With data dependency at an all-time high for businesses, almost every industry player is looking to establish themselves as a technology company first, similar to Amazon or Netflix. Organizations have pushed the gas pedal on their digital transformation journey, and now understand that they need to be mindful of how they integrate and manage enterprise data that is distributed, still easily accessible, trusted, and governed.
This has prompted the advent of modern data integration styles like data virtualization, as industries of all kinds and sizes look to accelerate change and leverage data effectively.
Expect to see the following five trends make their mark in 2022:
1. Data fabric will become the foundation for the distributed enterprise. As digital businesses and online sales channels proliferate and remote work becomes the norm, it creates a complex and diverse ecosystem of devices, applications, and data infrastructure. In particular, data infrastructure can span on-premises, single cloud, multi-cloud, hybrid-cloud, or a combination of these, spread across regional boundaries with no single solution to knit all this data together.
In 2022, organizations will create a data fabric to drive enterprise-wide data and analytics and to automate many of the data integration, preparation, exploration tasks. Data fabric unifies the data assets distributed across disparate locations, formats, and latency using logical, physical, or hybrid approaches. By enabling organizations to choose their preferred approach, these data fabrics will reduce time-to-delivery and make it a preferred Data Management approach in the coming year.
In fact, according to a recent TEI study by Forrester, “Data fabric technology takes data virtualization a step further by automating data management functions using artificial intelligence/machine learning and providing additional semantic capabilities through data catalog, data preparation, and data modeling.”
2. Decision intelligence will make inroads for enterprise-wide decision support. Organizations have been acquiring vast amounts of data and need to leverage that information to drive business outcomes. Decision intelligence is making inroads across enterprises, as regular dashboards and BI platforms are augmented with AI/ML-driven decision support systems. Decision intelligence is the combination of regular BI dashboards enhanced with AI/ML, whereby enterprises can make predictions of outcomes for a certain set of actions and recommend one action over the other, thus helping the decision support system.
In 2002, decision intelligence has the potential to make assessments better and faster, given machine-generated decisions can be processed at speeds that humans simply cannot. The caveat: Machines still lack consciousness and do not understand the implications of the decision outcome. Look for organizations to incorporate decision intelligence into their BI stack to continuously measure the outcome to avoid unintended consequences by tweaking the decision parameters accordingly.
3. Data mesh architectures will become more enticing. As organizations grow in size and complexity, central data teams are forced to deal with a wide array of functional units and associated data consumers. This makes it difficult to understand the data requirements for all cross-functional teams and offer the right set of data products to their consumers. Data mesh is a new decentralized Data Architecture approach for data analytics that aims to remove bottlenecks and take data decisions closer to those who understand the data.
In 2022 and beyond, larger organizations with distributed data environments will implement a data mesh architecture. As different functional units or domains within larger organizations have a better understanding of how their data should be used, letting the domains define and implement their own data infrastructure results in fewer iterations until business needs are met and are of high quality. This also removes the bottleneck of the centralized infrastructure and gives domains autonomy to use the best tools for their particular situations. Data mesh will create a unified infrastructure enabling domains to create and share data products while enforcing standards for interoperability, quality, governance, and security.
4. Organizations will embrace composable data and analytics to empower data consumers. Monolithic architectures are already a thing of the past, but we can expect even smaller footprints. As global companies deal with distributed data across regional, cloud, and data center boundaries, consolidating that data in one central location is practically impossible. That’s where composable Data Architecture, whereby organizations can pick and choose certain tools to build parts of or the entirety of their data infrastructure, becomes paramount and brings agility to data infrastructure. A good example of a composable architecture is a data fabric, which can be created using a data catalog tool, a semantic tool, a data integration tool, and a metadata tool put together.
Data Management infrastructure is extremely diverse and usually, every organization uses multiple systems or modules that together constitute their Data Management environment. Being able to build a low-code, no-code data infrastructure provides flexibility and user-friendliness, as it empowers business users to put together their desired Data Management stack and makes them less dependent on IT.
In 2022, expect organizations to accelerate building composable data and analytics environments, whereby organizations can avoid vendor lock-in and attain more flexibility as they put together their data infrastructure stack that meets their need.
5. Small and wide data analytics will begin to catch on. AI/ML is transforming the way organizations operate, but to be successful, it is also dependent on historical data analytics, aka big data analytics. While big data analytics is here to stay, in many cases this old historical data continues to lose its value.
In 2022, organizations will leverage small data analytics to create hyper-personalized experiences for their individual customers to understand customer sentiment around a specific product or service within a short time window. While wide data analytics, which entails combining structured, unstructured, and semi-structured data from various data sources for analytical purposes, is comparatively a new concept and yet to find widespread adoption – given the pace at which organizations are making use of geospatial data, machine-generated data, social media data, and a variety of other data types together – expect to see small and wide data analytics gain better traction across organizations as we enter into the new year.