Why Data Lakes Are Crucial To Business Analytics?

07 Jul 2020
Why Data Lakes are crucial to Business Analytics?

While the term Data is in cognizance of business of all sizes even the most layman person is aware of the buzz and fuss around Data. So from Database to Data Warehouse and now this Data Lake, we have come a long way.

What is a Data Lake?

Simplistically, Data Lake is a Big Data Architecture approach which can store unstructured and semi-structured data in a single repository and can later be used for multiple use cases from deriving insights, to Stream analytics and even for building Machine learning models.

Getting the house in order

When a business invests in Data analytics, they just know about the business goals to be achieved using the existing. Sustainability is the biggest miss when the decisions are made haphazardly in excitement to utilize the available data which results in bigger chaos with more cash burning into these modular initiatives.

To remedy this, a systematic approach of adopting data lake architecture can bring a huge advantage to all sizes of companies, in a data lake you can store all sorts of data for multiple initiatives from multiple sources. e.g. – You recently built a data mart intended to provide insights using a star schema for the accounts department to track and optimize expenses or you have created stream analytics with visualization dashboard to monitor real-time activity of traffic on the news section of a website. You will have to invest separately if you plan another such move, however with a data lake this process can achieve all applicability in a monolithic data lake architecture.

The Evolution towards Data Lake comes from SQL Database to NoSQL Database ultimately leading up to Data Lakes.

Access to huge sums of Data

Data from all sources be it sensors, logs, social media, web activity or Ads and even internal logs everything can be dumped into a data lake and later can be used for a variety of purposes. Data required for batch processing or real-time stream analytics seems reasonably sensible to store everything first and then do analytics or AI upon the data.

For instance, if you can derive more insights (based on social media, market trends, customer feedback) than your competitor than it obviously gives you a lead in the market.

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Breaking beyond silos

When there is a common dump for data across all business units it becomes fairly smooth to access data beyond silos and utilize to derive insights that are not possible with conventional analytics methods. Even data collected across multiple units and divisions can generate insights that could benefit the overall operational hierarchy.

Data Lake won’t evaporate your data ever

In the age of Data adopting a sustainable and long term implementation strategy should be a business’s first priority so a foundation can be laid for upcoming decades and that can accommodate the needs of most advanced implements to enable organizations to become fully data-driven.

Footnotes

In the end, it is convincingly apparent that data lakes aren’t going away anytime soon in fact they are the future Data Modernization when a business aims to achieve manifold benefits of Analytics, Machine learning and advanced AI.

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