What to look for when modernizing the Data Lake

Whether a company is born into the digital world or has a more traditional business, they must invest and excel in tech advances such as mobility, cloud computing, and most importantly, advancedanalytics and data science.

Data is the new water of the digital economy”.

In today’s digital-first world, businesses – irrespective of their size and nature – use data for several key purposes such as studying market trends, understanding customer behaviour and preferences, and to amalgamate all these deep insights to create products and services that fit customers’ aspirations.

But how do they perform this task?

While data is abundant, the process of gathering, storing and working with data is an uphill task. This is because most of the data amassed from various sources like social media, digital platforms, surveys and IoT devices, among others, is in a raw and unstructured format.

This means a large amount of data goes unused and this makes it challenging for organizations to gain value from all its data. This is where a data lake comes to the picture.

What’s a data lake?

A part of any company’s data management system, a data lake acts as a centralized repository for all data – whether structured, semi-structured and unstructured, and from any source. Data is primarily stored in a raw format, usually without a defined purpose at the time of storage.

A data lake architecture stores data in its original form so that it can be used later. This makes data lakes an ideal platform to eventually feed AI, Machine Learning (ML) and data science initiatives.

The need for data architecture modernization

Whether a company is born into the digital world or has a more traditional business, they must invest and excel in tech advances such as mobility, cloud computing, and most importantly, advancedanalytics and data science.

Doing so will equip them with the right tools to innovate their existing operations and deliver a seamless experience to customers. However, it isn’t that easy to achieve this goal.

To realize the benefits of advances in technologies, organizations must leverage all their data. This requires modernizing their data architectures. In other words, organizations must unlock andmigratetheir data from multiple, heterogeneous systems including legacy mainframe systems and enterprise applications, and quickly process and refine it for consumption in AI and ML initiatives.

Modern, cloud-based data lakes provide enterprises the agility and flexibility they need to store and process massive volumes of diverse data.

Things to keep in mind when architecting a modern data lake

Flexibility & Agility: Data architectures are constantly evolving. Companies are adding new sources of data, offloading data to new target systems for processing and refining, and adding new analytical tools and solutions to their technology infrastructure.

Cloud platform providers are also evolving and adding to their technology stack. In such an environment, ensure you have the flexibility and agility to adapt to unexpected changes in the data architecture.  

Automation: The current pace of business demands speed to insights. Manually designing, configuring and managing data lake pipelines, especially as the number of data sources continues to increase can be time and resource intensive.

Even after you ingest the data, processing and refining raw change data to create consumption-ready data can be slow, code intensive and error-prone. Automating data lake pipelines all the way from real-time data ingestion, to creation and provisioning of analytics-ready datasets is critical to realizing faster ROI from your data lake investments.

Data Integrity and Trust: Data lakes run the risk of quickly becoming data swamps, if data is dumped without consistent data definitions and metadata models – or if consumers can’t quickly access and understand data, verify its origin and trust its quality.

The administrative burden of ensuring data accuracy and consistency can delay and even kill the most well-funded analytics projects. Hence, ensure your data lake creation solution comes with an integrated cataloguing capability for automated metadata generation, supports source-schema change propagation, and persists change history for end-to-end data lineage.  

IT and Business Alignment: For success of data lakes, alignment between IT and business users’ needs is critical. While IT needs the ability to quickly design and configure data lake pipelines, create analytics-ready data and ensure data security and governance, business users need the ability to quickly find, understand and self-provision data so they can action on it.

Companies must, therefore, seek not only robust automation, security and governance capabilities for IT, but also data-consumer-friendly features like ‘search and publish’ for user self-sufficiency.

As an enterprise’s data lake evolves and matures with time and technologies, it will present more opportunities for innovation in products and services. Therefore, to ensure data lake truly provides a single source of trusted, analytics-ready data, organizations must keep in mind the aforementioned factors. The result will be faster business value for not just their internal stakeholders but also their customers.

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Ritu Jain, Director of Product Marketing – Data Integration Business, Qlik

  • Ritu Jain, Director of Product Marketing Data Integration Business, Qlik

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