We all know — “data is the new currency” driving, transforming, and reshaping the industries globally.
But, the one sector that has significantly evolved and witnessed the digitization of their data (earlier stored in legacy IT systems) is the financial services industry, including investment banks, insurance companies, brokerage firms, etc.
According to McKinsey, “rapidly accelerating technology advances, the recognized value of data, and increasing data literacy are changing what it means to be data-driven.”
Their study about the data-driven enterprise of 2025 predicts, “by 2025, smart workflows and seamless interactions among humans and machines will likely be as standard as the corporate balance sheet, and most employees will use data to optimize nearly every aspect of their work.”
But, to make the most of your data and make smarter decisions, you need a data strategy/framework in place.
This article will give you a detailed outlook of a good data strategy, including its definition, example, and the five essential components of a data strategy for the financial services industry.
What is a Data Strategy?
Data strategy, often considered a technical practice for managing enterprise information models, is a roadmap for data usage in an organization to achieve a set of short-term, long-term goals and objectives.
However, a modern and comprehensive data strategy not only defines data and its uses but also addresses policies, processes, people, and technology that would support the business-critical objectives set in stone. A data strategy can be implemented successfully into your organization’s existing framework if it covers all the use cases and requirements of the business.
Moreover, a sound data strategy builds a foundation for all your data practices and helps attain a better data-driven culture across the organization.
TIP: When outlining a data strategy for your organization, make sure that it takes the human element in the loop and does not comprise only technical processes for managing and analyzing the data.
Let’s talk about a typical data strategy example for a better understanding:
A bank undergoing a digital transformation has the objective of keeping low and optimal data storage costs. To meet such a goal, the bank needs to come up with a strategy that clearly defines cost-efficient data storage options or services, including:
- best practices for cost optimization,
- metrics to track the data storage expenses and key performance indicators (KPIs) to measure the total cost of ownership (TCO),
- processes involved in collecting and storing the data
However, solving the enterprise data problems and challenges takes more than just a guiding plan of action. Organization leaders should perform a thorough analysis of their business requirements & end-goals and map them with the essential components of data strategy and management.
The following are the five key components required to build a robust data strategy framework:
5 Key Components of Data Strategy for Financial Services Industry
1) Identify Data Requirements
Evaluate what data your business needs and identify the process of sourcing and gathering that data. Your data must address specific business use cases and help drive value to the organization. Therefore, start recognizing and documenting the key elements listed below to map the kind of data you need:
- Roadblocks to certain IT or technical projects that the data will solve
- Stakeholders and subject matter experts (SMEs) who will process, share and maintain the data
- Department-specific activities tied to the data-driven goals.
This will help you draw a clear picture of what data will empower the company and its workforce.
Once you have finalized the business and data requirements, the next step is the collection of the necessary data sets that would help you solve the existing data challenges. The data gathered from internal/in-house and external sources can be both structured and unstructured.
2) Integrate Technology into Data Landscape
Technology has an important role to play in the exercise of creating a data strategy as it complements and supports the data management framework.
First, assess your data landscape and outline an efficient, flexible data architecture, including software and hardware required for sourcing/collecting, storing, analyzing, and processing data.
To leverage and harness the power of data, organizations should integrate the right set of tools and technology. Deploy data collection tools, data scraping APIs, and data storage services.
The next step is to implement a scalable data lake platform for diverse operations on data.
TIP: While integrating technology into your data strategy, consider the upstream systems that are capable of supporting data requirements as well as downstream systems for receiving financial information in a new layout.
3) Embrace Analytics to Turn Data into Insights
A good, robust data strategy holds the capability to provide recommendations and deliver actionable insights with the application of analytics techniques. Data visualization is the key to communicating business-critical insights from data.
Although the financial services industry is embracing the digital revolution, many finance companies still rely on legacy business intelligence (BI) tools and traditional ways of analyzing data (e.g., Excel reports). Such tedious, manual processes create a bottleneck in the execution of the strategy.
To turn your data into meaningful insights, you need to incorporate an intelligent data visualization platform that:
- Allows you to spot data trends and outliers easily,
- Enables story-telling via metric-based dashboards,
- Encourages democratization of data, and
- Provides data granularity for in-depth analysis
TIP: A data visualization tool should not only make the data look good but also simplify it for the users, making it easier to understand/interpret.
4) Data Governance
Building strong governance and reporting model will transform your data from underlying data architecture that exists piecemeal across the organization into a secure, actionable, reusable, and single source of truth.
Governance of data enables data sharing and analytics practices at an enterprise level. A data governance model will ensure that the right people and relevant data owners have access to the right data sets. It also allows you to maintain the data lineage that tells you about the data origins and its transformation journey since the origination.
The governance program also helps address some crucial aspects of a data strategy. For example, how will the organization ensure data quality or handle issues around security, privacy, accessibility, ownership, and ethics?
TIP: Build a data dictionary for your organization that contains all the policies, measures, and dimensions of data usage. It will serve as the living document for the end-users.
5) People and Processes for Data Management
Defining the people and processes involved in data strategy and management is equally important as all the components mentioned above.
Map out the roles and responsibilities of the data governance and management team, including executive leaders, business stakeholders, data owners and architects, data stewards, and so on. Besides, selecting the right set of people and an efficient team is what will drive your data framework effectively.
So, involve relevant business groups, such as the financial planning and analysis (FP&A) team, corporate & local accounting departments, and reporting leads.
And, it doesn’t end there.
Ask yourself the bigger question: “How?”
How will you source, provision, consolidate, store, analyze, and share data? This needs to be answered by creating policies, implementing processes and guiding principles, and defining governance metrics across the company.
How to Start Implementing a Robust Data Strategy Framework for Financial Services Industry?
Now, what next?
Wherever you are in your data strategy journey, Data-Driven has the right solutions for you.
Here at Data-driven, we deliver innovative data & AI solutions to help the financial services industry build a robust data strategy.
For example, with our Data Governance and Assessment solution, we aim to help you understand your current data state and create an actionable data strategy implementation plan.
Learn more about this solution here.
Remember, your data strategy will be of no use if the data itself is not stored, secured, and used properly.
Therefore, it is crucial to have a framework in place that ensures your data is well-maintained and ready for use. Bringing a cloud-native AI data platform into play will help achieve this.
- Enterprise Governance on Data Lake with Unity Catalog & Databricks Implementation with R Jobs Migration Use Case - July 20, 2022
- Data-Driven recognised as the 2022 Microsoft Partner of the Year Finalist - July 6, 2022
- Azure Governance — The Importance of Azure Role-Based Access Control (RBAC) - May 25, 2022