, leaders must also lay out the intermediate milestones required to reach that goal.
- Lastly, a leader should be able to see how previous leaders have failed and be able to now have a sense of purpose around how the new approaches will actually create business value.
If you’re interested in any of the topics discussed here, Sandeep and Keyur talked about even more in the full podcast. Be sure to check out the full episode and the rest of the CDO Battlescars podcast series!