How to Future Proof Your Data to Make It More Valuable with Emergent Technologies – Part 2

This article on future proofing your data was originally published in Enterprise Tech Journal. Part one of this two part post focused on data application and system lock-in as well as the importance of future proofing your data. This part explains the benefits of liberating data from applications and how future-proofing powers new analytics.

How Can You Liberate Data from Applications?

To future proof their data, companies need a virtual data access layer. This layer also needs to be more abstract than the data layer common in most companies’ application stacks right now.

In the data access layer, companies need to be able to standardize their data so it can be managed across the entire business and not just in an individual cluster. The result is a uniform way of accessing data, regardless of the technology used to generate or store it, making it accessible across the entire business. Otherwise, the data isn’t truly accessible or shareable. In such instances, companies risk data application and system lock-in, where the data is only available within a given cluster, and not for the entire operation.

However, even that the new abstraction layer cannot be tied to specific technologies because of how that data layer is implemented will likely need to be as flexible as the data and repositories themselves going forward. No one can predict technology needs five or ten years in the future. Thus, creating a data layer that is abstracted away from specific technologies is the safest strategy.

To create this abstraction layer, companies could use generic APIs that allow new data to be plugged into the larger stack whenever it comes online. Connections between a company’s data and their stack should not be one-off integrations between applications; rather, connections should be abstracted through an interface similar to an API layer that allows for flexibility despite the complexity. Software tools can hide the underlying complexities of the storage and compute architectures, and the ones that run natively across multiple platforms, multiple compute frameworks, on-premise, and in the cloud will provide that ultimate flexibility and future proofing.

With a data abstraction layer, the ability to integrate multiple repositories in multiple locations and provide easy access is greatly facilitated. Such a layer also helps if companies have multiple data lakes. It allows them to take advantage of the dynamic nature of the open source stack and all the innovation that is occurring within it. With an abstraction layer, companies can be confident that they can create an application and that it will still be able to run and provide data in whatever their next iteration of the technology stack looks like. This is critical at a time when data delivery methods are changing very rapidly and providing real-time insights is essential. An abstraction layer also allows data to be fed into downstream applications and governed and archived in a straightforward way.

Future-Proofing Also Powers New Analytics

This abstracted data layer has many corollary benefits aside from just allowing companies to future proof their data and avoid data application and system lock-in. Liberating your data opens the door to harvesting more value from the data itself – especially when it comes to feeding that data into new, more powerful analytics.

Increasingly, as technology evolves, the data pipeline within a business will have to feed into AI and ML. AI and ML truly only function properly when drawing on lots of data. Thus, if a company has much of its data locked into a certain application or product, the results from AI will be partially formed or even misleading. To get the most out of AI and ML, companies need to train these systems with a variety of historical datasets. Freeing one’s data means that AI is more reliable and better able to help the business.

Delivering trusted data is critical for business and operational analytics pipelines. With ML and AI, delivering trusted data is even more critical. This is where data quality tools play a crucial role. And, just like companies need to ensure the data access and integration layer can operate across platforms and cloud resources, they must ensure the data quality tools in the stack are also enabled for future-proofing. Only then can they be confident that their AI and ML initiatives are fed with high-quality data, now and in the future.

New types of analytics, both using AI and ML and in other forms, are emerging on the market, and liberated data means companies can take advantage of all their rich and diverse data to drive more insights. This can include deep learning and social network analysis, along with graph and iterative algorithms. More data makes all of these analytic forms better. The better a company is at breaking down its data silos, the better it becomes at leveraging new data sources as well as historical and critical data assets locked in legacy platforms.

By breaking the connection between the application that generates the data and the data itself, companies can create an abstraction layer that allows all of their systems and data to be migrated and integrated more easily. This is highly beneficial for the business.

The reason liberating data and accessibility are so important is that by achieving this, companies can generate business insights they weren’t able to get before. All their data is integrated and put to use, rather than just some of it. Data that is liberated can be combined in new ways, marrying data sources from different systems in order to enrich it and provide new levels of insights.

Liberating Data Improves the Entire Business

By liberating data from applications, companies can accelerate the adoption of new technologies and make their existing systems more powerful because they’re drawing on greater pools of data for business and analytic insights.

Consequently, future-proofing is an opportunity for the organization to abstract away not just data, but complexity as a whole within its technology stack. That can mean less coding for those interested in analytics and business insights, as liberated data means people have the resources to take on more independently, without the intervention of IT.

Future-proofing arises not just from ensuring that data is not captive to a specific repository, but also from having as much of that data as possible abstracted in a way that is durable and accessible.

Future-Proofing Isn’t Easy – But it’s Worth the Effort

Creating an abstracted data layer is the best way for companies to approach their need for liberated data but that doesn’t mean that it’s an easy process. However, by liberating data, companies can take advantage of data today, working with today’s technology stack and infrastructure, while also positioning themselves to continue to take advantage of that data in the future. Keeping data in silos means the business will never be able to fully leverage the emerging analytics environment and AI to experience radical new business insights.

The process of moving toward liberated data can happen over time. There’s nothing preventing companies from liberating one application or dataset at a time. What’s crucial is that however the process occurs, the abstraction layer for data is designed in such a way that allows flexibility in integrating it with all other liberated data in the future.

The most valuable asset going forward is data. Ensuring that you can access and trust all your data in the face of changing technology enables you to unlock the value of that data through advanced analytics, machine learning, and AI, providing real-time insights and creating a competitive edge.

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