Data modernisation is rethinking how you use data and analytics as an organisation. It involves moving from legacy databases and architectures to modern, cloud-based platforms and scalable architectures, and migrating to next-gen analytics tools.
It’s about uniting data from across the entire enterprise, making it trustworthy, understandable, trackable, and involving introducing fresh processes, using new tools, and champions to see cultural changes through.
The onslaught of massive amounts of data from every step of an organisation’s activity, including product development, manufacturing, supply chain, operations, sales, and customer support, requires better and more advanced analytics, as organisations need to act quickly and efficiently to meet demands. Modernising data analytics allows organisations to easily adapt to changes, evolve and support their digital business transformation.
Organisations can scale, be flexible, integrate new data sources, get insights quicker, democratise data, and effectively plan for the future of business by modernising their data analytics.
Invest in building infrastructure
But often, when modernising existing data analytics platforms, organisations are reluctant to give up on a single database used in their legacy system, the ETL processes, stored procedures, and reporting tools that are part of it. However, it’s important to take a long term perspective and invest in building scalable infrastructure, and grow business without technical limitations.
Here are a few key principles that an organisation should keep in mind:
- Data analytics platform should increase productivity through a simple and secure data experience. Users must be able to easily utilise a self-service app store to download the libraries, pre-configured templates, or certified ISV solutions they want to use with single-click download and deployment.
- Automate provisioning of tools/libraries/frameworks so users can get to work quickly.
- Simplify data access through a converged file and object system. The data fabric should support files, objects, streams, and databases; ingest and transform the data into a single, persistent data store.
- Have an open-source foundation that allows data teams to pick up and drop their work onto any infrastructure — on premises, cloud, or edge.
According to experts, the first step an organisation should take to modernise a data analytics platform is to allow all data to land in its raw form from every source with little modification or filtering. The data can come from IoT devices, streaming sources or web services interactions. The data should not be organised, or harmonised to have the same format of the address or product ID. This is important to make the data analytics platform flexible and grow.
Data evolves as more and more data comes in. The evolution is based on the data availability, business usage of the analysis output, such as machine learning predictions or analytical reports. Every analysis looks at the data from a different angle, and for a different business need, it is developed by a different team. For instance, sales analytics is different from marketing analytics or logistics analytics. The transformations of the data from its raw form toward an analytical insight allow tremendous optimisation opportunities.
The next step of modernising the platform is aggregating, filtering, or transforming data from its original raw form to fit a specific business question. For predicting the daily sales of a brand, data teams don’t need to analyse every individual purchase for every unique product, they can look at daily and brand aggregation.
Treating it as cache gives the required flexibility and cost-effectiveness to build analytical use cases within an organisation. Understandably, it takes time to transform companies to use data more efficiently, and it must be planned for scale, and simplicity.
New technology keeps coming, changing the modern architecture. A decade ago, it was Hadoop that opened up scalable opportunities to handle a large amount of data. Then it was Spark with faster big data processing, better SQL and newer functional programming languages. Experts recommend writing data transformation logic using SQL based on PrestoDB as fewer bugs can sneak into the code, and many more people can write the logic using it.
There’s no one-size-fits-all
Most importantly, the modern data analytics platform must scale across clouds to enable any business user to answer any question at any time against any data. For that, enterprises must modernise their cloud analytics architecture, democratise data and analytics across line-of-business users, developers, and data scientists.
There’s no one-size-fits-all when building solutions for real-time data processing. Some enterprises run operational analytics workloads that require low latency and quick response times, while others run decision and support workloads that demand very high throughput, with different computing power needs. A modern cloud architecture should offer agility with infinite elasticity, self-service capabilities to keep workloads running smoothly at scale.
Another step in the data analytics architecture journey is to modernise data management, which means breaking down data silos. Business users must have the ability to query real-time data across deployment models to augment their analytics with ease.
A data fabric, generally a custom-made design that provides reusable data services, pipelines, semantic tiers or APIs via combination of data integration approaches, orchestrates on-demand data access at scale across multiple platforms in the analytics environment. Users can initiate queries from any linked platform and access any system it is connected to, combining data from multiple platforms in a single query. Data fabrics can be improved by adding dynamic schema recognition or even cost-based optimisation approaches and other augmented data management capabilities.
Using open data formats also makes collaboration easier; understanding and cataloguing data sources and existing models before starting queries and new modelling speeds time to value. Also ensuring that decentralised data is secure and protected gives enterprises and their customers peace of mind that their data won’t end up in the wrong hands.
There’s no doubt that data analytics fuel the enterprises, but modern analytics require better partner integration, allowing companies to integrate models more deeply, as well as exchange data between cloud-native services, analytics workbenches, and different analytics partners.
Organisation should look for platforms, or build, that enable tight integrations to maximise performance by minimising data movement and parallel computation.
Modern data analytics platforms must have the ability to add, replace, scale, and remove any part of it when needed by the business. As long as the IT teams are able to learn new and better technologies, they can build and operate a powerful and modern data analytics platform.
Now more than ever, enterprises are leveraging data as its most valuable asset, driving both real-time decision-making and innovations. But to achieve that will require leveraging a modern data analytics platform that is scalable, and unifying all data in a means that is fully available for analytics. Modern data analytics platforms are an essential part of every organisation that wants to stay relevant and competitive.
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