Master Data Management
Data-Driven Product Strategy: Turning Insights into Actionable Plans for Business Growth
Today, many organizations hold massive amounts of data, but they struggle to interpret and extract value from this data. It is essential to use data to their advantage to achieve organizational goals, design product roadmaps, and make high-impact decisions.
It’s quite common that even the best organizations follow traditional methods when they approach data – with a project mindset. Let’s look at how the project mindset works. Every time a business function faces a challenge and wants to solve it with the help of data, the organization starts a project. A team is formed with a series of tasks to obtain the data, cleanse and prepare it, then analyze it for the specific challenge. And each time a new business problem emerges, it follows a similar approach – “obtain, prepare and analyze data” for a specific need. The project mindset may sound familiar, and most organizations operate in a similar manner. This way of thinking fosters a restricted view of data and loses the ability to identify its potential in bringing success to the organization.
A change in approach
As a first step we must redefine our relationship with data and produce insights that are easily accessible to users across the organization to make better decisions, rather than going by their instinct, tradition, or gut feeling. To do that, we should conceptualize and approach data with a product mindset, which will enable companies to gain value from data quickly and continue to gain more value in the future.
Viewing data through a product development lens is a very conscious way of viewing the data to develop solutions to address unmet needs across organization. When you approach the data with product-centric view, the focus should be to ask relevant questions such as: What are my challenges? What are my business goals? Can data be an answer to achieving those goals and solving these problems? Once you start that, then you could have a strategic view to meet the evolving needs of customers and enable the development of sophisticated solutions to drive business value.
In this model, the role of the data team is very crucial as they provide data to the company to facilitate good decision making. The team should be led by a product owner who is responsible to apply classic product thinking to data and work closely with the team, clean and structure the data that will allow various functions across the organization to access it. The product owners should encourage the business to effectively use this data and draw insights to solve an array of business problems. This approach can do away with much legwork of dismantling organization silos and bring numerous benefits that banking functionalities can take advantage of to increase the value of financial services
This is where data mesh could play a vital role. The data mesh approach is a paradigm shift to thinking about data as a product. Data mesh solves the issue by making data more visible and building stronger stewardship among domains. The idea is to make data more accessible and available to business users by directly connecting data owners, data producers and data consumers. It aims to improve business outcomes of data-centric solutions as well as drive adoption of modern data architectures.
Some advantages of the data mesh architecture include:
- Product thinking gets embedded across the organization
- Better control over your data, leading to faster decision making
- Easier data discovery and accessibility
- Greater scalability of data systems with self-governing data domains and teams
- Superior data quality: The team creating data is responsible of managing and extracting value from it
- Interoperability across data domains
- Better regulatory compliance and data security
JPMorgan Chase created a data mesh to enhance its fraud detection. They took the route of appointing a data product owner who was in charge of leading the team to execute the process of extracting debit and credit card spending patterns along with usage details and other relevant information into a data product that enabled the bank to reduce fraud costs without compromising on data governance. *
The data mesh is an incredibly powerful approach to getting the maximum business value out of your data. Data mesh, together with the approach of data product owners, is not the solution to all data problems. However, it will clear the bottlenecks created by the conventional data platforms.