Over the last decade, data lakes have emerged as a key technology to streamline data collection and analysis. As organizations implementing data lakes only require to collect some of their business data at one physical location, this overcomes the large volume limitation of data warehouses.

Despite their benefits, organizations still need help with data lakes. Gartner analyst Donald Fienberg highlights why data lakes fail in “How to Avoid Data Lake Failures.”

Some of the common challenges with data lakes include:

  • High implementation costs
  • Presence of Data silos
  • Lack of technical skills

If not implemented well, data lakes can hamper rather than enable analytics success.

How does working with a Data Lake managed services partner (MSP) like Emergys help enterprises? Let’s discuss the benefits.

5 reasons to work with a Data Lake MSP

Companies must consider the following reasons for working with a data lake-managed services partner:

1. Lower costs

Data lakes are expensive to implement and maintain over time. Though cloud platforms and technologies like Apache Hadoop are free and open-source, companies can spend months setting up a data infrastructure. This can add to the overall business costs.

As data volumes and complexity keep growing, in-house data lake infrastructure is expensive to upgrade and maintain. Organizations also incur the costs of employing and retaining in-house data specialists and professionals.

By using inherent data systems and technical resources, MSPs can lower the costs for companies looking to implement data lakes.

2. Technology issues

Outdated in-house technologies and infrastructure understanding restrict companies in their data lake implementation. Existing data lakes require constant data flow and transformation for data analytics. Additionally, most data lakes use the popular Hadoop framework. While a Hadoop-based data lake works for large datasets, it could be better for smaller datasets.

On the other hand, data lakes MSPs work with modern and innovative Big Data technologies like Cloudera and Snowflake. While Hadoop works as a repository for raw data, Snowflake supports capabilities like real-time data ingestion and JSON.

3. Skills shortage

The industry demand for skilled data professionals like data scientists and engineers continues to increase yearly. 60% of companies struggle to hire qualified data scientists amidst a severe talent shortage.

Due to a severe talent crunch, companies often don’t have the technology bandwidth to transform their data into useful analytics for better decision-making. Besides that, organizations must develop a “data-centric” work culture to attract and retain good talent. As data technologies change, organizations must spend time and money “upskilling” existing resources.

MSPs have an available team of experienced data specialists who can get started with implementing data lakes quickly.

4. Poor data quality

According to Gartner, poor data quality costs companies almost $15 million annually. Data-driven organizations are dependent on high-quality data to drive the best results. Organizations face multiple data-related issues, including incomplete, hidden, and unstructured data. For instance, AI-powered applications produce inaccurate outputs when fed with low-quality data.

Poor quality can turn data lakes into data swamps, thus eliminating their benefits.

Companies need to build compelling data processing capabilities to feed high-quality data into analytical tools. Building these capabilities in-house takes both time and money. With their superior data cleansing skills, MSPs can provide effective solutions to improve data quality.

5. Lack of scalability

Data lakes often require structured and organized data, which makes them less flexible and scalable. After its initial deployment, data lakes are expected to handle increasing data volumes. Organizations must choose the right data analytics framework to deliver real-time data access to build a reliable and scalable data lake.

Additionally, a low total cost of ownership (TCO) and effective management are key requirements for scalable data lakes. With MSPs, organizations no longer need to think about how to scale up their data lakes for higher processing capabilities.

Next, let us discuss how Emergys’s data lake services can overcome these challenges.

How Emergys’s Data Lake Managed Services can help

Organizations must first eliminate isolated data silos to leverage their Big Data initiatives. Additionally, executives need help with making the right decisions when insights must be drawn from unstructured data sources.

Here is how the Emergys services help in implementing data lakes. The powerful Emergys MSP offering includes:

  • Customer consultation and roadmap for creating data lakes
  • Implementation of data lakes using cloud technologies like Apache Hadoop, AWS, and MS Azure
  • Data extraction from unstructured sources, including the web, social media platforms, log files and PDF documents
  • Implementation of data lake frameworks for real-time ingestion and analysis of data from IoT devices
  • Use of Big Data technologies like Kafka, Spark, and Streamsets
  • Data modelling for specific business models used in data lakes
  • Implementation of ETL tools like Talend and Informatica and scheduling tools like Control-M for integrating and controlling data lake processes

Here are one of our customer successes stories:

  • One of the largest healthcare companies in the U.S. had over 200 siloed applications. Based on MS Azure, Snowflake, and Talend, our data lake solution provided them with a 360-degree view of their patients, employees, and service providers

Conclusion

Data lakes are very effective at storing data, thus offering both flexibility and scalability to data-dependent organizations. However, companies often need help to extract business value from their data lakes, resulting in project failures. Working with a managed services partner like Emergys can add value through their technical expertise and project experience.

At Emergys Solutions, we design data lake solutions based on the customer’s end-user analytics and reporting requirements. Our managed services ensure that you have all the technologies and resources to meet your business objectives. Here are 5 reasons why you need to work with a data analytics managed services partner like Emergys.

Partner with us in your data analytics journey. Start your data journey today.

Connect with us to learn more.

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