Data lakes done right: challenges & mistakes to avoid in enterprise data management

We understand that building a data-driven enterprise is about more than just technology — it’s about assembling the right team to create a strong foundation for innovation, agility, and informed decision-making. With over 17 years of experience in forming high-performing tech teams, we partner with organisations to unlock the full potential of their data through expertly designed solutions.

Our dedicated teams, made up of data engineers, DevOps specialists, and data scientists, bring expertise to every step of your data journey. From architecting streamlined data lake solutions to consolidating data into secure, unified repositories, and implementing governance frameworks for accuracy and compliance, our experts are equipped to handle the complexities of modern enterprise data needs. These scalable, resilient solutions adapt as your business grows, transforming your data infrastructure into a strategic advantage.

But expertise doesn’t stop with delivery — we’re passionate about sharing our knowledge to help businesses thrive in their data-driven transformations.

Data — the game changer

Data is a powerful enabler across industries. Hospitals use it to maximise resources and save lives, while financial organisations leverage it to prevent IT failures, ensuring smooth operations. For businesses, more data equals more opportunities to learn, save, and improve. However, as enterprises generate vast amounts of data —  over 500 petabytes for some — the question arises: Where do they store it all?

An increasingly popular solution is a data lake — a centralised repository capable of storing raw, diverse data formats with the flexibility needed for advanced analytics, machine learning, and other data-driven innovations. In this article, I’ll share our insights into this powerful approach to data management. Together, we’ll explore the key challenges in building and managing data lakes, uncover common mistakes that can derail projects, and highlight how expert teams can help you overcome these obstacles to achieve lasting success. Let’s dive in!

The role of data lakes in enterprise data management

What do these have in common: an insurer adjusting premiums for weather risks, a traveller’s app rebooking a delayed flight, and a marketer fine-tuning local campaigns and discounts for e-commerce? They all rely on data lakes to power these capabilities.

Unlike traditional data warehouses that only work with structured, organised data, data lakes can store raw, unprocessed data in its original format. This flexibility allows companies to save time and reduce costs by skipping the need for extensive data extraction, transformation, and loading (ETL). Data lakes also support real-time use of structured and semi-structured data, making them a strong foundation for data analytics and AI-driven services.

Moreover, data lakes make data accessible to non-technical users. Without requiring SQL knowledge or constant reliance on IT teams, they enable business users to explore and leverage data through self-service dashboards, query tools, or visualisation applications. Features like data catalogues and glossaries further simplify the process, making it easy to browse, search, and analyse data sets using indexes and keywords.

Why centralised data lake storage is so important

Data lake architecture is essential for modern enterprises aiming to make decisions based on data, anticipate trends and user behaviour, respond dynamically to changes and events, and handle huge volumes of streaming data at scale. Without it, enterprise data use is restricted to structured information, which massively hinders innovation and progress.

In healthcare, for example, only 20% of electronic health records are structured, making the remaining 80% of insights completely useless if no solution is available to pull, store, and analyse them. With data lakes combining diverse data types, healthcare organisations unlock invaluable insights from data query and management platforms, social media, IoT and connected devices, and other systems across patients, medical providers, hospital operators, clinical researchers and healthcare insurers. This capability is particularly impactful in pharmacogenomics, for example, where vast genomic data and clinical trials inform personalised medicine, or in medical imaging analytics, working with large, unstructured image files from MRI, CT and X-ray scans.

In retail, data lake architectures support omnichannel experiences by creating comprehensive buyer profiles favoured by 7 out of 10 shoppers. They also collect and collate data from point-of-sales systems, IoT sensors, and transportation platforms for real-time inventory management and tracking, as well as power dynamic pricing and promotions.

Data lakes also enhance real-time traffic management by integrating live feeds from cameras and sensors, improving traffic flow, safety, and public transport reliability and reducing congestion and pollution. Another interesting use of unstructured data unlocked by enterprise data lakes is precision farming, integrating satellite imagery, IoT sensors, and market data to optimise crops and reduce waste.

Five challenges in building & managing data lakes

Data lakes enable faster decision-making, providing near/real-time access to information, empowering teams, and offering flexible growth. They can be more cost-effective than traditional data warehouses, allowing for high-volume data storage and raw data ingestion.

However, implementing a data lake solution in an enterprise isn’t free from challenges. Like all innovations, it requires a multidisciplinary team of data engineers, architects, governance experts, security professionals, cloud architects, and data scientists for effective setup and implementation. Strong project leadership and change management are essential to ensure alignment and progress. I’ll get to how to find and build these capabilities later, but let’s first look at the challenges this team will face.

Data lake challenge 1: Data integration

A seamless flow of real-time, complex data is the X factor in the race for efficiency and data-driven decisions. Yet, outdated solutions are often an obstacle, incapable of handling all the complexity a modern enterprise deals with daily.

One of our clients, a global mining leader, faced this frequent scenario before adopting a data lake solution. They were using an old Azure Data Factory set up to collect data for efficiency and sustainability decisions. 

To unify data from globally distributed departments and embed scalability and flexibility into the client’s enterprise data management infrastructure, Pwrteams assembled a multifunctional, scalable expert team. The team implemented a modern enterprise data lake, a Databricks solution running in Microsoft Azure cloud. Mixing different data types, like JSON logs from web servers with SQL tables from an ERP system, required complex transformations, powerful ETL (Extract, Transform, Load) tools and a reliable framework for security and governance to avoid the risk of unauthorised access and misuse.

Approaching the integration in a strategic, knowledgeable manner ensured data quality and consistency, which is essential for reliable and timely data analyses. Centralising the so-far distributed and siloed data in one lake with Unity Catalog improved control over access, metadata and governance. Workflow automation, impossible with the previous setup, reduced operational costs by 50% while improving data ingestion and distribution across operational systems, business intelligence and ETL tools, social media platforms, ERP, CRM, and other platforms in near/real-time. 

Data lake challenge 2: Data governance and compliance

Without proper governance, a centralised data lake can become a significant liability instead of an asset. Consider this scenario:

A large retail chain collects vast amounts of customer data — purchase histories, loyalty program data, heatmaps, clickstreams, and inventory records — to enhance customer experiences. While this approach sounds promising, it introduces significant risks without governance measures like access controls, data quality standards, and metadata management. For example, customers might receive duplicate promotional emails, irrelevant offers, or emails addressed to them incorrectly. These minor issues are just the tip of the iceberg.

The more severe consequences include unauthorised access to sensitive customer information, exposure of purchase and payment histories, and costly regulatory fines for non-compliance with standards like HIPAA, GDPR, or PCI-DSS.

Data governance experts must prioritise transparency and traceability across all data types and formats to mitigate these risks. A robust governance framework can address legal and financial risks while ensuring data is both secure and usable. Here's how we helped a mining industry client strengthen their data governance during a major infrastructure upgrade:

With these measures in place, the client transformed their data lake into a secure, transparent, and compliant resource that supports both operational and strategic goals.

Data lake challenge 3: Security risks

Being a single point of failure, data lakes are a big target for cyber threats and accidental breaches. They are meant to hold vast information volumes and scale infinitely, making applying one-size-fits-all security measures impossible. Moreover, different data types and formats also require different protections, which further adds to the complexity of securing a lake. 

Many things could go wrong when working with data stored in a lake. For example, a financial analyst who pulls up a sales report for an external vendor can accidentally share a set of data containing sensitive customer information. Similarly, a marketing manager in a hospital might view confidential patient health records due to misconfigured access settings. Additionally, ransomware can target data lakes, threatening all company operations by locking out access to all data.

Securing a data lake against these scenarios starts with thoughtful design. Apps like Active Directory, Okta, or AWS IAM help manage user permissions, while encryption tools like Azure Key Vault and AWS Key Management Service keep your information safe. Tokenisation replaces sensitive data with tokens for added security. But the list of enterprise data lake safeguards doesn’t stop there. 

You’ve also got data loss prevention systems, classification tools, monitoring and auditing platforms, backup and disaster recovery apps, and network security tools, all working together to ensure secure data handling. 

Managing these security elements can be complex, and human factors remain a common cause of breaches, with three in four events caused by human error. To make matters worse, 21% of enterprise cyber attacks target remote employees’ home environments, which makes it essential to embed relevant protections in your data lake setup to protect online teams, too. 

Ultimately, protecting your data lake isn’t just about embedding the right tools but also involves regular training, raising awareness, and constant communication. Your team of data lake experts should lead the way, ensuring everyone understands the risks and how to minimise them.

Data lake challenge 4: Scalability & performance

Scalability is one of the promises of data lakes, but your infrastructure needs to be properly configured and maintained to realise it. Otherwise, you may encounter problems that can slow down your data work and affect reliability. This can lead to flawed business decisions, missed opportunities, and slower innovation, affecting your market share and profits:

• Storage bottlenecks: Not setting aside enough storage from the beginning can lead to problems down the line, especially as your data grows. This can slow down access times and disrupt business operations and decision-making. Plus, you may end up having to put in extra effort and money to add more storage unexpectedly.

• Compute bottlenecks: Sometimes, the computing power of a data lake can be an issue (so the processing and analysis of data stored within a data lake). If you don’t plan your computing capacity well, you might encounter problems when handling complex queries or large datasets. This can make data processing slow and frustrating over time.

• Slow data ingestion: If your data lake solution doesn’t scale well, it can also slow down the collection and bringing of data from different sources. Inefficient data transformations and loading processes can hinder how quickly you can use the data, and without quality checks in place, inconsistencies may arise.
• Inefficient queries: As time goes on and the volume and complexity of data grow, the speed and efficiency of your queries can decrease if you don’t optimise them.

To keep your data lake running smoothly, no matter how much data you have, it’s important to use strategies like indexing, dividing your data into manageable parts, and optimising your queries. This requires a mix of tools – from cloud storage solutions and large data processing frameworks to tools for ingesting data and managing workflows. Pulling everything together effectively requires a combination of expert skills, including data engineering, cloud computing, data science, analytics, and big data management.

Data lake challenge 5: Cross-functional collaboration

Building an enterprise data lake is complex. It’s not a one-person job. You need a diverse team of skilled professionals to succeed:

Given the scarcity of talent and the complexities of modern tech hiring, assembling a cohesive team of experts with all the necessary roles and skills can be a significant challenge. This is particularly true for positions like Data Scientists, Big Data Engineers, and Data Security Analysts, who are among the most in-demand roles in IT and tech. Meanwhile, the European tech talent shortage persists.  

In these circumstances, partnering with a trusted, dedicated team provider can be a smart move. It allows you to quickly fill roles with experienced, vetted tech teams (within 4-8 weeks, on average) committed to your data lake project and business goals.

Common mistakes to avoid when implementing data lakes

Challenges abound in enterprise data lake architectures but so do the advantages. To unlock the latter, you need the skills and talent to tackle the former. 

Based on my experience with clients, I’ve noticed several common mistakes in data lake implementations that often end in failed projects or poor performance and high costs. The good news is that these issues can be avoided by getting expert guidance before starting your data lake project.

Mistake 1: Underestimating team expertise

Data lakes offer a simpler alternative to traditional enterprise data management systems, but deploying them isn’t as easy as it seems. While they can be set up faster than other solutions, the implementation still requires careful planning and an experienced team.

A common pitfall for companies is not involving DevOps experts and not hiring the right data engineers. This can lead to slow development and errors, resulting in issues with data quality and performance. A lack of automation increases the risk of mistakes and potential data loss.

Moreover, your company may struggle with compliance and governance without automated monitoring. Relying on manual processes can raise project costs, often exceeding what it would cost to hire skilled DevOps professionals. Therefore, it’s important to invest in the right expertise for a successful data lake implementation

Mistake 2: Poor data governance planning 

A big mistake in setting up data lakes is not having a governance framework. Without proper oversight, data issues like inconsistencies, missing values, and duplicates can arise, creating 'data swamps' – chaotic repositories that make data access difficult instead of easy.

Poor governance can also weaken security. If access controls and procedures are unclear, it can lead to unauthorised access and data loss, resulting in expensive compliance fines.

These governance failures lead to flawed decision-making and delays. Employees waste time retrieving, validating, and analysing data, which reduces their productivity. As a result, organisations face operational inefficiencies, delivering slower and less reliable products and services. This erosion of customer trust ultimately undermines stakeholder and investor confidence, seriously threatening your project and teams.

Mistake 3: Overcomplicating the architecture

Many companies make their data lake systems too complex, often due to a lack of expertise. This complexity can lead to slower data processing and more errors.

For example, having too many data layers slows down data processing and increases the likelihood of errors, while developing complicated data ingestion pipelines requires more time and effort to build and maintain them. Additionally, overly strict security measures can limit access to essential data for users who need it.

In the end, these issues can make it harder to access important insights, slow down operations, and increase costs. By getting help from skilled data engineers and DevOps teams, companies can simplify their data pipelines, create a more manageable security model, and design a clearer data structure. This makes systems more efficient, increases flexibility, and lowers costs.

Building a data-driven enterprise with Pwrteams

Data lakes offer immense potential, but navigating their complexity can be daunting. To unlock their value, you need a strong team and a well-defined plan. At Pwrteams, we've spent over 17 years assembling high-performing tech teams for global companies across industries. With expertise gained from over 300 team partnerships, we understand the integration, governance, and security challenges of building and managing data lake architectures.

By partnering with us, you can access expert data engineers, DevOps specialists, and data scientists who will integrate seamlessly with your organisation to help you:

• Design and implement optimised data lake architectures tailored to your needs, without unnecessary complexity.
• Consolidate data from multiple sources into a unified repository for fast and secure access.
• Establish robust data quality and security frameworks to ensure accuracy, compliance, and reliability.
• Build scalable, resilient solutions that grow alongside your business.

With no upfront costs and a refined hiring process, we connect you with the top-tier talent you need to transform your data landscape. Ready to build a team that delivers real value? Contact us today.