The pandemic has accelerated digital transformation across businesses, which, in turn, has led to generation of massive data. When managed and leveraged in an appropriate way, data can be a goldmine for businesses to make smarter decisions.
For most of the businesses, supply chains are the lifeblood. Collaboration across various suppliers, processes, software has long led to creation of huge data off the back of even small-scale supply chains. Unfortunately, the data generated is useless in its raw form, however, when businesses leverage the power of analytics to create insights from them can help them in making informed decisions and run supply chain networks seamlessly.
Today, analytics is advancing at a breakneck pace, from developing innovations around the cloud, democratizing technologies, new AI capabilities and much more; various Analytics trends are gaining traction in the market.
With this horizon and data analytics technology on the upward path, it is critical to stay informed of what’s new and upcoming. Here we have compiled key supply chain analytics trends for 2022.
Supply Chain Analytics Trends for 2022
The COVID-19 pandemic has taught us that agility is essential to avoid significant supply chain disruption during these unprecedented times. Over the last two years, competitive advantage within supply chains has relied on the ability of all businesses to adapt quickly, embrace new technologies, and find innovative solutions.
Analytics that highlights the trepidations as they arise can help in achieving this much needed agility. However, a customized approach is required for businesses to focus on getting better insights from their software and align it with business outcomes.
AI and ML will become more intelligent
In recent years, Artificial Intelligence (AI) and Machine Learning (ML) space has been complex, with more companies entering the space than before. However, as we begin to enter a more mature space in 2022, more companies will invest in AI-driven automated insights. Low-code and no-code will democratize artificial intelligence. While data scientists will continue to focus on high-value issues, the participation will increase in advanced analytics utilizing automation, natural language processing, computer vision, and machine learning. More responsible AI will reduce the gap from design to innovation.
More businesses will move to the Cloud
While the migration to the cloud provides various opportunities and advantage to the businesses, such as scaling analytics processes, it also means they are bound to governance around data ownership, data access, and data control.
In 2022, analytics will finally cross the gap to the cloud. Adoption of Cloud technology is steadily growing as businesses seek to leverage the big data already available in the cloud repositories. These businesses are geared up to take the benefit of cloud native computing and reap the benefits of easier analytics access.
Data Fabric will become the base for the distributed enterprise
With online sales channels and digital businesses proliferate and remote working becoming a norm, a complex and diverse ecosystem of applications, devices, and data infrastructure is created. Especially, data infrastructure can extent on-premises, hybrid-cloud, multi-cloud, single-cloud, or a combination of these, extended across regional boundaries with no single solution to combine all this data together.
This year, organizations will create Data Fabric to drive organization-wide data and analytics to automate many of the data exploration, preparation, and integration. Data fabric will help in unifying the data assets spread across different formats, locations, and latency using physical, logical, or hybrid approaches. These data fabrics will enable organizations to choose their preferred approach that will lead to reduced time-to-delivery and make it a preferred Data Management approach in 2022.
Data Mesh architectures will become more appealing
As companies grow in size, central data teams have to deal with wide range of functional units and associated data consumers that makes it difficult to understand data requirements across cross-functional teams and provide right set of data products to consumers. Data Mesh offers a new decentralized data architecture approach for analytics that intends to remove the hurdles and take data decisions nearer to those who understand the data.
Beginning this year, big enterprises with distributed data environments will implement a data mesh architecture. With different domains or functional units within large organizations have a better understanding of how the data should be used, enabling the domains implement and define their own data infrastructure results in lesser iterations until business needs are met and are of high quality.