Gartner Top 10 Data and Analytics Trends (2022)

Traditionally, banks targeted older customers for wealth management services, assuming that this age group would be the most interested. Using augmented analytics, banks found that younger clients (aged 20 to 35) are actually more likely to transition into wealth management— a clear example of how relying on business users to find patterns, and on data scientists to build models manually, may result in bias and incorrect conclusions.

Explore the latest:Gartner Top 12 Data and Analytics Trends for 2022

Augmented analytics is just one of the top 10 technologies Gartner has identified with the potential to address these and other major data and analytics challenges in the next three to five years. Digital transformation has put data at the center of every organization. Businesses are awash with data. They struggle to identify what is most important and what actions to take (or avoid).

Act now on emerging trends

Rita Sallam, Distinguished Vice President Analyst, Gartner, says organizations need formal mechanisms to identify technology trends and prioritize those with the biggest potential impact. “Data and analytics leaders should actively monitor, experiment with or deploy emerging technologies. Don’t just react to trends as they mature,” Sallam says. “Use this list to educate and engage with other leaders about business priorities and where data and analytics can build competitive advantage.”

“ By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence”

Gartner’s list of top technology trends in data and analytics does not include trends that are less than three years away from mainstream adoption (such as self-service analytics and BI) or more than five years out (such as quantum computing). Nor does it include nontechnology trends such as data literacy, storytelling or data ethics that are also critical to success.

(Video) Gartner Top Strategic Technology Trends for 2022

Trend No. 1: Augmented analytics

Augmented analytics automates finding and surfacing the most important insights or changes in the business to optimize decision making. It does this in a fraction of the time compared to manual approaches. Augmented analytics makes insights available to all business roles. While it reduces reliance on analytics, data science and machine learning experts, it will require increased data literacy across the organization. By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms.

Trend No. 2: Augmented data management

With technical skills in short supply and data growing exponentially, organizations need to automate data management tasks. Vendors are adding machine learning and artificial intelligence (AI) capabilities to make data management processes self-configuring and self-tuning so that highly skilled technical staff can focus on higher-value tasks. This trend is impacting all enterprise data management categories, including data quality, metadata management, master data management, data integration and databases.

Trend No. 3: Natural language processing (NLP) and conversational analytics

Just as search interfaces like Google made the internet accessible to everyday consumers, NLP gives business people an easier way to ask questions about data and to receive an explanation of the insights. Conversational analytics takes the concept of NLP a step further by enabling such questions to be posed and answered verbally rather than through text. By 2021, NLP and conversational analytics will boost analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users, particularly front-office workers.

Trend No. 4: Graph analytics

Business users are asking increasingly complex questions across structured and unstructured data, often blending data from multiple applications, and increasingly, external data. Analyzing this level of data complexity at scale is not practical, or in some cases possible, using traditional query tools or query languages such as SQL.

“ The application of graph processing and graph databases will grow at 100% annually”

Graph analytics is a set of analytic techniques that shows how entities such as people, places and things are related to each other. Applications of the technology range from fraud detection, traffic route optimization and social network analysis to genome research. Gartner predicts that the application of graph processing and graph databases will grow at 100% annually over the next few years to accelerate data preparation and enable more complex and adaptive data science.

Gartner Top 10 Data and Analytics Trends (1)

Trend No. 5: Commercial AI and machine learning

Open-source platforms currently dominate artificial intelligence (AI) and machine learning and have been the primary source of innovation in algorithms and development environments. Commercial vendors were slow to respond, but now provide connectors into the open-source ecosystem. They also offer enterprise features necessary to scale AI and ML, such as project and model management, reuse, transparency and integration — capabilities that open-source platforms currently lack. Increased use of commercial AI and ML will help to accelerate the deployment of models in production, which will drive business value from these investments.

Trend No. 6: Data fabric

Deriving value from analytics investments depends on having an agile and trusted data fabric. A data fabric is generally a custom-made design that provides reusable data services, pipelines, semantic tiers or APIs via a combination of data integration approaches in an orchestrated fashion. It enables frictionless access and sharing of data in a distributed data environment.

Trend No. 7: Explainable AI

Explainable AI increases the transparency and trustworthiness of AI solutions and outcomes, reducing regulatory and reputational risk. Explainable AI is the set of capabilities that describes a model, highlights its strengths and weaknesses, predicts its likely behavior and identifies any potential biases. Without acceptable explanation, autogenerated insights or “black-box” approaches to AI can cause concerns about regulation, reputation, accountability and model bias.

Trend No. 8: Blockchain in data and analytics

Blockchain technologies address two challenges in data and analytics. First, blockchain provides the lineage of assets and transactions. Second, it provides transparency for complex networks of participants. However, blockchain is not a stand-alone data store and it has limited data management capabilities. A blockchain-based system can’t serve as a system of record, meaning a huge integration effort involving data, applications and business processes. Realistically, the technology hasn’t yet matured to real-world, production-level scalability for use cases beyond cryptocurrency.

Read more: The Reality of Blockchain

Trend No. 9: Continuous intelligence

Organizations have long sought real-time intelligence, and systems are available to do this for a limited set of tasks. Now it is finally practical to implement these systems — what Gartner calls continuous intelligence — on a much broader scale because of the cloud, advances in streaming software and growth data from sensors in the Internet of Things (IoT). By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

(Video) What are the Gartner top 5 trends in data analytics for 2020?

Trend No. 10: Persistent memory servers

Most database management systems (DBMS) make use of in-memory database structures, but with data volumes growing rapidly, memory size can be restrictive. New server workloads are demanding not just faster processor performance, but also massive memory and faster storage. Persistent memory technology will help businesses extract more actionable insights from data. Many DBMS vendors are experimenting with persistent memory, although it may take several years to modify their software to take advantage of it.

Gartner Top 10 Data and Analytics Trends (2)

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(Video) Gartner top 2021 analytics trends
(Video) Gartner Top Strategic Technology Trends for 2021

FAQs

What are the top trends of data analytics in 2022? ›

Trend #1 - Context-driven analytics

Context-driven analytics and artificial intelligence (AI) models will replace 60% of existing models built on traditional data by 2025, and augment people's decision-making, according to Gartner.

What are the trends in data and analytics? ›

Data Science, Big Data Analytics, and Artificial Intelligence are the key trends in today's accelerating market. As more organizations are adopting data-driven models to streamline their business processes, the data analytics industry is seeing humongous growth.

What are the key trends in data? ›

The key trends in today's fast-paced market are Data Science, Big Data Analytics, and Artificial Intelligence. The Data analytics industry is expanding rapidly as more companies adopt data-driven models to streamline their business processes.

What are the most recent trends in digital analytics? ›

One-click results, drag-and-drop buckets, and intuitive layouts are significant trends in digital analytics.

What is the biggest trend of 2022? ›

The Fall 2022 Trends Vogue Editors Are Shopping Now
  • Ultra-Baggy Denim. Balenciaga cotton baggy jeans. ...
  • Leather Jackets: The Epitome of Cool. ...
  • Punk Prep. ...
  • Saturated and Strokable Knits. ...
  • Bigger, Better Bomber Jackets. ...
  • The White-Tank-and-Blue-Jean Formula. ...
  • Floor-Skimming Hemlines. ...
  • Tailored Waistcoats.
19 Aug 2022

What are the key trends for 2022? ›

2022 fashion: the key trends to follow this year
  • 1 - Sexy and sensual all the way. The sense of liberation that sparkled in the past months is not going away. ...
  • 2 - Dopamine colours. ...
  • 3 - Rekindling the Millennium mood. ...
  • 4 - Understated minimalist fluidity. ...
  • 5 - Micro wear. ...
  • 6 - Glitchcore. ...
  • 7 - The biker look. ...
  • 8 - Bohemian chic.
1 Jun 2022

What are the 4 types of trend? ›

Typically, there are three different types of trends given below: Uptrend. Downtrend. Sideways trend.

What are the 5 V's of data analytics? ›

Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.

What are the 5 types of data analytics? ›

Four main types of data analytics
  • Predictive data analytics. Predictive analytics may be the most commonly used category of data analytics. ...
  • Prescriptive data analytics. ...
  • Diagnostic data analytics. ...
  • Descriptive data analytics.

What are the 5 types of trend? ›

The five different types of trend lines are:
  • Linear.
  • Polynomial.
  • Exponential.
  • Logarithmic.
  • Power.
13 Dec 2019

What are the 3 major trends? ›

There are three main types of trends: short-, intermediate- and long-term.

What are the 3 types of trends? ›

The three main types of trends are uptrends, downtrends and horizontal trends.

What are 6 types of analytics? ›

Data analysis can be separated and organized into six types, arranged in an increasing order of complexity.
  • Descriptive analysis.
  • Exploratory analysis.
  • Inferential analysis.
  • Predictive analysis.
  • Causal analysis.
  • Mechanistic analysis.
25 Oct 2022

What are the 3 common categories of data analytics? ›

There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.

What is the next big data analytics? ›

In the future, big data analytics will increasingly focus on data freshness with the ultimate goal of real-time analysis, enabling better-informed decisions and increased competitiveness.

Which market will boom in 2022? ›

The consensus seems to be that the financial sector, industrial sector, capital goods will do well in 2022.

What is the Fall 2022 trend? ›

Anything Upcycled and Vintage. Trends from the '90s and Y2K have already resurfaced for 2022, but Valiente is interested in even more retro, recycled, and vintage looks heading into the fall season. "I am personally purchasing more consignment items versus new items after a long break from the vintage trend," she says.

What trends will be popular in 2023? ›

10 Spring 2023 Fashion Trends That Will Be Everywhere in a Few...
  • Puff Shoes. Getty Images. ...
  • Hot Pants. Coperni, Zimmermann, LaQuan Smith. ...
  • Bigger Bags. Louis Vuitton, Max Mara, Balmain. ...
  • Jeans, Reinvented. Bluemarine, Vivetta, Ulla Johnson. ...
  • Lots of Spots. ...
  • Everyday Fringe. ...
  • Supersized Bows. ...
  • Glam Hoods.
6 Oct 2022

What are consumers looking for in 2022? ›

In 2022, consumers want to feel a stronger connection with brands, be part of a community, and get more value from their purchasing experiences.

What is the 8 emerging trends? ›

They include: artificial intelligence (AI), augmented reality (AR), blockchain, drones, Internet of Things (IoT), robotics, 3D printing and virtual reality (VR).

What is the best trend indicator? ›

The following indicators are regarded as the best trend indicators:
  • The Bollinger Band Indicator.
  • The Moving Average Convergence Divergence Indicator.
  • The Relative Strength Index Indicator.
  • The On Balance Volume Indicator.
  • Simple Moving Average.

What are the hierarchy of trends? ›

There are three way to classify trends, and those are mega, macro and micro trends.

What are the 5 A's of big data? ›

5 A's to Big Data Success (Agility, Automation, Accessible, Accuracy, Adoption)

What are the 5 P's of big data? ›

It takes several factors and parts in order to manage data science projects. This article will provide you with the five key elements: purpose, people, processes, platforms and programmability [1], and how you can benefit from these in your projects.

What are the 3 V's of big data analytics? ›

Dubbed the three Vs; volume, velocity, and variety, these are key to understanding how we can measure big data and just how very different 'big data' is to old fashioned data. Find out more about the 3vs of Big Data at Big Data LDN, the UK's leading data conference & exhibition for your entire data team.

What are the 4 phases of analytics? ›

That's why it's important to understand the four levels of analytics: descriptive, diagnostic, predictive and prescriptive.
  • Descriptive analytics.
  • Diagnostic analytics.
  • Predictive analytics.
  • Prescriptive analytics.

What are 10 types of data? ›

10 data types
  • Integer. Integer data types often represent whole numbers in programming. ...
  • Character. In coding, alphabet letters denote characters. ...
  • Date. This data type stores a calendar date with other programming information. ...
  • Floating point (real) ...
  • Long. ...
  • Short. ...
  • String. ...
  • Boolean.
21 Jul 2021

What should companies focus on in 2022? ›

As we head into 2022, continuing the focus on what your customer's needs and wants will not only build your relationship with them but also generate ROI. In time where brands and businesses start blending together, standing out is the way to go. But the way to stand out is to be authentic to your organization's brand.

What are the 4 characteristics of trends? ›

Trends have long staying power and enjoy a long period of popularity. Trends are popularly accepted by many industries and people. A trend is rooted on the people's cultural traditions, beliefs, and values. A trend persists and continues because people have seen it as part of a society's culture.

What are the four types of data emerging patterns? ›

What Are Data Trends and Patterns, and How Do They Impact Business Decisions?
  • Exponential Trend. This technique produces non-linear curved lines where the data rises or falls, not at a steady rate, but at a higher rate. ...
  • Damped Trend. ...
  • Seasonality. ...
  • Irregular/Random Patterns.
31 Jul 2018

What is the current trend in business? ›

Businesses continue to utilize social media

Running a business without having a social media presence is almost unheard of in 2022. Search growth for “social media marketing” has increased 74% in 5 years. The importance of social media in business' marketing will likely continue to increase in 2023.

Which is best tool for data analysis? ›

Tableau is a data analytics software that is widely used in business intelligence. It helps users to see and understand data with its built-in visual best practices. Tableau is also known for its scalability and efficiency, which makes it a popular choice for businesses of all sizes.

What is the most commonly used analytics? ›

Excel is, of course, the most widely used Data analytics software in the world. Whether you are an expert in R or Tableau, you will still use Excel for the grunt work. Non-analytics professionals will usually not have access to tools like SAS or R on their systems.

What are the 2 types of analytics on big data? ›

There are four main types of big data analytics: diagnostic, descriptive, prescriptive, and predictive analytics.

What are the 7 analytical methods? ›

The 7 Most Useful Data Analysis Methods and Techniques
  • Regression analysis.
  • Monte Carlo simulation.
  • Factor analysis.
  • Cohort analysis.
  • Cluster analysis.
  • Time series analysis.
  • Sentiment analysis.
13 Oct 2022

What type of data analytics is most difficult? ›

Prescriptive analytics is comparatively complex in nature and many companies are not yet using them in day-to-day business activities, as it becomes difficult to manage. If applied effectively, predictive analytics can have a significant impact on business growth.

What are the 5 year projections of data growth? ›

Fastest growing segments

Within this market, the data science platforms, tools and services segment is forecasted to remain the fastest growing, with a CAGR of 17.82% through 2026. Its market value is expected to reach $12.8 billion by 2026 from $5.6 billion this year.

What term is replacing big data? ›

The terminology "Big data" should be replaced as "Large data", because we study the large data sets instead of the big numbers.

Which big data tool is in demand? ›

Apache Hadoop is the most popular and widely used Big Data framework in the market. Hadoop allows for distributed processing of massive data sets across clusters of computers. It's one of the best Big Data Tools for scaling up from a single server to tens of thousands of commodity computers.

› blog › five-types-of-big-data-... ›

The word “analytics” is trending these days. More and more businesses are looking for employees with data analytics know-how and experience to help them sort th...
Data analysis can be grouped into four main categories: descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.
There are four main types of data analysis to be aware of: descriptive, diagnostic, predictive, and prescriptive. Learn all about them here.

What are the top five trends in analytics expected during 2022? ›

5 Data Analytics Statistics to know for 2022

The key drivers behind this forecasted trend include big data analytics, demand for data-as-a-service, demand for personalized, self-servicing BI capabilities.

Is data analyst in demand in 2022? ›

Data is everywhere, being generated in huge volumes at a rapid pace. Wherever there is data, there is a need for data analysts. So, to answer the question: Data analysts are very much in demand in 2022, and will continue to be for the foreseeable future. Great news for anyone considering a career change!

What is the next big thing in data analytics? ›

Augmented Analytics

This is a data analytics concept that automates the analysis of large amounts of data using AI, machine learning, and Natural Language Processing technologies to offer real-time insights.

What technologies will be the most sought after in 2022? ›

Here is the list of the most in-demand and trending tech skills that will offer you a successful career:
  • Data Science.
  • Cloud Computing.
  • Artificial Intelligence.
  • DevOps.
  • Blockchain.
  • RPA (Robotic Process Automation)
  • Augmented Reality (AR) and Virtual Reality (VR)
  • Cybersecurity.
22 Sept 2022

What are the 4 types of advanced analytics? ›

4 Key Types of Data Analytics
  • Descriptive Analytics. Descriptive analytics is the simplest type of analytics and the foundation the other types are built on. ...
  • Diagnostic Analytics. Diagnostic analytics addresses the next logical question, “Why did this happen?” ...
  • Predictive Analytics. ...
  • Prescriptive Analytics.
19 Oct 2021

What are the 4 phases of analytics? ›

That's why it's important to understand the four levels of analytics: descriptive, diagnostic, predictive and prescriptive.
  • Descriptive analytics.
  • Diagnostic analytics.
  • Predictive analytics.
  • Prescriptive analytics.

Which are the 4 broad categories of analytics? ›

There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process.

Which data analyst has the highest salary? ›

Top 10 High Paying Data Analytics Jobs
  1. Data Engineer. Data engineers are responsible for identifying trends and anomalies in data sets. ...
  2. IT Systems Analyst. ...
  3. Marketing Analyst. ...
  4. Quantitative Analyst. ...
  5. Data Architect. ...
  6. Database Administrator. ...
  7. Data Analyst. ...
  8. Business Intelligence Analyst.
14 Oct 2021

Which field is best for data analyst? ›

Possible majors for data analysts

Data science: In response to the increasing demand for data professionals, more and more schools are offering bachelor's degrees in data science. In this degree program, you'll typically take courses in computer science, statistics, and mathematics.

What is the next level after data analyst? ›

The career path of a Data Analyst typically starts with an Analyst 1 position, and then progresses to Analyst II, Senior Analyst, and then Data Scientist or even Chief Technology Officer. So if you pursue a career in data analysis, you have a long future of steady job growth ahead.

What are the 5 A's of big data? ›

5 A's to Big Data Success (Agility, Automation, Accessible, Accuracy, Adoption)

What are the 3 V's of big data analytics? ›

Dubbed the three Vs; volume, velocity, and variety, these are key to understanding how we can measure big data and just how very different 'big data' is to old fashioned data. Find out more about the 3vs of Big Data at Big Data LDN, the UK's leading data conference & exhibition for your entire data team.

What are the 5 Vs of big data analytics? ›

Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.

What are the next 22 emerging technologies? ›

What next? 22 emerging technologies to watch in 2022
  • Solar geoengineering. It sounds childishly simple. ...
  • Heat pumps. ...
  • Hydrogen-powered planes. ...
  • Direct air capture. ...
  • Vertical farming. ...
  • Container ships with sails. ...
  • VR workouts. ...
  • Vaccines for HIV and malaria.
8 Nov 2021

Which technology is booming now? ›

Virtual Reality and Augmented Reality

Virtual Reality and Augmented Reality have both been technology buzzwords for over a decade now.

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