Your bank or financial institution is not restricted to just files and records. Your data has spread out in the world in forms of images, voices, videos, social media comments and your customers’ and stakeholders’ internet activity. It would be easier just to make a warehouse and stuff data into its segments. Trying and searching from them when the need has arrived for some insight. Yes, that would be controllable, but would that be profitable? Think again.

How to use data that is massive in form, varied in context, pouring in at the speed of light and full of insights that could steer your business in the right direction?

Filtering and segmenting data takes time, distorts its original form and takes a huge time uploading in database. In today’s time, having slow data is as good as having no data. Faster moving competitors with smarter databases and analytics would achieve far more in the time your warehouse figures out what to do with the data gathered.

It’s time to preserve data as it is, and with speed. It’s time to invest in data lakes.

Data lakes providing value in modern financial architecture

Data Lakes are being used for enterprise wide data management platforms. It is used to store and analyze data from disparate sources in its raw format.  It saves the cost of data transformation when you move it into storage in its original format. The data then can be analyzed and used based on need by the people and departments. Time, pace and quantity is set on the requirements of people. Data is more flexible to be analyzed.  But it also remains the responsibility of the end user to get the value out of the data using technology to the data lakes. For this, data governance is required. Or else the data lakes can end up being assembled disconnected data pools or information silos.

Modern data architecture:  Modern data is not just words, voices, images, conversations and opinions. Modern data architecture like data lakes are able to process, transform and analyze a mix of semi-structured, structured and unstructured data. The data volume is easily scalable, manageable and operable. It is easy to incorporate analytics tools, meta data catalogues and other tools that make searching for the right data easier and less time consuming. One of the world’s largest banks, HSBC has been using data lakes.

Data security:  Data is vulnerable in this age of misused technology.  Data banks are under threat from cyber criminals, data breaches, data loss and theft.  With brilliant new hacking inventions by cyber criminals, identity theft, robbery and forgery are on the rise. This has pushed data users to invest in data protection features. Data lakes include features of data backup and recovery along with various advanced security features like data encryption which can be implemented to keep the culprits out. Limited access, access control and password protection are some of the security measures easily available with data lakes.

Compliance management:  With increasing threats on data, the government and regulatory authorities are imposing new compliances on the BFSI sector every day. Keeping up with these regulations and compliances along with handling huge data flow is a challenging task. Thanks to technological advancement, with big data and data lakes harnessing the flow of data in a manageable way, putting restrictions and following compliances are fairly uncomplicated now. Assessing risk of situations and investors, detecting fraud from seemingly unrelated data with the help of analytics and following data related compliances like the Dodd Frank Act, Basel Committee guidelines on risk data reporting and aggregation can be done with proper filtration of data using data lakes.

Data Governance: Data in any organization, should be usable, transparent, consistent and reproducible. Without those qualities data is unable to produce desired results like giving business insights and calculating risks. To maintain these qualities we need data governance. This is particularly important for data lakes, as all data in raw form gets pooled in data lakes. Without governance, they would remain in silos, barely usable and static. Since various tools are available to diagnose and stream data, applying the right tools to the data lakes would help providing the governance your data needs, paving the way for analytics to bring out the insights in them.


  1. The data lake architecture’s best feature is that it stores both schema-less, raw and processed data in a centralized way.
  2. Data lakes provide a mechanism for rapid ingestion of data.
  3. Ability to map data across sources and provide visibility and security to users
  4. Ability to manage security, compliances and data masking (Substitute for real data, for testing, training purposes)
  5. Supports self-provisioning of analytic tools without IT intervention
  6. Develop, debug and optimize data smarter
  7. Low cost
  8. With features like Meta data tags and unique identifiers data can be easily queried when needed. Helping you get the right data at the precise moment.

Data lakes can easily turn into an ‘abyss’ of information without the right tools in place.  At the same time data lakes are your best bid to store, process, analyze and maintain data for the long run in the present scenario. Not only does it allow you to have one platform for all kinds of data, poured in from all sources, but also takes care of all data related requirements including data security which is one of the biggest challenges of our time.