How to Solve the Top 10 Cloud Data Lake Challenges
In today’s competitive landscape, organizations need a way to easily capture, store, access and analyze semi-structured and unstructured data to drive advanced analytics and AI initiatives. Such a tool would also accelerate continuous intelligence, better situational awareness and faster, more precise business decisions.
Enter the cloud data lake, a revolutionary concept designed to store a massive amount of raw data in a central repository and make the data available for analytics and reporting. Cloud data lakes provide access to data to easily discover, refine and analyze petabytes of information constantly arriving from multiple data sources.
Despite the many benefits of using cloud data lakes, they don’t always deliver on their promise. Here’s a snapshot of the ten most common data lake challenges:
- Data governance: Without adequate data governance, even well-constructed data lakes can turn into data swamps — unorganized pools of data that are difficult to use, understand and share.
- Data collection provisioning from disparate data sources and IoT devices: Data is generated from many sources such as websites, social media, applications and streaming sources. Collecting and ingesting data from these sources into a cloud data lake can create a huge bottleneck for analytics and AI/ML initiatives.
- Disparate data silos: In a typical multi-cloud environment, data is stored in siloed data repositories. This makes it difficult for to meet the diverse needs of all data consumers.
- Data quality: Bad data can lead to missed business opportunities, poor decisions, loss of revenue and increased costs. Organizations that suffer from poor data quality tend to experience operational inefficiencies and customer satisfaction issues.
- Data drift: This refers to unexpected and undocumented changes to the data structure, semantic and infrastructure. A data drift can break processes and corrupt data but also reveal new opportunities for data use.
- Data protection, availability and retention: Data loss, data inconsistencies and data corruption can occur in a cloud data lake due to human error or cyberattacks. All aspects of a data lake must center on protecting your data in transit and at rest.
- Cost overruns: The unpredictability of new pricing models and cost structure in the cloud creates budget issues and cost overruns for many organizations. In addition, a lack of skilled data engineers and automation tools results in inefficient use of existing tools that can increase the IT budget.
- Complex operations: Cloud data lakes often suffer from operational issues, which can make building and maintaining them operationally challenging. Custom hand-coded or point solutions will increase the total ownership cost, are hard to maintain and lack enterprise scalability. A lack of skilled resources can also limit scalability and result in poor performance.
- Difficult-to-handle large metadata: Many organizations have distributed data across different departments, applications, data warehouses and data lakes. And when data resides both on-premises and in the cloud, it’s difficult to know exactly what data they have and where. Plus, metadata volume in a cloud data lake increases proportionally with the data, which is challenging to manage when it scales.
- AI/ML projects making it from pilot to production: According to a survey by NewVantage Partners, only 15% of leading enterprises have deployed AI capabilities into production at any scale.1 Without the ability to automate the deployment of ML models in production, companies cannot generate measurable value from AI.
Get The Solutions to These 10 Data Lake Challenges
Now that you know the top 10 challenges…are you ready to find out HOW to solve these hurdles to data lake adoption? Download our eBook, How to Solve the Top 10 Cloud Data Lake Challenges, for the answers and other detailed information. This eBook will help you not only gain a competitive advantage but improve the customer experience.
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