What Is a Data Lake? How to Implement One Effectively for Your Business

For example, a company might store data from its operational databases, applications, and connected devices in a data lake, then prepare relevant datasets for analysis. A data lake can help businesses work with diverse data, but it does not automatically make information clean, easy to find, or ready for reporting. Governance and data management are essential.

Taking a data lake approach may help businesses improve customer interactions, enhance research and development (R&D), and increase operational efficiency. This article explains what data lakes are, their core benefits, how they can support BI, and how different industries may use them.

Businesses should understand what a data lake is, how it can work alongside BI solutions, and what considerations come with implementation. Read on for an overview.

Define Data Lakes

A data lake is a big data repository where you can store all of your structured and unstructured data. All data comes into a data lake in its native format as retrieved from the source, passing through little or no transformation. Teams can apply structure when they access or prepare data for a particular use, an approach often called schema-on-read.

By adopting a data lake, you can take full advantage of data storage and processing capabilities, reference the data for analysis at any moment, and use any tools and frameworks.

Valuable data can give your company a competitive edge. Researchers from Aberdeen estimated that a company’s data grows at a rate of at least 50% yearly. Such volumes can complicate the work of data-dependent companies and require sophisticated solutions to be effective.

Adopting a data lake can help companies tackle several business-critical tasks. Nearly half (43%) of surveyed companies responded that data lakes help increase operational efficiency.

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Other key benefits included making siloed data more available for all (32%), lowering transactional costs (29%), and offloading capacity from the mainframe (26%).

Unlike a data warehouse, a data lake offers more advanced architecture and multiple opportunities for storing and processing data. They make it easier to deal with the growing volumes of data and obtain better insights while using less effort. Data specialists can access data at any time to proactively use it for solving a company’s needs.

How to Implement a Data Lake

Implement a data lake around a specific business need, such as combining sales and inventory data for analysis. Start with a small, useful workload, then expand as the organization learns what data and controls it needs.

1. Choose a business goal and data sources. Identify the questions the data lake should help answer, which systems hold relevant data, and who owns each dataset. Include data sensitivity, update frequency, and retention needs in the plan.
2. Plan how data will be collected and organized. Decide how data will enter the lake, how failed or incomplete loads will be handled, and how teams will find each dataset. A common pattern keeps incoming data in a raw layer, validates and refines it, then prepares business-ready data for reporting or other uses.
3. Set up governance and security early. Assign data owners, document dataset meaning and quality, and grant access based on job responsibilities. Define safeguards for sensitive data, along with retention and deletion rules. Cataloging and access controls help users discover data while protecting it. 
4. Build and test a focused pipeline. Ingest a representative dataset and check that it arrives completely, can be understood, and is usable for the intended analysis. Test permissions and data-quality checks before making it available to more users.
5. Monitor and improve. Track data freshness, quality, access, pipeline failures, and storage and processing costs. Ask users whether they can find and use the data they need, then adjust the pipeline and controls before expanding to more sources.

A data lake requires ongoing management. Without clear ownership, documentation, security, and quality checks, stored data can become difficult to find and trust.

Benefits of Data Lakes

A data lake provides key capabilities that can help uncover new ways to level up your analytics and inform your decision-making. 

1. Data migration options involve importing data at different scales in real-time. This saves time and allows you to process different data formats.
2. Relational data can be stored, cataloged, and indexed in a way that ensures better security of data assets. 
3. Data scientists, business analysts, and developers in your company can access data smoothly and apply a variety of analytical tools and frameworks. Using tools from the Hadoop family and a Hadoop-based repository is an efficient method to achieve your goals.
4. Data lake implementation facilitates machine learning. Engineers can use historical data as an input to train different models to solve particular tasks. 
5. Data governance is another key aspect. The overwhelming amount and the variety of data require management. Data governance is critical to standardizing the data coming from diverse sources, ensuring data accuracy and transparency, and avoiding data lake dumps. 
   
   One solution is to use custom scripts to build a workflow and unite data-related processes and verification steps. Another solution is to use the existing tools applicable to data warehouses.  

We’ve painted a positive picture of a highly valuable data lake, but there are some downsides. Given that a data lake architecture is designed to handle raw data, the content requires outside control. 

Human supervision is necessary to ensure that the data is usable, accessible, up-to-date, and valuable. In that way, a data repository might be better suited to implementing your data initiatives, depending on the situation. 

How to Leverage Data Lakes to Improve Business Intelligence

BI is an efficient approach that can allow specialists in your company to use advanced methodologies to work with large volumes of raw data. This helps retrieve meaningful insights, which can improve decision-making and unearth new opportunities for business growth.

The role of BI solutions across various industries is hard to overstate. BI has become a top priority:

• Logistics and supply chain
• Finance and insurance
• Health care
• Telecommunications
• Marketing and advertising

Computational capabilities and cloud technology enable the processing of big data, reinforcing the digital transformation of those industries.  

A data lake can enhance a BI solution by providing greater potential for data processing. It can serve both as a centralized source of data for building a data warehouse and as a direct source of data for BI. 

Data lakes have applications in data science and machine learning engineering, where massive datasets are the backbone of technical solutions. In sum, a data lake can be an important pillar of BI and help optimize raw data processing.

How Other Industries Use BI Differently

Quick access to raw data, secure storage, reduced efforts at the start, and time-saving can benefit different industries. Here are a few examples.

Logistics and Supply Chain

Cargo storage, packaging, transporting, and distribution have a lot to do with big data. The same goes for the optimization of existing routes — in other words, transportation means, delivery methods, and allocation of workforce resources. 

A data-driven approach can help optimize operational efficiency, minimize errors, reduce delivery costs, and boost sales and revenues.

Finance and Insurance 

The role of big data in this industry is mainly to mitigate money-related risks while increasing productivity, and improving customer service. 

Data visualization tools are very useful in retrieving hidden information. Faster and more accurate reporting is what makes it possible to instantly share critical insights. What’s more, clients can receive better service due to simplified claim management and invoice processing. 

Health Care

Big data helps professionals in the industry to promote personalized medication and reduce the costs of treatment. Better data management is a vital part of life-saving activities. Having data at hand influences the reduction of medical errors and helps improve patient safety.

Telecommunications

Automation has already caused significant changes to this industry. It can help boost team productivity by handling repetitive tasks that involve customer relationship management. Automation is poised to reduce operational expenses and improve service delivery. 

Marketing and Advertising

A data-driven marketing strategy can yield an increased return in investment (ROI). Marketing specialists can leverage big data to get more accurate market insights and also to improve customer targeting.

This insightful report from Enterprise Irregulars dwells on some other industry-specific applications of cloud BI solutions.

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Over 70% of survey participants in the retail, technology, and advertising industries all consider BI to be important, with retail taking the lead at close to 85%. 

The opportunities that BI opens up may vary depending on the industry and business type. 

There is no one-size-fits-all approach, so it’s worth it to consult experts in BI and big data to help you figure things out. That could range from structuring your company’s data to creating custom, easy-to-interact-with dashboards to visualize workflows.

Data Lakes Are Useful Solutions for Data Processing

Data lakes offer a full range of benefits for businesses of various sizes. A forward-looking company should consider implementing a data lake to solve complicated business challenges.

By better managing your data, you can: 

• Leverage both your structured and unstructured data
• Gain more useful insights about your business
• Uncover unique business growth opportunities

Building your data lake can help you adopt the best data management practices. Upstage your competitors by providing a solid, data-driven base for your business.
 

About the Author

Katrine Spirina Technology Journalist, InData Labs

Katrine Spirina is a Technology Journalist at InData Labs, a professional services firm delivering AI-powered software and technical solutions to companies who want to leverage data and machine learning algorithms for business value. Katrine writes about AI, machine learning, and data science from the perspective of the transformative potential of technologies for businesses.

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