Driving Real-time Decisions with Data Lakes: A Q&A Compilation by Quantzig

Jan 22, 2021

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If you manage and analyze huge volumes of data on a daily basis, you’ve probably encountered the term ‘data lakes’ either as a buzzword or as a solution to the big data challenge facing businesses. With data continuing to proliferate, data lakes are now perceived as the best solution to tackle business challenges.

But rising data volumes are only a part of the data storage and big data management problem facing businesses today. Technologies like artificial intelligence (AI) and the Internet of Things (IoT) generate huge volumes of data from disparate sources. It includes data from embedded sensors, social media platforms, emails, clickstream data, and much more. This sudden influx of data sets can be difficult to manage, but businesses that skillfully leverage these data sets for decision making can enhance operational efficiency, identify outliers and discover new opportunities to thrive.

It’s never too late to capitalize on your biggest asset – data. Speak to an expert to gain exclusive, tailored insights on data lake analytics.

Our big data experts have compiled befitting answers to some of the questions we have encountered along the data management journey. We hope this article will help you explore new solutions to build and maintain enterprise data lakes.

Why are organizations incorporating data lakes into their business strategy?

Businesses today deal with huge volumes of unstructured data sets. Given the volume, variety, and veracity, traditional enterprise data warehouses cannot help organizations store and leverage these data sets for decisioning. Designed to store structured information, data warehouses, unlike data lakes, generate pre-programmed reports and lack the ability to accommodate real-world requirements.

Deploying data warehouses for data querying and analysis will not help businesses gain accurate insights. And since businesses must analyze structured, unstructured, and semi-structured data to make effective decisions, most are on the lookout for newer ways to capitalize on big data. Incorporating data lakes into the data management strategy has helped businesses tackle this challenge and gain an edge through impactful decisions.

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Collaborating with Quantzig can help you adopt a progressive approach to creating and implementing enterprise data lakes, with continuous guidance and support from analytics experts. Request a FREE proposal to get started.

What are the common data lake pitfalls, and how to overcome them?

Since the creation and implementation of data lakes differ from business to business, not all are comparable and few might lack enterprise-grade capabilities and act as a repository for a large volumes of unstructured business-critical information called ‘data swamps.’

Here are a few common data lake pitfalls and ways to overcome them-

Lack of a clearly articulated business case for creating enterprise data lakes

Without a clear understanding of how data lakes can benefit your business, you might fail to obtain the timely approvals and buy-in required to move forward. Clearly defining your business goals and ensuring your business functions align with these goals will help identify high-value use cases. We have the expertise and skill it takes to design and build data lakes tailored to your business needs. Reach out to our experts for more information.

Absence of a well-integrated data management plan

Data lakes supplement and, in rare cases, fully replace a cloud data warehouse. However, this depends on the ability of a business to create a well-integrated plan for data management. But because raw information is stored in its original format, data lakes might not be as effective as an enterprise data warehouse for querying structured data. In such cases, businesses must decide if they wish to completely replace their data warehouse or retain it for operational and financial reporting.  Moreover, enterprise data warehouses can also be supplemented with data lakes for storing, querying, and analyzing business information in a more cost-effective environment.

Wrong platform and technology choices

Selecting the wrong technology and analytics platforms can cost a dime in terms of implementation and management complexities. But with the right solution, businesses can accelerate data ingestion and leverage data governance to comply with regulations while capitalizing on real-time data processing and analytics.

Given all the pros and cons, and the pitfalls associated with implementing enterprise data lakes, it’s evident that data lakes are a real solution for the big data challenge. It also helps businesses capitalize on the most extensive and fast-growing asset, i.e., unstructured and semi-structured data.

As you begin assessing data lake solutions for your business, it’s essential to look for enterprise-grade capabilities that expand the scope of analytics and insight generation while streamlining data management and reducing costs. Quantzig’s cost-effective, advanced analytics solutions entrust businesses with real-time decisioning capabilities that maximize the potential of enterprise data lakes, empowering business leaders to make smarter decisions.

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