Welcome back! We discussed ‘Why Banks and Financial Services Institutions Need a Robust Data Modernization Strategy?’ in our last blog post. In this article, we will evaluate the key elements of building this robust data modernization strategy – understanding how a systematic data modernization approach can help organizations extend their legacy assets, build modern cloud data platforms for big data, and harness technologies like cloud, artificial intelligence (AI) and data analytics for data-driven transformation. We will also explore FinOps – the emerging cloud financial management discipline and cultural practice being adopted by a growing number of Global 2000 companies, for its critical role in aiding their data modernization initiatives of financial institutions. Let’s begin.
9 Key Elements of Building a Robust Data Modernization Strategy
1. Data Migration
Data migration is one of the most challenging steps in the data modernization journey. Once enterprises master this, they can consider half the battle won.
A robust data migration strategy ensures a seamless transition of clean data from an old to a new data platform, with minimal business disruption. Gartner predicts that 85% of enterprises to move to a cloud-first strategy by 2025. This highlights the critical role of cloud migration in shaping modern businesses.
More than 50% of enterprises are expected to use industry cloud platforms to accelerate their business initiatives by 2028 according to Gartner’s report. Cloud data platforms offer a host of advantages driven by cutting-edge technology and seamless integration with existing systems.
By moving to the cloud, financial enterprises can quickly adapt to market changes and customer needs, and enhance security and compliance. The centralized data storage can help them leverage advanced data analytics to gain insights for better decision-making and support seamless collaboration between teams to innovate. This can empower financial enterprises to deliver exceptional customer experiences, improve operations, mitigate risks, and grow. With FinOps integrated into the overarching data strategy, organizations can further maximize returns from their cloud infrastructure.
2. Data Ingestion
Data ingestion is the process of automating the collection of raw data from disparate data sources and persisting the data in a common targeted data repository.
A well-architected data ingestion strategy ensures:
- A good understanding of the system of records, their entities and schema.
- A robust data provisioning process, clearly defining data entitlements for inbound and outbound data.
- A strong process and tech-driven framework to seamlessly onboard new data sources.
This solid data ingestion strategy becomes the key to a strong enterprise data strategy, helping in the consolidation of all data into an enterprise data lake. It can also help in getting accurate copies of source data for future processing.
3. Data Cleansing and Transformation
Data becomes the most valuable business asset only if it is complete, correct, and consistent. To ensure access to high-quality data, enterprises need a robust data cleansing and data transformation process. A good data transformation process also enables data enrichment along with offering the following benefits:
- Removes data noise
- Improves data integrity
- Ensures completeness, correctness, and consistency of data
- Enhances data democratization across users.
If you are looking to embrace AI/ ML strategy to improve your business performance, data cleansing and transformation become the foundation of your digital transformation strategy.
4. Data Rules
A rules-based approach to validating data is an integral part of data quality management. A data rule defines the logic that you want to apply to the data during a task and this systematic approach ensures completeness, correctness, and consistency of data. A data rule is applied to different data elements without a database structure and is typically enforced through database constraints, validations, and checks. It also streamlines data integration.
5. Object-oriented Relational Database and Polymorphic Data Store
An Object-oriented Relational Database (OORDB) combines principles of object-oriented programming (OOP) with traditional relational database management systems (RDBMS). It allows the storage of complex data types, including objects and collections, within the database. Many modern databases like PostgreSQL focus more on Object Oriented Relational Databases.
Polymorphic data stores handle schema-less data structures like JSON or XML. This allows unparalleled flexibility, especially when dealing with evolving data schemas in growing businesses. This flexibility supports both the increasing volume of data and the evolving structure and key data elements required as businesses expand.
Examples of Polymorphic Data Stores include:
- Document Databases: MongoDB, Couchbase, and Amazon DynamoDB
- Graph Databases: Neo4j, ArangoDB
- Key-Value Stores: Redis, etcd
- Object Storage Systems: Amazon S3, Google Cloud Storage
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6. Data Modeling, Data Storage, and Data Management
Banks and financial services institutions collect a lot of data from customers’ online/ offline transactions, social engagements and interactions, feedback, surveys, and more. Data modeling creates an illustrated model of this data to denote its attributes, establish relationships between data items, identify constraints, and define the business logic or context to manage the data. Various data modeling techniques such as entity-relationship modeling (ER), dimensional modeling (used in data warehousing), and others are adopted to address specific data management needs and objectives.
Next, let’s explore Data Storage and Data Storage Management.
As organizations scale, managing growing volumes of data becomes crucial. A well-laid-out data storage management plan, leveraging AI and other advanced technologies, can address the challenges of data sprawl and redundant copies. This approach empowers BFS organizations to store data efficiently, and securely, in compliance with the laws and makes the data easy to find, access, share, process, and recover if lost.
Depending on your objectives, you can choose from a range of data storage tools and technologies, including on-premises solutions (local hardware and computer memory), cloud platforms (online storage using SSDs and flash drive arrays), or hybrid systems that combine both.
7. Data Quality Management (DQM)
DQM combines the right people, processes, and technologies to achieve end business goals through data quality improvement. Setting the criteria for high-quality information, sorting through the data collected, and developing strategies to eliminate low-quality data – this is all a part of the Data Quality Management process.
Why is DQM important?
To ensure that only valid data is collected through a united data framework, since poor data quality can result in poor business decisions, costing lost time, money, productivity, and reputation.
According to research by Gartner, “the average financial impact of poor data quality on organizations is $12.9 million per year.” IBM also discovered that in the US alone, businesses lose $3.1 trillion annually due to poor data quality!
Steps in DQM, at a glance
- Data Quality Analysis
- Data Profiling
- Understanding Data Metrics and Quality Criteria
- Setting Data Standards, Management Rules, and Creating Data Policies
- Data Monitoring and Continuous Updates
What drives DQM processes?
Technology: Data analytics and AI/Generative AI to transform vast volumes of data into trusted business information; prevent the negative impact of bad data by consistently learning data metrics’ normal behavior and sending anomaly alerts on bad data discovery.
Looking for expert guidance in creating a successful data modernization strategy?
8. Data Lakehouse
Data lakehouse architecture combines the ACID transactions and other capabilities of traditional data warehouses and the flexibility of data lakes to harness high volume, variety, and velocity of data. This open data management architecture enables financial services businesses to run high-end business intelligence and ML tasks on their enterprise data along with offering many other benefits such as improved data quality, governance, scalability, collaboration, and cost savings.
Banking and financial services businesses are increasingly adopting the data lakehouse architecture that supports advanced analytics for forecasting trends, assessing risks, and tailoring services to customers’ evolving needs.
Rapid advancements in AI and the advent of Generative AI, the popularity of open table formats, and the increase in data mesh implementations have contributed to this increasing shift to data lakehouse architecture.
9. Data Visualization, Data Analytics & Democratization
Data visualization is the pictorial/ graphical representation of data in a way that the organization can draw inferences to drive a business decision or resolve a problem. Various BI tools for data visualization, such as Superset, Tableau, and Power BI, are used for data visualization to help a BFS business in the following ways:
- Scale down big data to make it more comprehensible.
- Facilitate data-driven, accurate decision-making.
- Help in finding long-term solutions to problems.
- Establish a relationship between different data – identify data inter-dependencies or correlations.
- Enable comparative analysis for smarter decisions.
Data analytics is involved in every dimension of the banks and financial services sector – whether it is for enhancing customer experience, upselling/cross-selling products, exploring market opportunities, identifying the target audience for specific campaigns, managing operations (operational analytics), managing risks (risk analytics), budgeting and forecasting (business analytics), understanding costs (cost-benefit analytics) and more.
Data democratization makes accurate data accessible across the organization for actionable insights. Data governance is a key dimension of data democratization, ensuring the data being accessed is in line with all data security/privacy protocols through a technology-driven framework.
FinOps and its Impact on Your Data Modernization Strategy
Uncontrolled spending and budget overruns in cloud adoption are not uncommon. This has led organizations to focus on Financial Operations or FinOps. The ‘Finance-DevOps’ combo offers a structured approach to managing cloud spending effectively, aligning data usage with business value. FinOps combines technology, finance, and business teams through a set of operational processes that enable better financial management and resource allocation, driving strategies like “Grow the Bank” and “Run the Bank” for expanding and optimizing a bank’s day-to-day operations, customer base, and revenue streams.
FinOps has a critical role to play in data modernization as it aids in Data Management, Data Governance, Data Analytics, AI, and building a data-driven culture across the organization. The detailed insights and predictability of cloud costs allow for better planning of digital projects, accelerating digital transformation and innovation.
Therefore, when planning your data modernization strategy roadmap, considering FinOps can enable cost transparency and optimization for more informed decisions on data management and infrastructure investments.
Global 2000 companies continue to adopt FinOps. For BFS CIOs, CDOs, and leaders struggling to ensure uninterrupted cloud availability for customer innovation yet containing cloud expenditure, adopting the blend of ‘Finance and DevOps – FinOps’ leads to greater customer-centricity, financial accountability, and cloud-based innovation.
Thinking Ahead
Leaders in the banking and financial services sector, including CIOs, CDOs, and forward-thinking executives, are steering their organizations towards a transformative approach that centers on harnessing the power of data. Devising a robust data modernization roadmap is critical for long-term value gain. The focus must be on aligning the data strategy seamlessly with the core business objectives and embracing AI-driven tools and technologies that are right to achieve the end goal. This can help unlock the full potential of data to drive informed decision-making while safeguarding data against privacy, security, and compliance risks. Moreover, the incorporation of FinOps practices into this roadmap is crucial to bridge the gap between financial accountability and cloud-based innovation.
As a digital transformation enabler, KANINI is at the forefront of offering strategic data modernization solutions and services to banking & financial services and other industries, enabling enterprises to maximize the value of their data assets. Want to learn more about how your organization can benefit from data modernization? Get in touch today!
Author
Anand Subramaniam
Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.
