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Tony Lopykinski

Founder & Managing Principal

10 Data Analytics Trends to Watch in 2022

The COVID-19 pandemic introduced a new level of urgency in data analytics, and that isn’t changing any time soon. As workforces remain dispersed, there is an increased need for faster networks and self-service data that’s easy to access—factors that are driving many of the trends that will shape the industry in 2022.

The push to modernize data analytics has expanded capabilities in this growing market, another theme you’ll notice throughout our top 10 list. Technologies like machine learning and IoT are changing the landscape for the better, while the professionals behind the data and the analytics are more in demand than ever before.

There’s a lot happening in the data analytics field in 2022. Here’s a look at the top 10 trends that matter most to your organization:

1. Data and analytics get a much bigger seat at the table
Data analytics continues to be a hot and growing market. According to Fortune Business Insights, the global data analytics market is expected to be worth nearly $550B in 2028, up from $231B in 2021.

This is supported by the continued explosion of data. The World Economic Forum predicts that by 2025, the amount of data generated each day will reach 463 exabytes globally.

Before the pandemic, business leaders were beginning to understand the importance of leveraging data and analytics to accelerate business initiatives. As we enter the “new normal” brought on by COVID-19, we expect it to be viewed as a core strategic business function rather than solely an IT function. Business leaders now see that it’s not just a competitive advantage, but a competitive necessity.

2. Data fabric becomes part of the modern data analytics architecture
Today’s complex digital ecosystem consists of various devices, applications and data infrastructure types. Data fabric is a single, consistent data management framework that seamlessly connects these different, geographically dispersed parts together within an enterprise.

We expect to see more companies using data fabric architecture to enable enterprise-wide data analytics and automate the processes behind them, such as data discovery and exploration, data collection, data integration, and data preparation. Data fabrics enable organizations to quickly solve complex data problems by automating data governance and compliance, eliminating inefficient and manual data integration, minimizing data silos and increasing data quality – ultimately accelerating digital transformation.

3. Hybrid and multi-cloud solutions become even more pervasive
Organizations will continue to move all or part of their data analytics solutions to the cloud. Why? The reduced cost of computing and storage, multitude of resources for data analysis, and ease of scale are among the reasons. Gartner forecasts that end-user spending on public cloud services will grow 21.7% to reach $482 billion in 2022—up from $396 billion last year. By 2026, Gartner predicts public cloud spending will exceed 45% of all enterprise IT spending, up from less than 17% in 2021.

As cloud computing becomes more secure, reliable and affordable, the use of data analytics will continue to be infused into everyday business processes, giving employees the tools to gather, sort and share data on demand. Cloud computing provides employees with faster access to a wider range of structured, unstructured and real-time data. Hybrid multi-cloud solutions will help organizations manage these higher volumes and shorter latencies, leading to improved productivity and more informed decision making.

4. Greater focus on scalable MLOps
Machine learning operations (MLOps) and automatic machine learning (AutoML) will support robust analytics solutions across organizations to enhance efficiencies, reduce costs and improve predictive analytics. Given the shortage of data scientists and engineers, more organizations will look to MLOps frameworks and AutoML capabilities to eliminate the tedious, iterative, time-consuming process of coding throughout the ML workflow. This provides end-users with simplified, code-free applications to generate models, make predictions and test business scenarios.

5. Companies will live more on the edge with IoT
According to its 2021 report, IoT Analytics estimates that by 2025 the number of connected IoT devices will surge to more than 27 billion. And with those technologies comes data, lots of data. According to Statista, by 2025, total data volume of connected IoT devices worldwide will reach 79.4 zettabytes. (One zettabyte = 1 trillion gigabytes!)

During the pandemic, everything has become distributed—people, applications, devices and data. Organizations are leveraging edge computing and 5G networks to cost effectively store data closer to the physical assets. This move provides users with hyper-fast download speeds and low latency to glean more actionable insights from IoT data.

6. Increased use of small and wide data analytics
Small data offers a narrower range of information, yet enough to measure and interpret patterns. Alternatively, wide data brings disparate data together across a broad range of structured and unstructured sources for more meaningful analysis. Gartner predicts that 70% of organizations will shift focus from big data to small and wide data by 2025. The pandemic is driving that shift.

Both approaches enable more robust analytics and AI, reducing dependency on big data. Large historical datasets became less useful during the pandemic. New, small, often external datasets have allowed organizations to adapt their analytics to the uncertainties brought on by COVID-19 and quickly get data that offers actionable insights into users’ hands.

7. The need for data quality and governance will continue
It’s fair to say this is not a new problem, but the COVID-19 pandemic has made it a more urgent one. And, as advanced artificial intelligence and machine learning capabilities drive real-time, application-level visibility, the need to have access to accurate, high-quality data is even more critical.

We believe the best defense is a proactive data quality and master data management offense. Taking a more strategic and systematic approach will allow organizations to avoid inaccurate analytics solutions and severe impact to key business decisions. While this is primarily a people and process challenge, more software companies will enter this space as the need for tools to provide solid, data quality and MDM solutions will continue to grow.

8. Data and analytics democratization will grow
More organizations will shift to an analytics product versus project model to grow and evolve business specific solutions that are self-service oriented. Data science and ML Ops teams will focus on building self-service data analytics platforms that allow their teams and business stakeholders to leverage plug-and-play components including datasets, algorithms, and even API’s. With the abundance of business intelligence, no-code/low-code, and autoML tools, smart companies are turning their less data savvy business users into mini data scientists, enabling them to perform rapid and sophisticated analyses.

These democratized, data-driven environments will result in greater diversity in the industry—enabling people without a core data background to become key players in the data ecosystem.

9. The data engineer’s role will gain prominence
Traditionally, data engineers have only been responsible for ensuring data pipelines deliver timely, high-quality data to business intelligence (BI) and data science initiatives. With the shortage of data scientists, they’ll take on additional duties including monitoring AI and ML models for accuracy.

Much like the demand for data scientists, the need for data engineers will continue to grow, with increased distinctions between the two. This suggests that data engineering responsibilities often performed by data scientists or other technology roles will evolve into its own independent data engineering function aligned to either the CIO, CDO or Head of Analytics.

Ironically, the shift to more data democratization and no-code pipeline will likely create more work for data engineers; they’re the ones who will have to unravel the messes created by non-data experts now working on complex use cases and datasets. Again, much like the current market for data scientists, supply and demand will create an irrational market for data engineers, making both obtaining and retaining quality resources a challenge for the next several years.

10. The Rise of the CDO (Chief Data Officer)
The pandemic sent a strong message to businesses: to quickly make effective decisions in an increasingly complex and uncertain world, data must be a priority on every company’s leadership agenda.

The truth is, data strategy can’t be implemented effectively unless you have someone on the payroll who is clearly accountable for collecting data and delivering value and insight from it. Many companies will hire CDOs to fill that void in 2022 but will initially struggle with defining the role and what success looks like. The key will be to set mutually agreed-upon expectations and achievable goals for improving data accessibility and literacy at every level across the organization.

An increasing focus on data and analytics
The COVID-19 pandemic has significantly accelerated the need for high quality, low latency data and analytics. This “new normal” is elevating the status of data analytics in businesses and driving investment in supporting staff, technology, processes, and capabilities—trends that will continue throughout 2022 and beyond.

Keep in mind this list focuses on the most relevant trends we see our clients adopting in 2022. Leading-edge technologies like blockchain, data mesh, graph analytics, data catalog and discovery tools are on our radar, and likely will become more relevant in coming years.

For now, the trends we’ve covered are the ones that matter most to a majority of organizations today – and set the foundation for successful adoption of emerging technologies in the future.

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