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  • The Bluemetrix Team

When Should You Factor a Data Lake into Your Data Strategy

More than 90 percent of all data lakes are deployed to cloud environments because of the ease of use in which a physical environment can be set up and made available.

A challenge remains configuring and customizing these environments cost-effectively and ingesting relevant and disparate types of data where it can become rapidly productive, appropriate to an organization’s industry and unique business requirements.

In this article, we will explain when you should be using an automated data processing platform to build and integrate a true data lake into your data strategy, and do it cost effectively.

Free checklist for a successful data lake implementation

Two Different Approaches – Two Different Solutions

Nearly three-quarters of respondents in a 2018 Eckerson survey said the data lake they used “fosters better decisions and actions by business users.”

Typically, an organization will require both a data warehouse and a data lake (and data hubs, for that matter) as they serve different needs and use cases.

But what makes data lakes smarter for many of today’s enterprise-level organisations?

Perceived Disadvantages of Data Lakes

The main criticism of data lakes has been that exploring large amounts of raw data can be difficult without specialised tools and (often expensive) skills to organise and catalogue the data. Compared to the traditional use of data warehousing, some organisations may find they do not have sufficient in-house data science expertise or the physical infrastructure to develop effective data lake solutions. This could, they predict, result in higher costs and a high time-to-market, resulting in years before benefits can be realised.

The Solution to Data Warehouse Challenges

However, organisations should consider the multiple advantages of data lakes over data warehouses in the context of current digital transformation trends and the adoption of machine learning processes and techniques if they want to remain competitive in their industry.

While data warehouses provide a familiar interface for business users, data warehouse solutions are expensive, complicated to make changes to, lock companies into specific vendor solutions, and cannot deal efficiently with unstructured data.

Unlike data warehouses, data lakes offer flexible, scalable solutions that, when implemented on an automated data processing platform, eliminate the perceived disadvantages of high skills requirements and a costly infrastructure. The platform provides the infrastructure as-a-service, and the skills to maintain it. Data lakes are also highly accessible and easy to update, providing increasingly advanced levels of data lake maturity, from simple data reservoir to interesting exploratory tool to complete big data analytical solution.

Harnessing the power of AI transformation

Critically, unlike data warehouses, data lakes allow the ingestion of raw data obtained from multiple disparate sources, necessary for machine learning application and the rapid development of AI solutions.

This can result in enormous benefits to an organisation, including increased profits and efficiency and greater customer satisfaction. To put this into perspective, where it would take a data warehouse system 24 hours to create a data model for machine learning, the same process could take a data lake system 24 minutes.

The value of data lake in AI transformation

Data Lakes – a Modern Solution to Modern Problems