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The past year saw increased digitization across enterprises in multiple industries. Gartner's Top 10 Data and Analytics Trends for 2021 predicted a migration from big data to small and wide data, and we've seen that happen. More companies moved from traditional data analytics techniques that relied on historical data to modern, AI-powered data analytics. Organizations that were initially reluctant to move their on-prem systems to the cloud experienced a rude awakening. They were forced to rethink their operations and make the leap to cloud-based infrastructure, lest they flounder while offices were widely closed.
Data is the lifeblood of all organizations today — arming decision-makers with the insights they need to create effective strategies that bolster business growth through innovation. Organizations that leverage data-driven decision-making are on the path to stay further ahead of the curve than those who don't. As we go into the next year, data innovators and organizations building out their data stack and data strategies should expect to see the following trends in the 2022 data landscape:
With a Gartner forecast indicating that end-user spending on public cloud services will grow 21.7% to reach $482 billion in 2022 — up from $396 billion this year — next year will see more hybrid, multi-cloud, and edge environments set the stage for new distributed cloud models.
In another report published by McKinsey, 70% of companies will use hybrid-cloud or multi-cloud platforms as part of a distributed IT infrastructure in 2022. "This will help companies boost their speed and agility, reduce complexity, save costs and strengthen their cybersecurity defenses," according to the report.
There's too much unstructured data in the world today: data from productivity applications, emails, data from machines, surveillance data, and more. To provide greater value to their customers, enterprises moving huge volumes of data cannot continue to use traditional batch-based reporting. Companies must build their data stack from the ground up to overcome the unstructured data challenges that data siloing creates today. Hybrid multi-cloud solutions will help organizations manage unstructured data— all while ensuring they comply with governance and security regulations.
Competition rose among giants in the cloud ecosystem in 2021 — including the data warehouse and data lake. However, 2022 will see the overlap of the technologies driving digital transformation, including artificial intelligence (AI), business intelligence (BI), and machine learning (ML) use cases.
In an article published by A16z, titled The Emerging Architecture for Modern Data Infrastructure, some experts predict the convergence of data warehouses and data lakes will simplify the technology and vendor landscape for both use cases. With more and more modern data warehouses and data lakes resembling one another, the importance of partnerships among giants in the cloud ecosystem will become increasingly apparent.
As organizations contend with so many sources of data across a multitude of platforms and tools, a solid investment in metadata strategy is the first step towards regulating data processes for greater value. More and more platforms that help businesses understand where their data came from and how to best use it are emerging to meet this need. Whether in no-code/low-code environments or highly sophisticated structures, solutions that empower companies to organize their data and create the right data architecture for their business are going to be more important than ever in 2022.
Immuta's 2022 State of Data Engineering Survey, which explores the emerging challenges with data security and quality, shows that 60% of the total respondents of 372 — including data engineers and data architects — said their organizations are now using data catalog and data discovery tools.
Only 23% of the respondents stated their organizations don't use any data catalogs or data discovery tools, and the remaining 17% were uncertain if their organizations used any such tools. It's clear that advanced data tools have gone from a perk to a standard procedure at many organizations, and adoption rates for data catalogs and discovery tools will continue to rise in 2022.
Data lineage, which helps users visualize data workflows, will become even more important so that teams can keep track of how data has traveled from Point A to B, and if it's become garbled on the way. And data governance, which empowers users with tools and processes to manage visibility, compliance, and access and permissions across all organizational data, will also become a major priority even for companies that don't define themselves as "data-first."
As technology continues to develop and adoption rates grow, these tools will mature appropriately to fit business' needs, driving innovation, and enabling a data-driven approach to application development across enterprises. Here is the best data room for startups.
As the importance of data and the need for advanced AI and ML capabilities increases, companies will continue to experience data quality issues in their data architecture. The best solution is to tackle the issue in a proactive, holistic, systematic manner to avoid severe impact on key products and services.
Immuta's survey further gave new insight into the data quality challenge, with respondents stating that data quality and validation is a core area where companies face the most problems today. Organizations with low maturity DataOps practices had the highest percentage (39%) of respondents who were unsure of what (if any) data quality solution their organization is using.
This need in the market for data regularization and quality testing systems has led to the emergence of companies like quilliup, which alerts for data discrepancies and performs integrity checks throughout the ETL process. More companies will likely pop up in this space, as the need for tools that provide solid, quality data is only set to grow.
Even as data analytics, gathering, and processing tools grow more advanced, the issue of cutting through the noise and separating junk data from helpful data remains pertinent as ever. Challenges around ensuring data quality will continue to plague companies through 2022.
While this year saw a pandemic-induced increase in no-code digital solutions, the rise of no-code/low code platforms will drive greater enterprise agility through automation in 2022. Organizations will move from more IT-centered workflows to self-service analytics that allow non-technical business users to access data and make smarter business decisions at a fraction of the time required by traditional analytics and BI systems.
These democratized, data-driven workflows will usher in greater diversity in the industry — enabling people without a core data background to become key players in the data ecosystem.
Experts believe 93% of all data worldwide will be unstructured by 2022, according to research by International Data Group (IDG). As the big data and analytics challenge continues, organizations that see data and analytics strategy as a concern rather than an afterthought will remain competitive in the industry.
With decentralized processes on the verge of reigning supreme in the data landscape, companies must strategies for unlocking the value of scattered, irregular data. Data mesh architecture that enables access to a complicated range of data that aren't uniform or consistent, and makes them usable across numerous tools, will emerge as an important factor for success.
Organizations are always seeking to obtain optimal value from their datasets by developing their DataOps functions and frameworks. By staying aware of what's going on in their industries and markets, businesses can leverage developments in the space to their advantage and remain a step ahead of their competition. 2022's hottest data trends reflect the need for organizations to always strive to be on top of their data game, and as data grows even more critical, businesses that don't invest in their data strategy risk being left behind.
Itamar Ben Hemo, Co-Founder and CEO of Rivery
