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Revolutionizing Data Analytics: Strategies for Business Modernization

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Data modernization is rethinking how you use data and analytics as an organization. ​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, and 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 organizations need to act quickly and efficiently to meet demands. Modernizing data analytics allows organizations to easily adapt to changes, evolve, and support their digital business transformation.

Organizations can scale, be flexible, integrate new data sources, get insights quicker, democratize data, and effectively plan for the future of business by modernizing their data analytics.

Invest in building infrastructure

But often, when modernizing existing data analytics platforms, organizations 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, invest in building scalable infrastructure and grow the business without technical limitations.

Here are a few key principles that an organization should keep in mind:

  • Data analytics platforms should increase productivity through a simple and secure data experience. Users must be able to easily utilize 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 organization should take to modernize a data analytics platform is to allow all data from every source to land in its raw form with little modification or filtering. The data can come from IoT devices, streaming sources, or web services interactions. The data should not be organized or harmonized to have the same format as 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 optimization opportunities.

The next step of modernizing 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 analyze every individual purchase for every unique product; they can look at daily and brand aggregation.

Treating it as a cache gives an organization the flexibility and cost-effectiveness to build analytical use cases. 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 modern architecture. A decade ago, Hadoop opened up scalable opportunities to handle large 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 modernize their cloud analytics architecture and democratize 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. In contrast, 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 modernize data management, which means breaking down data silos. Business users must be able to easily query real-time data across deployment models to augment their analytics.

A data fabric, generally a custom-made design that provides reusable data services, pipelines, semantic tiers, or APIs via a 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 connected system, combining data from multiple platforms in a single query. Data fabrics can be improved by adding dynamic schema recognition, cost-based optimization approaches, and other augmented data management capabilities.

Open data formats also make collaboration easier; understanding and cataloging data sources and existing models before starting queries and new modeling speeds time to value. Also, ensuring that decentralized 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 enterprises, but modern analytics require better partner integration, allowing companies to integrate models more deeply and exchange data between cloud-native services, analytics workbenches, and different analytics partners.

Organizations should look for platforms or builds that enable tight integrations to maximize performance by minimizing data movement and parallel computation.

Modern data analytics platforms must be able to add, replace, scale, and remove any part of it when needed by the business. If the IT teams can learn new and better technologies, they can build and operate a powerful and modern data analytics platform.

More than ever, enterprises leverage data as their most valuable asset, driving real-time decision-making and innovations. However, achieving that will require leveraging a scalable modern data analytics platform and unifying all data in a means that is fully available for analytics. Modern data analytics platforms are essential for every organization that wants to stay relevant and competitive.

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