In recent years, big data analytics and management have become more strategic, spurred by digital transformation initiatives, endeavors to leverage data for competitive advantage, and even efforts to monetize data assets. Why is there such a massive surge in growth?
Here are the top ten defining trends that will continue to fuel the data analytics market in 2022 and beyond.
What is Data Analytics?
Data analytics is all your data in real-time, historical, unstructured, structured, and qualitative forms. It allows organizations to identify patterns and generate insights for informing and, in some cases, automating decisions that align intelligence and action. The advanced analytics solutions of today provide end-to-end support from preparing, analyzing, and operationalizing data to monitoring the results.
Data analytics drives businesses forward by optimizing critical business moments by implementing algorithms everywhere. For example, a customer walking into your store, a piece of equipment about to fail, or other events that can impact the course of the business. Data analytics can be applied to a wide range of industries, including Manufacturing, Financial Services, Insurance, Transportation, Travel and Logistics, Healthcare, and more. Analytics can contribute to predicting and handling disruptions, delivering proactive customer service, making smart cross-sell offers, predicting equipment failure, managing inventory in real-time, optimizing pricing, and preventing fraud.
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Different components of data analytics
Several approaches can be used to process any set of data.
- Data Mining
Data mining aims to turn huge amounts of raw data into a usable chunk of information. Anomalies in groups of data are also identified and relationships between the various groups of data to establish correlations. Many clinical trials use data mining to identify patterns in the behavior of patients.
- Predictive Analysis
You can create auto-correction for your phone and predictive typing for your email using text analytics. Algorithms are developed by processing massive amounts of unstructured text. It involves linguistic analysis, pattern recognition in textual data, and filtering out junk emails from useful ones.
- Data Visualization
This is a technique for representing data in a visual format for a better assessment. It makes complex data easier to understand. A few examples are bar charts, histograms, graphs, and pie charts.
- Business Intelligence
An analysis of data that generates actionable insights is referred to as business intelligence. It provides information for making business decisions, such as product placement and pricing. It involves using visual tools, such as heat maps, pivot tables, and mapping techniques.
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- Democratization of Data
While data analytics was once seen as an afterthought or a secondary activity, organizations are now embracing it as a fundamental driver of intelligent decision-making and a key component of any new project.
Many companies provide analytics to their entire workforce, not just business analysts. Gartner analysts predict that by 2025, 80 percent of data analytics initiatives to improve business outcomes will qualify as a core business capability.
- Building a New Industry Standard with Data Fabric
Forrester analyst estimates that 60% to 73% of enterprise data is unused for analytics. Analyzing disparate data using data fabric is a new way to look at the old problem. When IT creates a unified data architecture that connects data endpoints and processes across all organizational environments, including hybrid and multi-cloud environments, mission-critical data becomes more discoverable, pervasive, and reusable.
There’s more to it than just using more data sources. The standardized data management and easy access to data across different environments make data fabric architecture valuable.
- Analytics is Everywhere
The focus of business analytics initiatives has traditionally been on analyzing data generated internally, including sales, market surveys, and financial performance. But increasingly, businesses are getting their data from external sources and enhancing their own by using it. According to IDC, 75 percent of enterprises will use external data sources in 2021 to improve cross-functional and decision-making capabilities.
According to analyst Joe Hilleary in the Eckerson Group report, this year will see an explosion in businesses using data marketplaces to access public, third-party data for analytical and machine learning tasks.
A data marketplace enables companies to sell and purchase data for a fee. According to Hilleary, third-party data acquisition and use have become more affordable and transparent as the supply of data for sale grows as more organizations look to monetize their own. As companies seek to monetize their data assets, the number of data providers and data suppliers increases. Moreover, cloud data marketplaces such as the Snowflake Data Marketplace simplify the purchasing and selling of data.
Consumers in the future will be empowered by personalized and dynamic insights that can help them extract the most value out of their data or reach their goals faster. Organizations that foresee this trend could have a significant competitive advantage over others that fail to offer such functionality to their customers. Through AI/ML, automation, and business intelligence, customers can benefit from personalized services powered by analytics.
- The Strategic Importance of Supply Chain Analytics
The COVID-19 pandemic has presented businesses with many challenges, including managing supply chain disruptions. Shuttered manufacturing plants around the world have caused many disruptions. Still, those problems have been exacerbated by the lack of visibility many companies have in their supplier networks, making it difficult to adapt plans, find alternative suppliers, and adjust distribution to match supply and demand.
According to a recent Eckerson Group report, supply chain management has evolved from a tactical to a strategic function. The year 2022 is expected to witness many companies step up their digital tracking of data from manufacturers and transportation companies using sensors and other technologies, analyzing interdependencies in the supply chain, and developing contingency plans using AI and machine learning.
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- Artificial Intelligence is Gaining Ground
Artificial intelligence is revolutionizing every aspect of every walk of life. This tool allows users to rethink how information is integrated, how data is analyzed, and how insights are used to improve decision-making. Algorithms employed by artificial intelligence make decisions based on data, often in real-time. On the other hand, passive machines can only respond mechanically or through predetermined ways.
Artificial intelligence is generally used in conjunction with data analytics and machine learning. Machine learning analyses data to find patterns. In the case of practical problems, software designers can take that knowledge and use it to examine specific issues. With every passing second, AI is drastically changing the world and posing serious questions for society, the economy, and the government.
- Data Observability is Becoming Mainstream
The term “data observability” became a buzzword in 2021. Data observability technology will be implemented in 2022 when the hype catches up with reality. Many digital transformation and machine learning initiatives rely heavily on data analytics.
This is because the data’s quality, reliability, and completeness are of the highest standard. In the same way, as service level agreements govern applications and IT infrastructure, data observability helps monitor data for quality, behavior, privacy, and ROI.
- Taking Data on the Cloud
It is predicted that the cloud data lake will serve as a central repository for collecting and processing data for different types of research. While cloud data lakes will undeniably gain traction, data is piling up at all levels, including on-premises storage, the Cloud, and the edge. In some cases, this calls for processing and analyzing data where it is instead of moving it because it is faster and cheaper.
In addition to searching for data at the edge, how can you also process a considerable amount of it locally before sending it to the Cloud? The Cloud may be a better solution for more complex, large projects. We will see more “edge clouds,” where computing is located at the edge of the data center rather than in the Cloud.
- Data Strategies for Multi-Cloud will Evolve
Today, many organizations have a hybrid cloud environment where the bulk of their data is stored in private data centers and backed up across several vendor systems. The Cloud has become indispensable for secondary or tertiary storage as unstructured (file) data has grown exponentially.
Managing costs, ensuring performance, and managing risk across silos can be difficult. Consequently, IT leaders find it challenging to extract value from data across Cloud and on-premises environments. A multi-cloud strategy works best when organizations use different clouds for different data sets and cases.
One newer concept is to use computing to gather data that resides in one place. A colocation center with direct access to cloud providers could serve as a central location. The multi-cloud strategy will evolve through various methods: sometimes the computer arrives at your data; other times, your data resides in multiple clouds.
- Citizens Science is Becoming more Influential
Several cloud providers are expected to release more machine learning applications, such as domain-specific machine learning workflows to democratize data science. Consequently, this is a seminal trend, as, over time, individuals will need fewer coding skills. Machine learning will now be available to a much broader range of jobs: some will reside within central IT, and some will be based within lines of business.
In 2022, we can expect to see more low-code/no-code tools, like Amazon Sagemaker Canvas. Citizen science is quite a nascent trend, but it’s definitely on the rise and a good indicator of future data trends. Platforms and management solutions that offer consumers-like convenience for searching, extracting, and using data will gain popularity.
- Application of Data science Algorithms to IoT
There is a greater need for Data Science in IoT than ever before. Understanding smart data would be the best way to apply the right algorithms to IoT data. Smart data is overcoming the challenges presented by high velocity, scale, and variety, allowing it to provide extensive information to assist decision-making.
Conclusion
Many new Data Science trends in the data technology industry were born in 2021 as a result of the mass online migration of global businesses. Blockchain implementation in analytics, scalable AI, graph analysis, and cloud computing started before 2021, and these trends are expected to continue in 2022.
Data analytics have been driving organizations for years in terms of transparency, speed, and decision-making capabilities, and they will remain so in 2022 and beyond. Particularly in the aftermath of the pandemic, these programs have led organizations to recognize the great wealth of untapped data. As companies reinvent themselves culturally and technologically, they utilize data to the fullest and be digital-first.
As the data tsunami continues to envelop enterprises, they will be forced to adopt data management strategies to derive useful information from it to drive critical business decisions. This effort will consist of both analytics and creating open and standards-based data fabrics that can be used to bring all this data under control for analysis and action.