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.
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.
Data Lakes – a Modern Solution to Modern Problems
"In our experience, an agile approach can help companies realize advantages from their data lakes within months rather than years. Quick wins and evidence of near-term impact can go a long way toward keeping IT and business leaders engaged and focused on data-management issues—thereby limiting the need for future rework and endless tweaking of protocols associated with populating, managing, and accessing the data lake." McKinsey
When should organisations be using data lakes?
For faster predictive and advanced analytics across multiple sectors, from health and finance to smart cities and marketing
To create experimental machine learning models and AI algorithms for testing new ideas
For applications where there are consistently high volumes of data
Where the nature of the data keeps changing (as in the case of the current Covid-19 pandemic)
Where the ingested data is raw and unstructured, or mixed
As a self-service tool for business users to create their own queries and reports
To create agile, data-driven applications
Financial Systems Use Case
Traditional proponents of data warehouse solutions, for financial systems, a data lake managed on a cloud-based platform can offer new opportunities in the industry, and:
Cut down the amount of time to create and deploy machine learning models for new and advanced banking practices, like self-service
Enable more secure centralised data storage
Support increasingly complex global compliance regulations
Allow machine learning analytics to create more accurate financial forecasts and risk assessments for different customer needs
Promote experimentation and innovation to provide new financial offerings
Use custom on-demand microservices to change the way banking is perceived, and used, by customers
Analyse billions of financial transactions faster
The Bluemetrix Approach
Innovation and advancement in industries like healthcare, finance, and mission-critical industries is hampered by the fragmentation of different types and uses of data across projects, organizations, and countries in their sectors. Different governance rules create additional challenges.
Bluemetrix has been building and deploying data lakes for over 400 enterprises since 2016, giving you unique insights into how to advance data lake maturity from a state of fragmentation to a state providing advanced analytical capabilities, according to the business needs of organisations. Bluemetrix provides the technology infrastructure, skills, tools, and services to create, manage, and maintain custom data lakes, so that enterprises are free to focus on the business bottom line.
Bluemetrix helps organisations to quickly and easily create data pipelines using automation tools to:
Ingest structured and unstructured data from any source
Validate and quality check ingested data
Secure and anonymise the data
Carry out governance and GDPR compliance on the data
Transform the data
Automatically update your Data catalogue with all operations carried out on the lake