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Maximising Analytics & Machine Learning with Data Lakes

Maximising Analytics & Machine Learning with Data Lakes

Is your organisation struggling to keep up with massive data volumes? Perhaps it's time to embrace the Data Lake!

In today’s digital-first landscape, organisations are facing an ever-growing volume of data. This data can come from a variety of sources, including internal systems, external data sets, and user activity and trends. To capitalise on the valuable insights and competitive advantages that such data can provide, organisations need to have a robust and flexible data infrastructure in place.

One potential solution is the Data Lake. A Data Lake is fundamentally a large storage repository that can accommodate vast volumes of disparate data types. Its key advantage lies in its flexibility – unlike traditional storage solutions, which often require rigid schema specifications for each stored dataset, a Data Lake allows for very granular control over the exact type and format of the stored data. This makes it possible to store different datasets in their raw form, without having to them pre-processed or structured in any special way.

Another key benefit of using a Data Lake is that it allows for sophisticated analysis techniques like machine learning and analytics to be used effectively on large volumes of continuously growing data. By making it easier to retain all available relevant information in one place, organisations can reap the benefits of powerful analytical tools without being constrained by restrictive storage limitations or intensive computational demands. Ultimately, by choosing to

As of last year, global demand for Data Lakes is predicted to grow by 27.4%.

The Origins of Data Lakes

The term ‘Data Lake’ was first coined by Pentaho CTO James Dixon in October 2010. They were originally built using on-site file systems, but these proved difficult to deploy since the only way to increase capacity was adding physical servers. This made it difficult for organisations to upgrade their systems and increase capacity.

However, since the early 2010s, the rise of Cloud-based services has enabled companies to build and manage Data Lakes without having to build costly on-premises infrastructures.

Data Lakes are now a trusted and established form of architecture in the world of data science, advanced analytics, and digital-first business. Many organisations are rapidly re-platforming their Data Lakes, abandoned legacy platforms and remodelling data.

“If you think of a Data Mart as a store of bottled water, cleansed and packaged and structured for easy consumption, the Data Lake is a large body of water in a more natural state. The contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.” – James Dixon, CTO of Pentaho

Why do Businesses need Data Lakes?

The onset of the COVID-19 pandemic has accelerated the drive towards data reliance. Without a Data Lake, organisations will struggle to get ahead in sales, marketing, productivity and analytics.

Why do digital businesses need Data Lakes?

 The onset of the COVID-19 pandemic has accelerated the drive towards data reliance. Without a Data Lake, organisations will struggle to get ahead in sales, marketing, productivity and analytics.

Power & integration

Data Lakes allow organisations to convert raw unstructured data into standardised, structured data, from which they can apply data science, machine learning and SQL analytics with minimal latency. As aforementioned, Data Lakes can seamlessly integrate a broad range of data formats and sources including binary files, images, video and audio. Any new data that arrives at the lake will be up to date.

Centralisation & Democratisation

Centralisation ensures data security and eliminates the risk of duplication and collaboration problems. With a centralised Daata Lake, downstream users will know exactly where to look for all required sources, saving them time and boosting efficiency. The flexibility of Data Lakes enables users from a wide variety of skills backgrounds to perform different analytics tasks in unison.

Sustainability & Affordability

Data Lakes are sustainable and affordable because of their ability to scale and leverage object storage. Furthermore, in-depth analytics and machine learning on flexible data are currently among the highest priorities for organisations. The prediction capability that comes from flexible Data Lakes can drastically reduce costs for your organisations.

6 Key Benefits of Data Lakes

Limitless Scalability

Data Lakes empower organisations to fulfil any requirements at a reasonable cost by adding more machines to their pool of resources. This process is known as ‘scaling out’.

IoT integration

Internet of Things (IoT) is one of the key drivers of data volume. IoT device logs can be collected and analysed easily.

Flexibility

Did you know that 90% of all business data comes in unstructured formats? Data Lakes are typically more flexible repositories than structured data warehouses, meaning companies can store data in whichever way they sit fit.

Native Format

Raw data such as log files, streaming audio and social media content collected from various sources is stored in its native format, providing users with profitable insights.

Advanced Algorithms

Data Lakes allow organisations to harness complex queries and in-depth algorithms to identify relevant objects and trends.

Machine Learning

Data Lakes enable integration with machine learning due to their ability to store large and diverse amounts of data.

Best Practices

Lakehouse architecture brings data science, traditional analytics and Machine Learning under one roof. What are the best practices for building your Data Lake?

Top tips for building your Lake House:

  • Make your Data Lake a landing zone for your preserved, unaltered data.
  • To remain GDPR-compliant, hide data containing personally identifiable information by psuedonymising it.
  • Secure your Data Lake with view-based ACLs (access control levels). This will ensure better data security.
  • Catalogue the data in your Data Lake to enable service analytics.

In order to avoid a data swamp, an organisation must have a clear understanding of what information it is trying to collect and how that data will be used. Without a clear strategy in place, it can be difficult for an organisation to scale up successfully and meet the various demands of its stakeholders. That’s why it’s important for organisations to embrace modern Data Lake designs that are capable of meeting the demands of today’s data-driven world. By incorporating artificial intelligence and up-to-date data integration techniques, organisations can gain more accurate insights from their vast troves of data. Additionally, effective DevOps practices and strict regulations to manage data wildness are essential to ensuring the long-term success and sustainability of an organisation. With these key strategies in mind, businesses can navigate the complexities of the modern data landscape with confidence and thrive in our increasingly digital society.

Preparing for Tomorrow

Did you know that 90% of all data ever has been generated since 2016? To maximise your Data Lake value in the long term, you must make sure that it has enough capacity for future projects. This will mean expanding your data team. With Agile developers and DevOps processes, your organisation will be able to run a smooth and viable operations that manages the thousands of new data sources that come your way.

Eventually, your Data Lake may need to run on other platforms. If like most organisations, your company uses a multi-Cloud infrastructure, then your Data Lake will need a future-proof, flexible and Agile infrastructure. Using data vault methodology is the best way to ensure the continuous and steady onboarding of new data. It is good practice to store data in open file and table formats.

There are many different methods that Agile organisations can implement to increase their Sprint Velocity. Done correctly, the combination of Sprint Velocity and high-quality software technology has the potential to help your organisation double its efficiency and productivity. It will also help you avoid overpromising your clients and stakeholders on product and service delivery. The ability to move quickly is paramount but going that extra mile to plant the seeds of efficiency is key to ensuring productivity and sustainability in the long term.