Data analytics involves analyzing data sets to find useful information for solving problems. It combines computer programming, statistics, and mathematics to provide precise data analysis. The increasing demand for big data-driven decisions is fueling the growth of this market and shaping data analytics trends.
This rapidly evolving field has become crucial for organizations looking to harness the power of data. Data analytics enables businesses to uncover valuable insights, identify patterns, and optimize operations. By leveraging data analytics trends, companies can forecast future advancements and identify risks.
This article discusses the growth of the data analytics market and examines key advancements shaping the future. It also examines how the current data analytics trends are helping to drive innovation.
The significance and potential of data analytics trends are evident in the remarkable market growth. The data analytics market size was valued at $51.55 billion in 2023. By 2030, experts expect it to reach $ 279.31 billion. These numbers represent an impressive compound annual growth rate (CAGR) of 27.3% during the period between 2023 and 2030.
North America dominated the global market geographically, with a substantial 34.68% share in 2022.
The pivotal role of data analytics trends in delivering superior customer interactions highlights its importance. According to a Qualtrics and ServiceNow study, around 83% of customers are willing to switch brands for better digital engagement. Additionally, 70% are more likely to trust brands offering seamless services. By analyzing data, organizations can personalize and enhance customer touchpoints.
Having said that, it is high time to go over the latest data analytics trends that are shaping the industry today.
Augmented analytics, driven by AI and ML, is one of the rapidly growing data analytics trends. This approach uses AI, ML, and NLP to facilitate analysis,
The global augmented analytics market has witnessed significant growth, valued at $8.95 billion in 2023. Experts expect it to reach $11.66 billion in 2024 and surge to $91.46 billion by 2032. The market will exhibit an impressive compound annual growth rate (CAGR) of 29.4% during the forecast period of 2024-2032.
Companies are raising funds to enhance the efficiency of augmented analytics. For instance, Synergies Intelligent Systems secured significant funding to drive its growth. In May 2022, they raised over $12 million through a Series A funding round. NGP Capital led this funding round with additional contributions from New Future Capital (NFC). The funding boosted Synergies Intelligent Systems’ augmented analytics efforts.
One notable trend propelling augmented analytics is the rise of no-code and low-code solutions. These solutions allow the creation of applications through drag-and-drop interfaces without extensive coding. In March 2023, Pyramid Analytics introduced AI-driven augmented efficiency in collaboration with the OpenAI platform. This move aimed to drive the widespread adoption of this data analytics trend with their no-code and AI-assisted capabilities.
In 2023, the BFSI (Banking, Financial Services, and Insurance) industry segment led the augmented analytics market. Banking and financial institutions invested significantly in advanced solutions to enhance decision-making.
However, analysts expect the situation to change. They project the retail industry will gain the highest CAGR over the forecast period. Increased online shopping and the growth of e-commerce supply chains will drive this gain.
Other sectors like healthcare and manufacturing are also driving the increased demand for augmented analytics solutions.
Retail to gain the highest CAGR over the projected period. An increase in online shopping and a rise in the e-commerce supply chain is expected to drive the technology demand. The analytics tools helps enhance patient care, and reduce operational complexities.
Apart from retail, other sectors like healthcare and manufacturing are also driving increased demand. In healthcare, augmented analytics helps enhance patient care quality and reduce operational complexities.
In 2023, North America led the way in augmented analytics, with a market share of $3.03 billion. This was driven by various factors, such as:
Analysts expect the United States to capture the maximum segment share within North America. The dominant presence of leading augmented analytics market players based in the country drives this expectation.
One of the most significant emerging trends is edge analytics. It involves processing and analyzing data where it is generated, typically at the edge of the network close to IoT devices and sensors. This approach is gaining traction due to the massive growth of data being generated every day from billions of connected devices.
According to Gartner, around 10% of enterprise-generated data was created and processed outside traditional centralized data centers or the Cloud in 2021. However, Gartner predicts this figure will skyrocket to 75% by 2025. The need for real-time data analysis and the limitations of conventional cloud computing drive this prediction.
The sheer volume of data being generated is staggering. Ericsson reports that monthly data traffic through fixed wireless access (FWA) was 16.6 exabytes in 2021. Furthermore, it is forecast to reach almost 130 exabytes by 2028.
Seagate expects data generated by IoT devices to exceed 90 zettabytes by 2025. Currently, the world generates over 64 zettabytes of data each year. This data comes from 23.8 billion connected devices. Experts project this number to exceed 180 zettabytes from over 41 billion connected devices by 2025.
Among the key data analytics trends, many enterprises are transitioning to edge computing to handle this deluge of data. It involves processing and analyzing data within devices at the network’s edge. Additionally, IDC predicts that global spending on edge computing will reach $208 billion by the end of 2023, a 13.1% increase over 2022.
The edge analytics market size is estimated at $13.88 billion in 2024. Analysts expect it to reach $41.75 billion by 2029, growing at an impressive CAGR of 24.64% during the forecast period from 2024 to 2029.
Several factors drive this rapid transition toward edge analytics. These include:
Moreover, edge analytics is crucial for industries requiring immediate data analysis and rapid, like healthcare and manufacturing.
The democratization of data remains the top priority trend in data analytics for 2024. Self-service analytics empowers both experts and non-technical users to analyze data without relying on specialized data skills or knowledge.
Traditionally, complex legacy BI tools confined data analysis to the domain of specialists. It forced business users to forgo insights or depend on IT to interpret data for them. However, this outdated approach is evolving with the continued rise of user-friendly self-service BI platforms equipped with:
As a result, regular business users, dubbed “augmented consumers,” can now:
Surveys highlight the significance of this trend:
By democratizing data, enterprises empower numerous employees to become “citizen data scientists.” These individuals can analyze data, albeit not as their primary role. Companies like Coca-Cola have invested in upskilling programs, training over 500 managers in digital skills like data analytics. They have plans to extend the program to more than 4,000 workers in the coming years.
According to Experian research, companies lose between 15% to 25% of revenue due to poor data quality. Gartner analysis shows this factor impairs competitive edge and business goals. A Harvard Business Review report citing IBM found the U.S. economy incurs $3.1 trillion annual loss from data-related inefficiencies. These losses come from reduced productivity, system outages, and maintenance costs.
Prioritizing high-quality data is critical for maximum AI potential. One of the key data analytics trends highlights the importance of this prioritization, leading to two emerging approaches: data mesh and data contracts. Data mesh takes a decentralized, domain-oriented approach, treating data as a product. Data contracts focus on collaborating to:
Investors see potential in data mesh and data contracts approaches. In Fall 2023, NextData, led by Zhamak Dehghani, the author of “Data Mesh,” raised $12 million. Additionally, Gable.ai, a data contracts platform headed by CEO Chad Sanderson, raised $7 million.
Data as a Service (DaaS) also represents one of the most prominent data analytics trends. The DaaS market is surging, fueled by businesses’ growing need for data-driven insights and competitive advantages. The analysis indicates the DaaS market will reach $20.74 billion in 2024. Experts project it to soar to $51.60 billion by 2029, exhibiting a remarkable 20% compound annual growth rate (CAGR).
A notable trend is the high growth potential in the Banking, Financial Services, and Insurance (BFSI) sector. The asset-servicing industry is shifting from service-led offerings to data and technology-driven services. Banks are adapting DaaS to offer reports-as-a-service or analytics-as-a-service. These offerings cater to customers seeking business intelligence insights.
Large national and regional banks prioritize data and analytics. However, smaller banks and financial institutions have yet to embrace the benefits fully. Overall, firms involved in financial analysis or stock markets are expected to benefit significantly from DaaS offerings like Bloomberg Terminal.
DaaS solutions simplify data outputs and generate coherent datasets. They identify trends and reduce data processing time. These capabilities help banking and finance institutions unite datasets in an understandable way. DaaS also ensures data compatibility across systems. Moreover, institutions widely implement DaaS solutions to enable stakeholders to leverage data and create new revenue streams.
For instance, Commerzbank, a major German bank, has developed over 200 APIs. These enable process transformation and add value to partners by offering near-real-time DaaS.
The fintech industry’s growth also highlights the DaaS market’s potential. PitchBook data shows the total value of investments into fintech companies worldwide in 2022 was $226.5 billion. It is a significant increase from $127.7 billion in the previous year. This surge in fintech investments will offer the DaaS market lucrative growth opportunities.
As the DaaS market grows, it not only caters to advanced users but also supports the learning needs of beginners and professionals alike. Many platforms offer beginner-friendly online data analysis courses to help individuals build essential skills from scratch. These courses are designed to be self-paced and interactive, ensuring that learners can easily understand and apply data analytics concepts.
One of the key data analytics trends is the growing use of metadata-driven data fabric solutions. As companies become more complex, they need flexible integrated systems for managing data.
A data fabric is an architecture that allows access to data engineering, analytics, and other data services. It ensures consistent data practices whether data is in the Cloud, on-premise, or at the edge. The data fabric connects different endpoints and provides integration, metadata discovery, governance, and processing.
Analysts evaluated the global data fabric market size at $2.1 billion in 2022. They project that it will hit around $8.9 billion by 2032. The market is poised to grow at a CAGR of 15.54% during the forecast period from 2023 to 2032.
Companies see benefits from adopting data fabric solutions as part of data analytics trends. According to IBM, by 2024, using data fabric can increase return on investment by 158% and reduce extract, transform, and load requests by up to 65%. Moreover, data fabric enables self-service data access and collaboration as its active metadata automates governance, data protection, security, integration, and engineering tasks.
Additionally, data fabric solutions provide several benefits. They reduce the time required for integration design by 30%, deployment by 30%, and maintenance by 70%. These time savings result from reusing and combining different data integration approaches within the data fabric.
In terms of market segments, the fraud detection sector had the highest revenue share of about 27% in 2022. One of the pillars of a cybersecurity environment is a business data fabric that is resilient. Data fabric improves business process effectiveness while enhancing security with appropriate defensive measures. Big Data platforms are able to evaluate transactions in real-time while also spotting unusual user behavior.
When it comes to industry insights, the BFSI sector had the highest income share of over 23% in 2022. The exchange of consumer data with an agreement to enhance the customer experience, spur innovation, and boost productivity is encouraged by many state laws. Open banking’s solution, data fabric, makes it simpler for companies to handle data.
On the other hand, pilot projects in the energy and electricity sectors have been the extent of digitalization efforts; no large-scale initiatives have been put into action. It is primarily caused by aging IT networks that rely on traditional methods.
Geographically, North America had the largest income share of over 47% in 2022. The North American area controls a lion’s portion of the data fabric market. The area is also known for being among the first to embrace cutting-edge solutions. The main driving forces behind the U.S. market are the presence of the majority of data centers.
One of the most significant data analytics trends gaining momentum is the generation of synthetic data. This approach involves creating artificial data algorithmically instead of relying on real-world phenomena.
Industry reports indicate that the synthetic data generation market was valued at $288.5 million in 2022. Moreover, it is projected to grow at a remarkable CAGR of 31.1% during the forecast period, reaching $2,339.8 million by 2030. Notably, North America dominated the global market with a 33.41% share in 2022.
Synthetic data is generated through techniques like statistical modeling and simulations. Experts predict nearly 60% of data for AI and analytics projects by 2024 will be synthetically generated due to these advantages:
A key driver behind the adoption of synthetic data is the rise in deployment of Large Language Models (LLMs). These learning algorithms, such as Generative Pre-trained Transformer (GPT), can translate, generate, and predict text and other content types based on large datasets.
Furthermore, privacy concerns and compliance risks associated with real-world data have contributed to the growth of synthetic data generation. Regulations like GDPR, CCPA, and HIPAA have made it challenging to access real-world datasets. Consequently, synthetic data provides an alternative with similar statistical properties while ensuring privacy.
The natural language processing (NLP) segment held a leading revenue share of over 26% in 2022. It can be attributed to the exponential use of synthetic data to bootstrap new language releases and train NLU stems. For example, in September 2023, Amazon launched the Echo Show and Alexa mobile app to customers in the U.S., U.K., Germany, and Japan. Consequently, the company increased its focus on synthetic data to complete training data or its NLU systems.
Predictive analytics has also emerged as a promising application segment, driven by solid demand from the BFSI sector. Banks and financial sectors are leveraging synthetic data for fraud detection. Notably, in September 2020, American Express reported testing technology to generate fictitious financial data resembling credit card transactions. It involved using Generative Adversarial Networks to identify credit card scams.
The healthcare and life sciences segment accounted for the highest revenue share of 2% in 2022. In this sector, synthetic data provides a solution for data breach risks, patient privacy concerns, and regulatory frameworks. For instance, in May 2022, Anthem Inc. announced a partnership with Alphabet Inc.’s Goo le Cloud to create 1.5 to 2 petabytes of synthetic data. The goal was to improve fraud detection and personalized services.
The future of data analytics is being shaped by several important trends that are changing how organizations use data in major ways. A key part of these trends is making advanced analytical capabilities more accessible. Solutions like augmented analytics that use AI and ML are democratizing data analysis by delivering real-time insights. The rise of self-service analytics platforms allows business users across organizations to analyze data independently.
At the same time, trends like edge analytics address the need to process the huge amounts of data. By analyzing data at the source, organizations can enable real-time decision-making.
Underlying these data analytics trends is a bigger focus on data quality and integrated data management. Approaches like data mesh and data contracts aim to improve reliability and get the full value from data assets.
Additionally, the growth of Data as a Service (DaaS) models streamlines access to data. It gives organizations on-demand analytics capabilities.
Together, these data analytics industry trends provide organizations with tools to drive innovation. As businesses continue adopting them, they position themselves to succeed in the data-driven digital landscape.
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Request CVsIn 2024, several trends are shaping data analysis. Augmented analytics is using AI to provide real-time insights. Edge analytics involves analyzing data at the source for immediate decision-making. Data democratization is enabling non-experts to analyze data independently. Data as a Service (DaaS) offers on-demand analytics capabilities, and synthetic data generation is gaining momentum for privacy and scalability.
The future of data analytics is characterized by decentralized and democratized data management approaches, such as data mesh and data contracts. These methods improve data quality and reliability, enabling organizations to maximize the value of their data assets and make informed decisions more efficiently.
In 2024, data issues include poor data quality, which causes companies to lose 15-25% of revenue. Addressing these issues involves prioritizing high-quality data management and adopting decentralized data approaches.
