Data Analytics Trends:- Top 5 Key Trends in 2022

The data analytics industry is on a high-growth trajectory. The number of data analysts/scientists is growing rapidly, and the demand for skilled professionals will continue to grow in the coming years. As per the recent study from McKinsey Global Institute, more than half (52%) of respondents observed that they have difficulty finding analytical talent with the right skills.

Data is the lifeblood of today's businesses, providing decision-makers with the information they need to develop effective strategies for boosting growth through innovation. Organizations that use data-driven decision-making are more likely to stay ahead of the competition than those that do not. The following trends should be expected in the 2022 data environment by data entrepreneurs and enterprises building out their data stacks and data strategies:

More hybrid, multi-cloud, and edge environments are being developed

With Gartner forecasting that end-user spending on public cloud services will increase 21.7 percent to $482 billion in 2022, up from $396 billion this year, more hybrid, multi-cloud, and edge environments will set the stage for new distributed cloud models next year.

Another McKinsey report predicts that in 2022, 70% of businesses will use hybrid-cloud or multi-cloud platforms as part of a distributed IT infrastructure. "This will help companies increase their speed and agility, reduce complexity, save money, and strengthen their cybersecurity defenses," according to the report.

Today's world contains far too much-unstructured data: data from productivity applications, emails, machine data, surveillance data, and so on. Enterprises moving massive amounts of data cannot continue to use traditional batch-based reporting to provide greater value to their customers. To overcome the unstructured data challenges that data silos create today, businesses must build their data stack from the ground up. Hybrid multi-cloud solutions will assist organizations in managing unstructured data while also ensuring compliance with governance and security regulations.

The Enormous Convergence

In 2021, competition increased among cloud ecosystem titans, including the data warehouse and data lake. However, by 2022, the technologies driving digital transformation, such as artificial intelligence (AI), business intelligence (BI), and machine learning (ML), will be overlapping.

Some experts predict that the convergence of data warehouses and data lakes will simplify the technology and vendor landscape for both use cases in an article published by A16z titled The Emerging Architecture for Modern Data Infrastructure. With more and more modern data warehouses and data lakes resembling one another, the importance of collaboration among cloud ecosystem titans will become clearer.

With so many data sources to manage across so many platforms and tools, a solid investment in metadata strategy is the first step toward regulating data processes for greater value. To meet this demand, an increasing number of platforms that assist businesses in understanding where their data came from and how to best use it is emerging. Solutions that enable companies to organize their data and create the right data architecture for their business, whether in no-code/low-code environments or highly sophisticated structures, will be more important than ever in 2022.

Rapid Development of Data Catalog and Data Discovery Tools

According to Immuta's 2022 State of Data Engineering Survey, 60 percent of the 372 total respondents — including data engineers and data architects — said their organizations are now using data catalog and data discovery tools.

Only 23% of respondents said their organizations do not use data catalogs or data discovery tools, while the remaining 17% were unsure. It is clear that advanced data tools have progressed from a perk to a standard procedure in many organizations, and adoption rates for data catalogs and discovery tools will continue to rise in 2022.

Data lineage, which allows users to visualize data workflows, will become even more important so that teams can keep track of how data has traveled from point A to point B and whether it has become garbled along the way. Data governance, which provides users with tools and processes to manage visibility, compliance, access, and permissions across all organizational data, will also become a top priority, even for companies that do not consider themselves to be "data-first."

As technology advances and adoption rates rise, these tools will mature to meet the needs of businesses, driving innovation and enabling a data-driven approach to application development across enterprises.

The Data Quality Problem Will Persist

Companies will continue to experience data quality issues in their data architecture as the importance of data and the need for advanced AI and ML capabilities grows. To avoid a significant impact on key products and services, the best solution is to address the issue in a proactive, holistic, and systematic manner.

Immuta's survey provided additional insight into the data quality challenge, with respondents stating that data quality and validation are two of the most difficult areas for businesses today. Organizations with low DataOps maturity had the highest percentage of respondents (39%) who were unsure of what (if any) data quality solution their organization was using.

Because of the market demand for data regularisation and quality testing systems, companies such as quilling has emerged, which alerts for data discrepancies and performs integrity checks throughout the ETL process. More companies are likely to emerge in this space as the demand for tools that provide solid, high-quality data grows.

Even as data analytics, gathering, and processing tools improve, the problem of cutting through the noise and distinguishing useful data from junk data remains as pressing as ever. The challenges of ensuring data quality will continue to plague businesses until 2022.

Enhanced Democratization

While this year saw an increase in no-code digital solutions due to the pandemic, the rise of no-code/low code platforms will drive greater enterprise agility through automation in 2022. Organizations will shift away from more IT-centric workflows and toward self-service analytics, which will enable non-technical business users to access data and make smarter business decisions in 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, allowing people without a core data background to become key players in the data ecosystem.

According to International Data Group research, experts believe that by 2022, 93 percent of all data worldwide will be unstructured (IDG). As the big data and analytics challenge continues, organizations that treat data and analytics strategy as a priority rather than an afterthought will remain competitive.

With decentralized processes on the verge of taking over the data landscape, businesses must devise strategies for maximizing the value of scattered, irregular data. A data mesh architecture that allows access to a complex range of data that aren't uniform or consistent, and makes them usable across multiple tools, will emerge as a critical success factor.

Organizations are constantly striving to maximize the value of their datasets by developing DataOps functions and frameworks. Businesses that stay informed about what's going on in their industries and markets can use developments in the space to their advantage and stay one step ahead of their competition. The hottest data trends for 2022 reflect the need for organizations to always strive to be on top of their data game, and as data becomes more critical, businesses that do not invest in their data strategy risk falling behind.