The pandemic has accelerated business digital transformation, which, in turn, generates more data. When managed and leveraged appropriately, data is a goldmine for companies to make smarter decisions. However, data analytics becomes unwieldy due to the sheer volume of data, necessitating increasingly sophisticated tools and methodologies for analysis and analytics. This reality is reflected in projections that the global data analytics market will reach just over $25 billion by the year 2028.
As 2021 comes to a close and the pandemic continues to drive digital transformation, what trends can we expect to see on the horizon for 2022? We’ll cover some of the key trends here, including why they’re important and what implications they may hold for the growing data economy.
What is Data Analytics?
To briefly define data analytics and differentiate it from data analysis, data analytics encompasses the collection, organization, storage, segmentation, and visualization of data to make it digestible and actionable for business practices.
Data analytics should not be confused with data analysis. Data analysis is only a part of data analytics, namely the preparation of data to gain meaning from it. Preparation in this context means collecting, cleaning, formatting, and organizing data to deduce meaning from it.
Data Analytics Trends to Expect in 2022
The edge computing industry is predicted to grow to more than $87 billion by 2026. As manufacturers, energy companies, and other industries drive demand for edge computing, data centers will become more localized to meet data consumers where they’re at, literally. Edge computing brings data analytics technology closer to the physical asset and its network, resulting in greater reliability and scalability. More specifically, edge computing reduces latency, increases processing speed, and enables more real-time functionality for the customer’s network.
Expect to see more companies adopt edge computing at least in combination with cloud computing services for ultimate versatility.
Also read: Micro Data Centers are Evolving the Edge
Since the need for data scientists and data engineers outpaces their rate of training and availability to fill roles, technology has responded accordingly. Now, accessing and analyzing data no longer requires a high level of technical expertise. The data analytics market today offers low-code/no-code tools that anyone can use to pose queries, generate reports, and develop data visualizations. Often part of a larger business intelligence (BI) platform, self-service analytics allows key stakeholders to make smarter decisions more quickly without necessarily needing to consult with IT and/or data analysts. This trend will continue to empower employees of various ranks and roles to participate in data analytics.
The rise of blockchain in data science
Most commonly known in the finance technology (Fintech) and healthcare industries, blockchain technology is entering the IT industry, making it easier to manage large data sets in a decentralized digital ledger. As data accuracy becomes increasingly important, blockchain helps in this regard, as it tracks and validates data. It essentially logs a paper trail for digital transactions and assets. Blockchain takes care of the data preparation process in a decentralized way that bypasses the need for a centralized team of data analysts, enabling faster decision making.
Augmented BI analytics with AI, ML, and AutoML
AI/ML tools are becoming increasingly common in business intelligence platforms to enhance their functions. Research by Beroe, Inc. suggests that the global business intelligence market is estimated to reach $30.9 billion by 2022, due to a variety of factors, such as demand for data-as-a-service and demand for self-servicing BI capabilities, to name a few.
AutoML is accelerating business decision making by automating the processes that get data ready for analytics, such as cleaning data and developing training models. AutoML therefore not only makes data insight-ready at a more rapid rate but it also reduces the need to find and hire much-sought-after data scientists.
AI/ML tools in BI platforms measure, interpret, and predict the results of decisions and learn from them by adjusting decision parameters as needed. Leading enterprises are applying AI/ML algorithms to large amounts of data about the market, customers, applications and more in order to perform predictive analytics.
Machines are carrying out more of the functions that humans either cannot do or no longer have to do, and they will continue to shape business decisions into 2022. We can also expect this trend to bring more synergy among business leaders, IT, and data analysts in order to align data with strategic initiatives and goal setting.
Cleaner, greener AI in data centers
Given data centers’ carbon emissions and the amount of energy they generate, there will be increasing interest in “model efficiency” which describes simple, efficient AI models that can be used to solve complex problems using less resources. That means that AI models do not necessarily have to have as many layers in their neural network. Bayesian AI models can solve complex problems using less data, computing power, and overall training.
Small and wide data analytics
Small data include a narrower range of information but still enough to measure and interpret patterns. In contrast to large datasets which shed insight on enterprise-wide metrics and answer more complex questions, “small data” provides key insights on things like customer behavior. For that, marketers don’t necessarily need to collect vast amounts of data. Instead, small batches of data from varied data sources—think both structured and unstructured—allow for incremental, timely improvements to a product or service. Small batches of data also provide enough information to understand customer sentiment and personalize interactions with customers.
DataOps to XOps
XOps is one of the top data analytics trends to watch out for in 2022 and encompasses DataOps, ModelOps, AIOps, and PlatformOps. XOps applies DevOps best practices to data analytics and machine learning/modeling to boost reliability, repeatability, and reusability and reduce duplication. XOps, as a powerful combination of IT disciplines, enables seamless and efficient data analytics to drive strategic decision making and business goals.
A consequence of today’s digital transformation is a complex, interconnected ecosystem made up of various devices, applications, data infrastructure types/formats. Finding one solution that connects these parts has been a persistent challenge until the emergence of data fabric architecture that seamlessly weaves these different, geographically dispersed parts together.
In the coming year, we can expect to see more companies use data fabric architecture as a data management approach that enables enterprise-wide data analytics and automates the processes behind them, such as data discovery and exploration, data collection, data integration, and data preparation. This will shorten companies’ product development lifecycles, saving them time and money.
Data analytics has become an essential part of business functions and will continue to be. The trends noted here reveal an increased democratization, automation, simplicity, and synergy in how companies will collect, manage, analyze, and leverage data in the new year. Which of these trends will your company adopt?
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