Organizations are constantly working to improve their DataOps functions and frameworks in order to get the most out of their datasets. Businesses can leverage advancements in their industries and markets to their advantage and stay a step ahead of their competitors by remaining informed about what’s going on in their industries and markets.
Digitization has been more prevalent across multiple industries during the last year. Gartner’s Top 10 Data and Analytics Trends for 2021 predicted a migration from big data to wide and small data, and the industry has witnessed that. More businesses are shifting away from traditional data analytics methodologies that rely on historical data and toward AI-powered data analytics. Organizations that were hesitant to migrate their on-premises systems to the cloud had to experience a rude awakening. They had no choice except to rethink their operations and migrate to cloud-based infrastructure, or risk floundering while offices were shuttered.
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. Enterprises that leverage data-driven decision-making are more likely to stay ahead of the competition than those who do not.
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Craig Stewart, CTO at SnapLogic says, “Next year will be when companies truly understand the importance of opening up data access to all parts of their organizations. They will move forward by giving non-IT, business-level employees not just access to the company’s data, but the tools to utilize said data themselves. The future is enabling individual departments to procure, develop, analyze, and use what they need – with IT visibility and governance as required – and the technologies have finally matured to a place where this can be realized.”
In the 2022 data landscape, enterprises and data innovators building out their data stack and data strategy should expect to see the following trends:
More multi-cloud, hybrid, and edge environments
More multi-cloud, hybrid, and edge environments are expected to emerge in the next year, paving the way for new distributed cloud models.
There is far too much unstructured data today – data from emails, productivity applications, surveillance data, and data from machines, and so on. Enterprises handling large volumes of data cannot continue to employ batch-based reporting to give better value to their customers. To address the unstructured data difficulties because of data siloing, businesses should construct their data stack from the ground up. Organizations will be able to handle unstructured data using hybrid multi-cloud solutions while adhering to governance and security policies.
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The data quality issue to persist
Businesses will continue to face data quality concerns in their data architecture as the value of data and the requirement for enhanced AI and ML capabilities grows. To avoid serious impact on important products and services, the best solution is to address the problem in a holistic, proactive, and systematic manner.
Companies that warn about data anomalies and perform integrity checks throughout the ETL process have sprung up in response to the market’s demand for data regularization and quality testing solutions. As the demand for technologies that give reliable, high-quality data grows, more companies are expected to emerge in this market.
Even as data analytics, collection, and processing capabilities improve, the problem of cutting through the clutter and distinguishing useless data from useful data remains as important as ever. Data quality issues will continue to be a problem for businesses in 2022.
Increased democratization
While 2021 saw a pandemic-driven increase in no-code digital solutions, the rise of low code/no-code platforms will power greater enterprise agility via automation in 2022. Self-service analytics, which allow non-technical business users to access data and make better business decisions in a fraction of the time needed by traditional BI systems and analytics, will replace more IT-centered workflows.
People without a core data background will be able to become key players in the data ecosystem thanks to these democratized, data-driven workflows, which will bring more diversity to the sector.
Companies must devise ways for unlocking the value of irregular scattered, data as decentralized processes become more prevalent in the data landscape. Data mesh architecture will emerge as a critical factor for success, allowing access to a complex range of data that isn’t standard or consistent and making it useable across several tools.
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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.
