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How Enterprises can unleash full data potential with Data Lake Analytics

Dheeraj Rawal Dheeraj Rawal | Sep 23, 2020
Data Lake Analytics for Enterprises

This blog is a tell-all for organizations waiting to power their businesses with intel pulled out of the data. Read it to find out how you can benefit from data lake analytics and whether data lake fits your business goals. Also, discover how a fintech group adopted a data lake solution that saved them 40% time (when many companies are still evaluating data strategy and investments, this fintech company seems to have come out of 'rabbit hole' situation).

The State of Growing Data

A report published by Aberdeen Research made interesting revelations on a range of data challenges that companies face. The report also stated approaches these firms resort to tackle these challenges.

Here’s what report claimed: on average an organization collects data from about 33 different sources with the intent of analyzing it.

This data grows about 50% a year (which can be truly baffling for a business that’s using traditional/legacy means to manage data). Organizations can’t manage what they can’t measure (properly). All in all, not able to gather and analyze the data is losing opportunities in many ways (we will see why in the latter part of this blog).

Date Lake, Analytics, and Beyond

This brings us to Data lake – it's a data repository that enables storing enormous volumes of structured and unstructured data (all types of as-is/raw data in various shapes and sizes). Imagine data coming from IoT devices, social media platforms, customer emails, vendor systems, internal platforms, etc. stored in this data lake in the original format (only to be analyzed later).

Until recent years, big data technology’s access has been limited to large scale enterprises (for the obvious investment reasons), but the landscape’s changed with cloud data lake analytics services hitting the market. Many small and mid-sized firms can now afford and benefit from the technology (as it makes inroads), without breaking the bank.

What Makes Data Lake a Promising Investment

Here are the most common reasons enterprises choose to invest in data lakes: to enhance operational efficiency, democratize data, reduce transactional costs, and reduce capacity from the data warehouse or a mainframe.

The Aberdeen survey also claimed that businesses who deployed data lake analytics witnessed an increase in organic growth revenue of 24%.

Let’s look at some key benefits that make data lake promising for companies:

  • Data Availability for Your Decision-Making

    With data lake, businesses can make the data available and accessible to different teams. Yes, democratizing the data is doable. Employees at different hierarchies and departments can lay their hands on the data for decision-making - the data no longer remains a luxury that only top executives enjoy.
  • Scalability at Remarkable Lower Costs

    Simply put, businesses can store large volumes of data without having to structure the data. This is an added advantage for organizations that are looking to just store the data for now without the immediate requirement of analyzing it. This option comes cheaper than data warehousing – which apparently, requires structuring of data while storing.
  • The flexibility of Keeping Data As-Is

    Since the data stored is in native format (unstructured), businesses can transform the data as needed in the future. This allows the data to be schema-free. Data technology is fast-changing, having it structured now for a later use prove costly if the format turns irrelevant later.
  • Advanced Analytics in Real-Time

    Companies benefit from data lake’s incredible processing power. Deep learning algorithms make real-time analytics a possible scenario. With cloud services that offer on-demand analytics, businesses pull out intelligent insights in seconds, without having to worry about infrastructure requirements.

    Well, another edge that data lake offers is that it supports languages other than SQL. Legacy warehouses have this limitation as anything beyond the purview of SQL isn’t doable, advanced analytics is challenging. But with data lake, you can run different analytic techniques like SQL queries, machine learning big data analytics, real-time analytics, full-text search, and more.
  • Speed That Enterprises Love

    What otherwise would’ve taken months to make reports, data lake analytics makes report creation possible in a matter of hours. Developers, data scientists, and analysts love using tools like Hadoop, Spark, etc. with data lake to process large datasets fast.

Business Stories

Fintech business leverages AWS Data Lake

TNG, a Fintech group, adopted data lake for reporting and analytics using Amazon S3 service. The company experienced a tremendous drop in latencies (below 10 milliseconds) and has also lowered the cost of running analytics by as much as 50%. TNG saves about 40% of its management time spent on procurement and vendor management.

Confectionery Company Couples Azure Data Lake and Power BI

A Confectionery leader with 35,000 employees across the globe was immersed in traditional spreadsheets for reporting and decision-making. Saviant Consulting helped this company by implementing a Power BI solution that pulled insights straight from Azure Data Lake Analytics at the backend. What followed is a real-time view on orders, performance-tracking, actionable data for decisions concerning employee, retailer, and distributor front.

The True Value of Data Lake Delivered

There are more businesses out there like TNG that use data lake for crucial business objectives and initiatives. Companies devise innovative reward and loyalty programs by using insights extracted from data lake analytics.

Organizations are sharpening their research wings – manufacturing companies optimize their designs by choosing the right materials that ensure performance.

Data lake makes it even easier for businesses that collect tremendous volumes of IoT data because collecting, storing, and analysis can now happen in real-time. This results in enhanced operational efficiency and quality.

AWS or Azure

Both Amazon Web Services (AWS) and Microsoft Azure offer cloud data lake services. For enterprises invested in Microsoft, Azure Data Lake Services of Azure Analytics product come as a natural choice. For enterprises with large projects, AWS seems the right fit. Making a head-to-head comparison of these giants calls out for a separate blog (drop a note below if you’re keen to read that).

Being a cloud technology consulting company, we help organizations determine the right approach that aligns with their goals and digitalization initiatives. We’ve enabled digital transformation initiatives for around 120+ global customers that have been spot on.

Author's Bio

Dheeraj Rawal

Dheeraj Rawal
Content Specialist | Saviant

Dheeraj is a technology enthusiast who helps businesses to uncover powerful tech stories that aid in decision-making.

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