Eight trends in data analysis in 2022

The data analysis market is booming. Loud IDC Analysts estimate companies will be worth $215 billion in 2021 Big-Data - and business analytics solutions, up 10% from 2020. The demand for skilled workers in data analysis is also exploding. Researchers from the U.S. Bureau of Labor Statistics predicts strong growth (31%) in data science through 2030. It is predicted that nearly all companies (90%) will view information as a "critical business asset" and analytics as an "essential competency" this year.

So what is driving this growth? Based on my industry experience, I believe these are the eight key trends that will define the data analytics market in 2022 and beyond.

1. Artificial intelligence

Artificial intelligence (AI), automation and machine learning are changing the game for businesses everywhere. AI is making rapid progress, especially in the area of ​​data analysis, where it not only complements human skills but also contributes to better value creation. The pandemic and teleworking have the potential to Track and measure data , expanded significantly and spawned a new data-driven culture in companies. This data culture is driving investments in AI-based analytics.

AI has a wide range of applications to increase business value. Some examples include increasing sales by forecasting demand and ensuring adequate inventory levels, improving customer satisfaction through faster delivery times, and increasing operational efficiency by automating processes that would otherwise require a human.

2. Composable data analysis

Composable data analytics is a process by which companies combine and use analytics capabilities from different data sources across the organization to enable more effective and intelligent decision making. Such tools offer more flexibility than traditional approaches and feature reusable, interchangeable modules that can be deployed anywhere, including in containers.

With composable analytics, companies can reduce data center costs even if they have migrated to the cloud. The analysts from Gartner predict that by 2023, 60% of companies will build business applications that contain components from three or more analytics solutions.

3. Data Fabric as the new industry standard

One Forrester -According to analysts, 60 to 73% of enterprise data remains unused for analytics. Data Fabric is a new approach to solving the old problem of using disparate data for analytics. When IT can provide a unified data architecture that serves as an integrated layer to connect data endpoints and processes, business-critical data can be made more discoverable, consistent, and reusable across an organization's environments, including hybrid and multi-cloud environments .

And it's not just about using more data sources for analysis. The real value of the data fabric architecture lies in standardizing data management and making data easier to access for users in different environments.

4. AnalyticsOps

In 2018 it was Data Ops included in the Gartner Hype Cycle for data management. DataOps can improve the collaboration, automation, testability, and curation of data processes, especially the transition of these processes into production. Since then, interest has increased and vendors specializing in DataOps have seen high ratings. As Machine Learning Operations (MLOps) adds additional credibility to the Ops mindset, I expect to see a trend in 2022 toward building an overarching practice that I call "AnalyticsOps" that can make it easier to deploy composable analytics and manage the data structure.

5. A shift from big data to small and wide data

The emergence of AI, data fabric, and composable analytics solutions enable companies to examine a combination of small and large - as well as structured and unstructured - data, applying techniques that search for useful insights in small or even micro-tables of data. For example, while a traditional data source provides one column for the color of an item, AI-friendly data can contain many columns (often called features) that answer the question "Is it red? Is it blue? Is it green?" and so forth. With so many more potential columns/features, these large data structures require special consideration from the database engine.

Access to sources large, small and extensive is a key capability that companies will likely continue to leverage in the coming years. However, Gartner analysts predict that by 2025 70% of companies will move from big data to small and wide data (or data that comes from a variety of sources), enabling more context for analysis and intelligent decisions.

6. Democratization of data

Gone are the days when data analysis was viewed as an afterthought or a side activity. Companies today view data analysis as an important factor for intelligent decision-making and as a central element at the start of any new project.

Companies may want to make analytics available to all employees - not just business analysts. However, the additional workloads and required concurrency must be taken into account. Gartner analysts predict that by 2025 80% of data analytics initiatives focused on business outcomes are considered an essential business capability.

7. Analytics everywhere

The consumers of the future will benefit from personalized and dynamic insights that help them get the most value from their data or achieve their goals faster. Companies that anticipate this trend could have a significant competitive advantage over other companies that do not offer such features to their customers. Through a combination of AI/ML, automation and business intelligence, customers can benefit from personalized, analytics-driven services.

8. Analytics in Edge Computing

The edge computing market is growing at a staggering 19% CAGR every year and is expected to reach $36.5 billion in 2021 87,3 Mrd . USD will increase in 2026. As computing power moves to the edge of the value chain, supporting technologies (including data analytics) are likely to increasingly reside at the edge of the value chain in close proximity to physical assets.

This shift enables speed, agility and greater flexibility, supporting real-time analytics and enabling autonomous behavior for Internet of Things (IoT) devices. Gartner analysts predict that by 2023, 50% of data analytics responsibilities will come from data created, managed, and analyzed in edge environments.