Data lake best practices to accelerate your business growth

Excel sheets and databases have long served businesses for data storage and accessibility.   

However, as data is growing exponentially, these traditional systems fall short in providing the flexibility and scalability required to meet modern data demands.   

That’s where a data lake helps! 

Data lake provides a single space where you can store data “as is”, access it when you need, and employ machine learning and advanced analytics to gain useful insights.  

This blog will explore the benefits, use cases, and common challenges associated with a data lake and also cover the best practices you can employ to harness its full potential.  

Data storage: a critical concern for businesses 

The constant spike in data volume is making data storage a major concern for businesses today.  

As per research, 402.74 million terabytes of data is produced every single day, and this figure is only going to grow in the coming years.  

While on one side, this astronomical figure highlights how fast we are growing digitally, it also poses scores of data storage challenges for businesses.  

Let’s look at some of the common data storage challenges haunting businesses of all sizes: 

Scalability 

As data volume increases, you will need systems that can cater to your changing requirements. Scaling is especially challenging for on-premise infrastructure that requires new installations, once the storage capacity is full.  

Security 

As data volume rises and regulatory requirements grow, data security gets more complex. The likelihood of unauthorized access and data breaches increases when there are large datasets to handle. Once data goes into the wrong hands, there can be significant legal repercussions and penalties.  

Data unification 

When data remains in silos, utilizing its full potential gets hard. There is a constant to and fro and accessibility issue, that impacts the pace at which work gets done. 

Data quality and lifecycle management 

As data ages, it becomes less relevant, and occupies valuable storage space, making it difficult to accommodate new data. Without effective policies for data retention, a data platform remains filled with low-value data that doesn’t serve the current needs of teams.  

Data lake – a unified, flexible home for your data  

 For diverse and vast data sets, a data lake acts as the ultimate solution.  

With a data lake in place, you can store data in its native format in a centralized location, scale it as per your needs, and keep your data secure with features like cataloging, access controls, and metadata management.  

Benefits & use cases of data lakes 

Data lakes have the ability to clear the chaos and pave the way for more clarity and efficiency in how your team works with data. 

Let’s take a quick look at the benefits you can glean from a data lake:  

• Provides a centralized repository to store data streaming in from multiple sources like your CRM, ERP, and social media 
• Supports multiple data formats, which means you don’t have to worry about structuring the data 
• Easy to scale as data volume increases 
• Eliminates data silos and paves the way for faster analysis and informed decision-making 
• Provides a foundation for analytics process to extract useful business-critical insights  
• Secures your data with role-based access controls, data encryption, and data masking 

Let’s now explore some of the data lake use cases:   

• 360° customer view – a data lake gives a unified view of customers, setting the stage for targeted marketing and personalized service.  

• Fraud detection – Enables processing and analysis of a massive amount of data that helps identify fraudulent activities.  

• Data analysis – Sets the stage for running advanced queries and conducting detailed analysis to track trends and extract useful insights. 

• Machine learning training – Equips you with a variety of data types to train machine learning models. 

• Targeted marketing campaigns – Enables marketers to understand customer journeys better and craft campaigns that strike the right chords. 

Common challenges arising from inefficient use of data lakes 

While data lakes have tremendous benefits, they also pose certain challenges that are crucial to tackle to put these data storage systems to optimum use.  

Data consistency 

Data lakes store a vast amount of raw data that lacks a standardized structure. This often leads to the data lake turning into a data swamp, where valuable data becomes unusable. 

With no quality checks in place, there are chances of drawing incorrect conclusions that hinder the decision-making process.  

Also, when data is not standardized, issues like data duplication and incomplete records might arise, reducing the data lake’s effectiveness.  

Data governance and security 

Due to varied data types and formats, ensuring compliance and security gets complex in a data lake.   

Since a data lake consists of sensitive information such as personal details and financial records, implementing a solid governance framework including access controls and metadata management is essential.   

Further, storing historical versions of data in a data lake requires you to store manual snapshots, which can be both challenging and costly.  

Bulk updates and data modification 

As data lakes are typically optimized for write-once-read-many (WORM) workloads, bulk updates get complex, often requiring a full reload or complete data restructuring, especially when handling large data sets.  

This limitation is often the reason behind slow performance and reduced efficiency of a data lake.   

Performance management 

The growing amount of data in a data lake often results in performance bottlenecks for data retrieval and analysis. With query time and storage costs skyrocketing, the overall performance of data lake takes a hit.  

Best practices to put your data lake to optimum use 

The right set of best practices will help you overcome the above challenges and make the most of your data lake. With these best practices, you will be able to derive value from your data lake, optimize its performance, and make the right decisions.  

Establish strong data governance and compliance measures  

As data lakes store a massive amount of data including sensitive information, it’s crucial to have solid governance measures in place to ensure:  

• Data quality
• Data security, and  
• Compliance with regulations like GDPR and HIPAA 

Setting role-based access controls is a great way to restrict data access to authorized users. You can also set up metadata management that enables you to tag data with crucial details like source and sensitivity level.  

Further, practices like data masking, encryption, and auditing, can be adopted to ensure your data remains in safe hands.  

Use data catalogs and data partitioning  

Data accessibility and retrieval are the two cornerstones of a useful data lake. If users are not able to fetch the right data at the right time, all the effort and time invested in creating and managing a data lake are futile.  

Using data catalogs, you can keep your data lake organized, allowing users to easily locate data through keywords and tags, and understand its purpose, source, and format.  

With data partitioning, you can segment data by date, geography, or business unit, that can be queried independently, improving both data organization and performance. When data engines have to query a limited data set, processing time gets faster, and results get better. 

Keep your data lake up to date with regular audits  

The rule of thumb when managing a data lake is to regularly review its quality, security, and overall performance.  

You can’t leave a data lake to its own devices. It has to be nurtured over time, with frequent cleaning of inaccurate or outdated data, updating catalogs, and analyzing query performance.  

When you identify what’s working and what’s not, you take the right actions at the right time, before the data lake turns into a data swamp filled with redundant and incorrect data.  

Implement processes for fast query performance 

Data lakes grow rapidly, with data flowing from different sources. If left unchecked, this can slow down query performance.  

When you are dealing with large datasets, performance optimization is essential. This can be achieved through partitioning which helps break down data into smaller, more manageable chunks.  

You can also leverage indexing and caching to run queries swiftly, conduct faster analysis, and keep the data lake performance intact, even as data volume rises.  

Manage data lifecycle effectively 

With data volume constantly growing, it’s essential to keep tabs on its lifecycle. 

With careful data lifecycle management, you will have only the most relevant information in your data lake. Adopt policies that help you set specific criteria for archiving or purging data based on its age, relevance, or regulatory requirements.  

You can also regularly audit data to identify what to retain or discard, ensuring that only the most valuable information remains in your data lake.  

This will keep your data lake from becoming a data swamp and ensure easy data accessibility. Also, your data lake will always have the space to store new data, keeping the storage costs under control.  

Work on the design and structure of your data lake 

The success of a data lake platform depends largely on how well it is designed.  

A poorly designed data lake can quickly transform into a chaotic mess that’s difficult to manage, making data search a cumbersome task. For your data lake to be a valuable asset, it’s essential to have a well-thought-out structure where there are clear hierarchies and segments.  

A layered approach with raw, processed, and standardized data clearly segmented is a great way to manage data quality and structure at all stages. With separate, clearly defined layers, a data lake looks more structured and provides faster access to information.  

The impact of data lakes on business success  

With data lakes, you get a flexible and scalable space where data from diverse sources can be stored securely. This means you can easily perform data analytics and get reliable business insights, that further drive strategies and decisions.  

Let’s glance at the various areas where data lakes can be game-changing for your business:

Enhanced customer experiences 

By bringing customer data from disparate sources (CRM systems, social media, etc.), on a centralized platform, data lakes save you from the constant to and fro of discovering the right data at the right time.  

With all customer data available in one place, it’s easy to analyze purchase patterns and browsing history and extract intelligent insights to provide hyper-personalized services. Personalized experiences that are tailored to your audience’s interests go a long way in building strong customer relationships that last a lifetime.

Data-driven decision-making

Data lakes unify the data coming in from all directions, offering a single source of truth, where data can be analyzed and visualized to gain an in-depth understanding of your business.  

You can identify anomalies and uncover trends. Also, by integrating AI and machine learning models, you can gain relevant recommendations. All these things together pave the way for quick and well-informed decision-making.  

Streamlined operations

Business operations are bound to improve when departments have a centralized repository for all their data.  

Collaboration over data gets easier, resulting in streamlined operations, faster decision-making, and fewer inefficiencies.  

The otherwise cumbersome task of looking for data across diverse tools and platforms becomes a breeze. This improves project delivery times and better performance. 

Data lake and data lakehouse – how are they different? Source

Based on your business needs, it’s important to understand the different data storage techniques, so you can decide which one fits the bill.  

While a data lake is primarily used for storing a large volume of raw and unstructured data, a data lake house follows a hybrid approach, providing you with the flexibility of a data lake and the structure of a data warehouse.  

Data lake architecture accommodates all data types and sources, giving you more flexibility and cost-efficiency. This is a flat architecture where metadata tags and unique identifiers are used to locate data.  

On the other hand, the data lakehouse architecture comprises storage, staging, and semantic layers, and supports ACID transactions, ensuring data reliability and consistency.  

If storing data in its native format and big data processing are your goals, data lake is your best bet. But, if you are focused more on analytics and want a hybrid solution that provides the best of both data lake and data warehouse, then a data lakehouse would be a good choice.  

Unlock the full potential of data with a well-managed data lake 

Raw data hides within it, golden opportunities that can take your company ahead of the game. However, to tap these opportunities, you need the right platform that centralizes all your scattered data, so that it can be easily accessed and analyzed.  

Having a well-designed and effectively managed data lake is a great way to gain valuable insights and make the right decisions.   

By addressing common challenges such as data governance, quality, and security, and implementing best practices, you can turn your data lake into a powerful, reliable resource that drives your business toward growth and innovation.