Data volumes continue to grow exponentially, forcing organisations to re-think their infrastructure architectures in order to stay on top of their data.
Many organisations have already, or are planning to embark on various initiatives to revamp their data infrastructure to cope with the avalanche of data, mostly unstructured, coming from new sources.
At the centre of these initiatives is data modernisation − a proactive multi-step process of upgrading an organisation's data infrastructure using modern technologies to consolidate previously siloed data, making it available for analytics and to power new services like artificial intelligence and machine learning (ML).
Organisations modernise their infrastructure to move away from outdated legacy systems, with their limitations, to modern platforms designed to meet current data challenges.
Modernisation should take a holistic view of the organisation’s data landscape. The goal is to have a modernised, intelligent data platform that brings together data integration, data warehousing, big data analytics, ML and data governance all in one place.
Achieving and reaping the benefits of a modernised data estate requires a foundational shift in the way organisations operate.
In this Industry Insight – the first of a two-part series − I provide some insight into key considerations of a data modernisation strategy and the benefits of such a strategy. Let’s jump in…
A data modernisation strategy
Achieving and reaping the benefits of a modernised data estate requires a foundational shift in the way organisations operate. Business leaders must view data as a strategic asset to drive innovation and create new revenue streams.
For data modernisation to be successful, the underlying data model and architecture must support prompt delivery of high-quality data to data consumers.
The following five steps can help organisations deliver and continuously optimise their data architecture.
Step one: Data estate analysis
To move to a desired state, it is important to start with assessing the current data estate of the organisation. Involve all stakeholders to ensure all departments across the enterprise are part of the initiative. Focus on understanding high-level data issues the data consumers are facing and together come up with ideas to design a better future-proof data architecture.
Step two: Data architecture and model assessment
Assess the current data architecture to see if/how it can be redesigned to take full advantage of the target modern data platform. Identify current and future use cases to ensure the new architecture supports them. Design a unified data model that combines all disparate models for improved efficiencies.
Step three: Target architecture design and engineering
Design the overall target architecture detailing technologies, platforms and design patterns to be used. Develop pipelines to ingest, transform and store data. Based on business requirements, proceed to create data lakes, data marts and other services needed to implement a modern data architecture. Use latest fit-for-purpose tools to build a robust modern architecture.
Step four: Reporting and analytics
Now business insights can be delivered from the data. This can take a form of data analytics using modern tools to sift through big data sets to derive insights, or take a structured approach that is driven by a well-designed data model to deliver data visualisations via dashboards and support self-service capabilities.
Step five: DataOps
DataOps helps organisations reap the rewards of a modern data architecture. DataOps is a collaborative data management method that focuses on faster delivery of insights and data by automating the data delivery design and management. It promotes continuous improvement across the data value-chain to ensure organisations realise value from their data initiatives.
The benefits of data modernisation
A modernised data infrastructure brings with it new capabilities that can help organisations manage and use their data assets better. Below are some of the benefits of data modernisation:
Efficient data processing: Enterprise data comes from multiple sources and this data needs to be collected, cleaned and aggregated for business consumption. Data modernisation reduces the time it takes to have high-value data available to data users for analysis.
Improved data access: In the post-pandemic era, remote work will continue in one form or another. Data modernisation improves data accessibility and ensures high levels of data security that restricts access to sensitive data, while allowing people adequate access to do their jobs.
Business growth: Data modernisation introduces standardisation across the enterprise. Data allows businesses to track various key performance indicators (KPIs) that are used to measure business performance. KPIs create a common language around data and when supported by high-quality data can provide valuable insights to help organisations grow.
Faster decision-making: Data modernisation allows appropriate real-time access to data for self-service analytics in a collaborative environment. This allows for faster, more informed decisions using the most current data.
Modern data storage: Data modernisation enables organisation to get to a point where their data estate can cope with all forms of data, whether structured or unstructured. The modern data platform has the ability to scale and cope with constantly growing datasets, processing them in real-time to deliver insights on-demand.
As the above is already quite a bit of insight to work through, do keep an eye out for my next article, as I will address the challenges of legacy system modernisation – a consideration many organisations tend to look at instead of a data modernisation strategy. And with that, I will also touch on the risks of not modernising data infrastructure. Until next time…
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