Unlocking the value of data is a challenge for many leaders these days. From hype cycles to tools, organizations can work with their data in a multitude of ways. One such principle that’s been very effective is the concept of product thinking. Product managers, product analysts, agile concepts, and product-led processes are a huge component of today’s organizations.
In this post, we will explore how data product thinking can be applied in financial services.
Understanding Data Product Thinking:
Product thinking, like design thinking, is a set of cognitive processes and methodologies that allow an individual to journey from a problem space to a solution space. Meaning it’s a framework for solving problems. Traditionally applied to physical and digital tools, the term has recently been used to describe a new approach to data. Data product thinking takes the concepts of product thinking and applies them to data. One such framework for this is the data mesh framework, which looks at data as a solution to address various challenges.
In general, the product thinking process follows three steps:
1. Problem – Describe a pain point and understand why a particular issue exists.
2. Opportunity – Define the approach and size of what a product solution could do.
3. Solution – Solve the problem and produce an ROI.
What does this look like with data? Think of your traditional service-driven data function in an organization. We’ve all seen BI teams completely overwhelmed by request volumes and delivering reports and dashboards to fit the service requests. When we consider shifting the problem from service to product, the role of the team moves towards creating discoverable data assets that allow consumers of the product to use them as they need, without a centralized data analyst. A `”data product”` is, therefore, data that has been transformed from raw extracts into a usable form that can be sold or distributed to others. This way of thinking is complicated because it’s intangible in many ways.
Examples of Some Data Products
A few examples of good data products are:
1. Google Maps
2. Google Search
3. Experian
4. ZoomInfo
Data Products in Financial Services
Leaders in financial services are always looking for ideas on leveraging, managing, controlling, and exploiting data within systems. Financial services firms face high regulation, complex models, and talent gaps to try and leverage their data. Product thinking is effective at letting the business drive data decisions and resources, but at the same time, it allows leaders to focus on superordinate goals for the organization.
Data products in financial services include things such as forecasting, risk modeling, automation, and regulation. One such example is that many institutions will create consumer analytics data products. These models help to understand consumers and how they interact with their community. A data product or team focused on creating a customer analytics product and concentrating on the organization’s needs related to those analytics.
Fraud detection and prevention is another common area where data product thinking is very effective. Data products that predict or capture fraud can be used to help customers. These products may be as simple as a data set of all daily transactions with tagging on the propensity for the transaction being fraud to an API with a fraud scoring algorithm that can give you results in real-time.
Data sharing products, like credit bureaus, can help customers save time by providing faster access to services and onboarding. A data product that can share data between solutions, which allows an online applicant to spend less time filling out an application. The time reduction helps to capture more new accounts.
Financial institutions on the data product journey build their teams in different ways. There’s no right way, but teams generally need engineers, analysts, and scientists (in some cases). Typically, teams running a data product are between 3-5 people.
Technologies and Tools
There are several tools in the market that can help you to scale your data product development. Technology such as Snowflake has built-in data marketplaces and data-sharing capabilities that allow you to share and distribute data securely. Because of this, Snowflake is one of the primary platforms that enable you to create data products and use them internally or externally. As a data provider on Snowflake’s data exchange, you can package products for other Snowflake users to consume.
Many financial institutions use Snowflake, DataBricks, or similar technology to build data products and create a Data Mesh. Data Mesh helps companies move away from traditional monolithic and slow architectures and move towards ones better suited for the needs of today’s businesses.
Conclusion
Data product thinking offers a transformative framework for organizations, particularly in the financial services sector, striving to extract maximum value from their data resources. Rooted in problem-solving methodologies, this approach guides a structured transition from identifying challenges to delivering tangible solutions. It challenges the conventional service-oriented data functions by reshaping raw data into discoverable assets, reducing dependence on centralized data analysts.
Across industries, successful data products like Google Maps and Experian exemplify the adaptability of this approach. In financial services, where complexity and regulatory constraints abound, data product thinking empowers businesses to steer data-driven decisions while focusing on overarching goals. It finds application in various facets, from customer analytics to fraud prevention, streamlining processes to enhance efficiency and the customer experience.
Modern technology platforms such as Snowflake and DataBricks facilitate data product development, enabling organizations to embrace Data Mesh principles and depart from outdated architectures. In summary, data product thinking is a potent tool for financial institutions to harness their data assets effectively. By adopting this approach and leveraging contemporary technologies, these institutions can navigate the complexities of their industry and flourish in a data-centric era. Recognizing data as a strategic asset is the key to achieving immediate goals and long-term sustainable growth.
The views and opinions expressed in this blog are those of the authors and do not necessarily reflect the official policy or position of the company. The predictions and forecasts made on this blog are based on the authors’ best estimates and assumptions and are subject to change without notice. The accuracy of the predictions and forecasts cannot be guaranteed. The authors will not be held responsible for any errors or omissions, or for any actions taken based on the information provided on this blog.
By using this blog, you acknowledge and agree to the terms of this disclaimer.
References
– https://mitsloan.mit.edu/ideas-made-to-matter/how-to-build-a-data-analytics-dream-team
– https://www.amazon.com/gp/product/B0BRVRT5TV/ref=kinw_myk_ro_title
– https://atlan.com/data-as-a-product/
– https://www.uxpin.com/studio/blog/product-thinking/