At a time when the future can be hard to predict, organizations are recognizing the need for business-ready data that they can trust to make critical strategic decisions. Increased demand for a more agile, future-ready business has many ramping up investments in data management solutions for analytics, AI/ML, and other digital transformation initiatives.
In both the industry and here at Syniti, we’re seeing demand for innovations in cloud-native data management solutions, data fabrics, active metadata management, and others, rising up in support of an increasingly demanding and empowered workforce. Here are some key data trends we expect to see more of in 2022:
1. Data Fabric – Less Siloes, More Connection
Managing data access, security, integration, and usage across on-prem and multicloud environments is a challenging task. Too often when it comes to data, companies rush to adopt new technologies, build new data warehouses, and source data from throughout the business, only to create more silos rather than break them down.
The advent of data fabrics seeks to change that paradigm by simplifying how data is accessed and managed through a unified data architecture and management tooling layer. More than just management of data, data fabrics also help data teams understand how data is being stored, curated, and consumed; for example, if a user accesses a particular type of data frequently, that consumption can be tracked at the landscape level. Leveraging advancements in Artificial Intelligence and Machine Learning (AI/ML), a data fabric can then personalize resource recommendations end users might otherwise have been unaware of, providing a more directed experience. This lends itself to the democratization of data.
The ability to go out and understand how data is moving and changing within the landscape on an automated, enterprise-level, while also governing access, usage, and security, supports a more agile business and intelligent consumption of data.
2. Active Metadata Gets to Work
Metadata is at the core of understanding and sharing information about the data within the systems that live at the enterprise level. It can often be a somewhat static process, scanning and profiling a system once, putting it into a data catalog, and calling it done.
Active metadata, on the other hand, is a continuous process, seeking to discover changes that are occurring within the landscape in real-time. Changes in consumption, new data insights, and the landscape of data consumers, even changes in the system landscape, can all be detected in a dynamic way.
To make active metadata work, you need approaches such as AI/ML-powered insights engines, capable of traversing metadata assets stored in graph database technologies. Rather than “scan, store, and call it done”, the system must continually uncover new relationships and insights across assets so they can be understood and actioned by business users.
3. Cloud Platforms and Low-code Development for the Agile Business
Successful enterprises are always looking for ways to be more agile, develop new business models, respond to changing market conditions, grow revenues, and stay ahead of the competition. These businesses are increasingly turning to advancements in cloud platforms such as Microsoft Azure, Google Cloud Platform, and Amazon Web Services. Not only are they moving their application infrastructure, but they are also extending those applications with advanced application services that can be used to build extensions or new applications that go beyond what they could do in their old infrastructure.
Traditionally, moving an enterprise’s entire application infrastructure, reporting, and analytics to a cloud platform was very time-consuming and costly. Advancements in automating the migration of data and these mission-critical applications have made the move to a cloud platform a key strategic priority for many businesses. Rather than static applications that require a lot of coding and development, companies are staying agile by moving towards a low- or no-code environment that’s easily kept up to date.
There’s no place in today’s world for static information and applications; global enterprises need their applications to adapt as the business adapts, and for their business users to easily access business-ready data to drive business outcomes. That is a much more attainable goal when leveraging cloud-native applications running on a modern cloud infrastructure. With this, businesses are able to take advantage of new, sophisticated technologies that allow users to focus on business challenges rather than application limitations. In this way, the application landscape is able to constantly evolve as the business evolves.
4. The Complexity of Today’s ‘Hyper-Automated’ Enterprise
Or... “Hyperautomation”: Streamlining Workflows’ Wherever Possible
In an increasingly remote-first or remote-centric workforce, companies are also focusing investments in software and platforms to help connect a more distributed company across virtual and in-person touchpoints. From global enterprises to governmental agencies, organizations are looking to AI-enabled solutions that automate mundane tasks, workflows, and improve decision making. The demand for hyperautomation is also being driven by the demand for accelerated growth and operational excellence while also lowering costs.
In response to this rise in “hyperautomation”, more and more regulatory requirements have also come about to keep in check how AI/ML is used and how it impacts our lives as citizens and workers. Individuals are demanding a higher level of transparency when it comes to how automation is used, as well as the potential for incorporation of personal information into this automated decision-making. This has added another layer of complexity when it comes to the adoption of AI and automated solutions for companies.
As leaders navigate this new, more complex landscape, the right design and data governance strategy will be necessary in order to securely and safely interact with employees, third parties, vendors, and customers alike, all while ensuring what’s being automated is valuable, timely and relevant.
5. The Fully-Sustainable Business Model
Environmental impact is increasingly a priority for many companies and their people. Large investment decisions, business partnerships, and buying behaviors may be impacted by a particular businesses’ approach to sustainability and the environment. Increasingly, Enterprise Application vendors and Global System Integrators are trying to help their customers take advantage of these trends by developing new application functionality, reporting, and analytics that support environmental decision-making. For example, when it comes to purchasing decisions, companies can now review how products are manufactured and distributed or how materials are sourced based on environmental impact. In this way, enterprises can dramatically reduce their eco-footprint by simply re-evaluating the decisions they make every day.
As application functionality and the availability of environmental data and reporting standards improve, companies will be able to prioritize their environmental impact, gaining more insight and control over their entire lifecycle from start to finish and rethinking what it means to have a net-zero, sustainable business model.
In Conclusion
As we look to the year ahead and re-assess the drivers from years past, it’s evident that one thing remains true: Shining a brighter light on the way data moves and changes throughout the company is crucial to the success of the business and its workforce.
Yet initiatives in data and technology aren’t always as simple as buying a “tool”. It comes down to having a vision, the strategy, and the right partners to help you get there. Without well-defined best practices in place, even seasoned leaders can find themselves only adding to already-siloed infrastructures with solutions that cannot scale. With these new initiatives in the data and technology space, the right partnership is crucial to implementing these complex advancements strategically and safely.