Top 10 Data Analytics Trends in 2021

Learn about the latest data analytics trends in analytics and technology so you can better predict customer needs, personalize content, and achieve your objectives.

Your firm is susceptible if you don’t employ analytics because it gives you vital information into what is and isn’t working. If your firm doesn’t know what mistakes it’s made with its business decisions, marketing performance, or customer interactions, it’ll continue to make them. Data analytics assists company decision-makers in determining their industry position, as well as determining which fields in their products and services are needed, why sales may have reduced or increased, and where there may be a market potential.

A brand may easily anticipate client demands, personalize content, boost its bottom line, and achieve its goals by utilizing modern analytics and technologies. Analytics and systematic statistical reasoning are being used by businesses to make decisions that improve efficiency, risk management, and profits while keeping inventories, pricing solutions, and employing talent.

Embedded analytics solutions (the integration of analytical capabilities and data visualizations into another software program) allow firms to act faster by integrating analytics directly into user-facing apps. By making analytics accessible to important stakeholders, including customers and workers, embedded analytics may simplify the process of creating insights and reduce the time it takes to run data analysis and make meaningful suggestions.

Since the COVID-19 epidemic, we’ve all seen how healthcare organizations have acted as frontline leaders. More than 500 medical treatment and vaccine trials are using a living patient database to aggregate and curate data from trial registries and other sources in response to this need. These aid medical health professionals in predicting the spread of the disease, developing a novel treatment procedure, and devising a clinical management strategy in this severe situation. As a result, we can argue that data and analytics, paired with artificial intelligence (AI) technologies, are critical in the endeavor to forecast, prepare for, and respond to a global crisis and its aftermath in a proactive and timely manner.

According to Gartner, enterprises will start to overcome the 80% failure rate of ML deployment and successfully integrate it into a production environment.

Gartner research VP Rita Sallam also considers the below trends as the latest AI tech demand that will level up business in a new way in 2020.

1. Data Analytics Trends: AI With Smart Solutions

By 2024, data streaming and analytics infrastructure will have grown 5X, and 75% of businesses will have moved from piloting to operationalizing AI. AI approaches like reinforcement learning and distributed learning, according to Gartner’s research VP Sallam, are producing more flexible and versatile systems to handle complicated business circumstances, especially now since pre-COVID models rely on historical data that may no longer be valid.

(NLP) provides critical information and forecasts about the virus’s spread, as well as the effectiveness and impact of countermeasures. Artificial intelligence and machine learning are now crucial in realigning supply and the supply chain to meet changing demand patterns. More substantial investments have resulted in the development of novel chip architecture, such as neuromorphic technology, which can be used on edge devices. These technologies reduce reliance on centralized systems that demand large bandwidths while also speeding up AI and ML computations and workloads. As a result, more scalable AI solutions with a greater business impact are created.

2. A Modified Version of Dashboard

Data stories (rather than dashboards) will become the most common means of consuming data in 2025, according to Gartner, and will supplant visual, point-and-click creation and exploration. One-third of these stories will be generated automatically utilizing augmented analytics techniques, according to the researchers.

Users usually have to do a lot of manual work in Dashboard to get deeper insights. Because of the move to in-context data stories, the most relevant insights will be delivered to each user based on their context, role, or use. NLP, enhanced analytics, streaming anomaly detection, and collaboration are all used to create these dynamic insights. The amount of time customers spend utilizing predefined dashboards will automatically decrease.

3. Decision-Intelligence Power of Better Decision-Modeling

By 2023, about 33% of commercial firms will have decision intelligence analysts using decision modeling.

A single thread termed decision intelligence connects several disciplines, including decision management and decision support. Decision intelligence, according to Gartner, is a practical domain that includes applications in the realm of complex adaptive systems. These complicated adaptive systems combine classic methodologies (such as rules-based approaches) and modern disciplines (such as AI and machine learning).

Using the decision intelligence framework, data analytics leaders can design, compose, model, align, execute, monitor, and tune decision models and processes in the context of business outcomes and behavior.

4. Data Analytics Trends: X-Analytics

Gartner invented the phrase X- analytics, in which X signifies the data variable for a variety of structured and unstructured information, including text analytics, video analytics, audio analytics, and more. This X analytics is used by analytics professionals to solve complicated challenges such as climate change, illness control, and wildlife conservation.

AI is now doing outstanding work for video, audio, vibration, text, emotion, and other content analytics, according to Gartner, which will cause large-scale innovations and transformations in 75 percent of Fortune 500 organizations by 2025. According to the experts, X analytics paired with AI and other approaches such as graph analytics (another hot trend) will play a crucial role in recognizing, predicting, and planning for natural disasters as well as other business problems and opportunities in the future. According to Sallam, AI techniques and their application in the cloud are evolving, allowing for greater acceptance and effect of X analytics.

5. Augmented Data Management

Machine learning, data fabrics, and active metadata are already being used by organizations to dynamically connect, optimize, and automate data management operations, reducing time to data delivery by 30% by 2023.

With the use of machine learning and artificial intelligence, augmented data management may transform metadata from auditing, provenance, and reporting to powering dynamic systems. This solution may also look at a large amount of operational data, such as actual queries, performance data, and schemas.

Considering the above facts, data analytics leaders focusing on augmented data management for enabling active metadata to simplify and consolidate their architectures, as well as increase automation in their redundant data management tasks.

6. A Better Platform With Cloud Technology

By making AI one of the top workload categories, businesses may expect a 5X increase in cloud AI between 2019 and 2023. 90% of data and analytics innovation will be driven by the public cloud service sector. This trend began long before the pandemic, but the influence of COVID-19 on the enterprise has likely expedited it. In the case of cloud vendors, the more data and analytics you conduct in their cloud, the more computing you will do in their cloud. Leaders in the workplace can manage their work more quickly and effectively by embracing the new cloud stack.

Simultaneously, data and analytics professionals continue to struggle to match the proper services to the relevant use cases, resulting in unnecessary governance and integration costs.

As a result, while shifting to the cloud, data and analytics professionals should prioritize workloads that may benefit from cloud capabilities, as well as cost optimization and other benefits such as change and innovation acceleration.

7. Data Analytics Trends: A Collision Between Data and Analytics Trends

According to Gartner, about 95% of Fortune 500 firms will have integrated analytics governance into broader data and analytics governance activities in the near future. Data analytics are unique capabilities that must be managed as such. Vendors are now offering end-to-end processes that are enabled by augmented analytics trends, blurring the lines between formerly distinct markets.

The different responsibilities of data and analytics interact and collaborate as a result of this collision. It will have an influence not just on the technology and capabilities delivered, but also on the people and processes that support and employ those technologies and capabilities.

However, it is now up to the leaders and analytics service providers to figure out how to turn this collision into a positive convergence by incorporating both advanced data-analytics tools and capabilities into the analytics stack. Simultaneously, they must concentrate on people and procedures in order to promote communication and collaboration.

8. Practical Implications of Blockchain

Researchers mentioned Blockchain technologies address two challenges of data analytics trends. It provides:

The full lineage of assets and transactions.
Transparency for complex networks of participants.

Blockchains aren’t inherently more protected than alternative data sources. Within the data and analytics realm, it’ll be used for vertically specific, business-driven initiatives like smart contracts. By 2021 Gartner projects the majority of the blockchain uses will be replaced by ledger DBMS products.

9. Data Marketplace and Exchanges

By 2022, 35% of major organizations would-be purchasers or sellers of data through formal online data marketplaces, up from 25% in 2020. These markets and exchanges combine third-party offerings into a single platform. Furthermore, these allow for centralized availability and access, resulting in cost savings for third-party data.

At the same time, market leaders should establish a fair and transparent methodology to monetize data assets through data marketplaces by defining a data governance principle.

10. Relationship Between the Foundations of Data and Analytics Value

By 2023, graph technologies will be used by 30% of global organizations to help with rapid contextualization for decision-making. Graph analytics is a set of analytic approaches for investigating links between entities of interest, such as companies, people, and transactions.

Following the COVID-19 pandemic, graph analytics is being used extensively to respond to current and future pandemics. Medical and public health specialists have been able to fast uncover new therapeutic remedies thanks to machine learning algorithms and new analytics trends.

Investigating how graph algorithms and technologies can improve AI and ML initiatives, as well as evaluating new opportunities to incorporate graph analytics into their analytics portfolios and applications to uncover hidden patterns and relationships, is something data and analytics leaders are considering.

The inherent nature of the business is to keep evolving and constructing a brighter future for us through analytics. Data analytics trends in the future will play a key part in changing any firm. It may now be an issue of how a company may align those data analytics trends in order to achieve success. With an intelligence-driven decision-making method, analytics service providers have made the situation simple. They use an analytics vision and architecture to improve company performance and a decision support system to help organizations make better decisions. It’s time to choose the ideal analytics strategy for your company and secure the desired position in a competitive market.

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