10 Top Analytics Trends & Forecasts for 2024 You Should Be Thinking About

Advances in data analytics are changing the way business is done. The role of data analytics was also highlighted even more when the COVID-19 pandemic hit as practically all sectors had to rely on data and its interpretation to predict what the future holds. Analytics trends suggest that our reliance on these technologies—including the best data analytics software—will only grow in the coming years.

Data analysts and businesses continue to collaborate on making use of data better, easier, and more efficient. These 10 data analytics trends epitomize such a fruitful collaboration. The insights below will help you refine an analytics strategy, whether you’re at the planning stage or you want to pivot midway.

Big Data Analytics Automation
In-Memory Computing
Augmented Analytics
NLP and Conversational Analytics
IoT-Analytics Integration

Data-as-a-Service
Explainable AI
Big Data Security Analytics
Data Governance
Graph Analytics

Data has finally taken center stage in this digital economy. It’s now clear how data and analytics continue to transform the business world. In a study, almost half of businesses agree that big data and analytics had altered how things are done in marketing (McKinsey, 2018), sales, and other activities.

To survive in today’s highly competitive market, businesses must have three integrated elements (Harvard Business Review). They need disruptive business models, agile product innovation, and actionable customer insights.

The Most Important Capabilities of the Data-Driven Enterprise

Can extract new value and insights from existing data/analytics applications

Can provide enhanced security and access controls for enterprise systems

Can deploy analytics with high performance and scalability

Can easily access and combine data from various external data sources

Can certify data and create and enforce a single version of the truth

Can deliver actionable, customized intelligence to staff across the enterprise

Source: Harvard Business Review Analytic Services Survey

The world now produces 2.5 quintillion bytes of data each day. Over 90% of all existing data was made during the last five years (Domo). It is also estimated that by 2025, around 463 exabytes will be created every day globally (Visual Capitalist, 2019).

With so much raw information around, the biggest challenge is separating the signal from the noise. Analytics should help businesses and organizations cut through all the data to discover actionable insights and create value, especially at this time when enterprises must maximize the resources they have to stay afloat amid the pandemic.

New developments have emerged, albeit with some old problems taking on new appearances. As innovations succeed to improve analytics, new challenges arise to complicate things. This is why it is essential to look deeper at these business analytics trends that will dominate 2021 and beyond.

1. Big Data Analytics Automation

Automation continues to transform the world. In business, automation had spurred transformations that result in sustained efficiency. In recent years, the automation of big data analytics offers perhaps one of the biggest of automation’s capabilities. This has eventually paved the way for analytic process automation (APA), which is believed to help anyone unlock predictive and prescriptive insights, leading to quicker wins and faster ROI (Alteryx).

Data analytics automation is truly a disruptive force. One survey found that 48% of executives consider data analytics as critically important (Snowflake, 2018).

The total global information is growing twice every 18 months. This technology will help hasten productivity and increase valuable data usage.

Analytics + automation = computing power for business

Automation of big data analysis offers numerous benefits to businesses. It will enable executives to effectively predict further ahead. This will help to steer their organizations using the correct analytics to support decision making.

Data analysis automation has added benefits like enhanced scalability of big data technologies and improved self-service modules (Dataversity, 2017). Also, it helps improve operational efficiency and reduce operational costs.

One notable feature is that it can search for categorical data to create a set of features with relevant values. In an ecommerce business, it can run as a numerical identifier that flourishes across massive datasets.

Source: Insights Association, 2020

2. In-Memory Computing

Another major trend that’s expected to make a considerable mark in 2021 is in-memory computing (or IMC). Since the cost of memory has decreased lately, IMC is now a major technological solution (GigaSpaces, 2021) that offers numerous advantages in analytics.

Data is commonly stored in a centralized database. With IMC, data storage happens in RAM across various computing devices (HPE, 2019). This innovation results in agile performance and allows real-time data scaling. It can solve bandwidth bottleneck issues in today’s systems and processes, including analytics (Semiconductor Engineering, 2019).

Starting in 2021, more businesses will turn to IMC for their computing needs. This analytics can expand the efficiencies of business intelligence solutions.

No more space problems

IMC effectively solves the actual speed and massive scalability needs (The New Stack, 2018) of businesses. In turn, this helps firms address complex requirements. These include addressing the demands for real-time regulatory compliance, omnichannel marketing, and digital transformation.

It is a proven enabling technology (Cognizant) today but is expected to undergo more developments. IMC offers a remarkably robust mass-memory to facilitate high-performance business activities.

IMC is part of the memory-centric architecture (ZDNet, 2017). This larger technology initiative aims to support the more efficient use of memory and other storage types.

The increasing use of IMC in data analytics will be a significant trend in the years to come.
IMC allows data storage to happen not in centralized servers but in RAMs across various computers.
It provides a very powerful mass-memory capability to support heavy business tasks.

3. Augmented Analytics

One of the major predictive analytics trends today is the increasing use of augmented analytics. It uses artificial intelligence and machine learning protocols to transform how analytics data is generated, processed, and shared.

By deploying sophisticated algorithms, this trending analytics tool can provide context-aware insight suggestions, automate tasks, and facilitate conversational analytics (Qlik). In the process, it can drastically lessen businesses’ long-time reliance on data scientists and analysts.

Augmented analytics will spur major developments

In 2021 and beyond, augmented analytics will become a key factor behind the growth of analytics and BI platforms. Also, it will assume a critical role in the advancement of embedded analytics and data science platforms.

The rising volume of business data is one of the major drivers of augmented analytics deployment (Oracle, 2019). Likewise, the increasing demand for obtaining critical insights from customer data is boosting its widespread use.

Because of its sophisticated application portfolio, the demand for augmented analytics continues to rise in some sectors. These include the aerospace, defense, and transportation industries.

The use of augmented analytics continues to increase due to its wide application cases.
It uses advanced ML and AI protocols for generating, processing, and sharing data.
Augmented analytics will be a significant driver in spurring new buy-ins of analytics and BI platforms.

4. NLP and Conversational Analytics

Conversational data offers information on how people interact with a chatbot or device. Conversational analytics helps in processing these valuable datasets. With an AI-based analytics tool, you can track and analyze these data in real-time and deliver the correct response.

Most BI and analytics platforms can process inquiries posted on a page and provide visual analysis. But NLP-conversational analytics elevates this convenience a step further. Users can post questions to be as simple as a discussion with a digital assistant or a Google-like search. This is why In 2021 and beyond, NLP is foreseen to play a key role in monitoring and tracking market intelligence as businesses utilize data and information to formulate future strategies (Analytics Insight, 2020).

The role of NLP-conversational analytics will be huge

Any person can search or make inquiries using voice or text with more intricate queries and responses. To expand their use and development, these analytics tools must be more accessible and user-friendly.

This trend allows employees to quickly analyze complex data combinations with an easy-to-use analytics solution. With an NLP-conversational analytics tool, you only need to enter a basic search query, and results will be provided.

What’s more, business users can initiate conversations with virtual assistants (Chatbots Magazine, 2017) to retrieve data. With every interaction, the capabilities of the NLP-conversational analytics platform will grow exponentially.

Voice Search Demographics

in millions of users

Source: eMarketer

NLP-conversational analytics allows easy monitoring and analysis of customer data in real-time.
With more than half of consumers utilizing voice search through devices such as smart home speakers, market analytics is bound to involve voice search analytics.
Using this sophisticated analytics tool enables users to efficiently analyze complex data combinations.

5. IoT-Analytics Integration

There will be over 35 billion Internet-of-Things connected devices this 2021(Security Today, 2020). IoT’s growth is expected to have a significant impact on numerous business activities. And one of the most affected will be data analytics.

As the number of IoT sensors that get connected to devices increases, a continuously-growing volume of data is created. However, these data can only be useful if handled and processed correctly. This is why data analytics will be vital to exploring the vast possibilities these new massive datasets will bring.

Businesses are expected to increasingly turn to advanced IoT-analytics solutions. These sophisticated tools can provide relevant data and the needed data transparency to the general public.

What’s the impact of IoT-analytics integration?

Combining data analytics and IoT offers a wealth of benefits and possibilities for businesses. For instance, IoT devices will constantly generate large volumes of data and diverse data structures. In turn, data analytics software and leading business intelligence toolswill enable companies to analyze the information, no matter what structure or size.

Likewise, the combined IoT-analytics use offers a powerful source for gaining actionable market insights. This will help a lot in designing a seamless customer experience, which translates to better profits. Deploying the I0T-analytics tandem can provide a competitive edge in the long run.

This innovation will likely be one of the customer analytics trends for years to come.

Source: Statista 2019

The massive growth of IoT will create considerable possibilities for businesses through analytics.
The combination of IoT and data analytics offers a powerful way to leverage massive amounts of customer data.
IoT-analytics can provide actionable insights, better CX, and higher ROI.

6. Data-as-a-Service

Data-as-a-service (DaaS) is a cloud-based technology that enables subscribers to access and use digital files through the internet. The challenges created by the COVID-19 pandemic have opened growth opportunities for the DaaS industry. This market is expected to reach a value of $12 billion by the end of 2023, at a CAGR of 39% (Market Research Future, 2021).

DaaS is a data stream that subscribers can access on-demand (PredictHQ). As almost every modern business has embraced data as a decision-making tool but only a few companies have the internal resources to completely leverage the power of their collected data, DaaS is posed to be a viable solution. Furthermore, as most users can now easily access high-speed internet, DaaS will have a broader reach. This trend will likely be one of today’s top data and analytics advancements.

DaaS democratizes big data usage

Before, working with massive data is very challenging. You need an extensive amount of computing resources for data processing and storage. Since this involves the use of enterprise-grade data centers, it’s very demanding financially and resource-wise.

Thanks to the cloud-based option that DaaS introduced, most of these data processing and storage are now more affordable and less resource-intensive.

Its growth will help units of large companies to better collaborate without any added expense. This innovation will streamline data sharing. Ultimately, DaaS will help enhance business productivity.

Source: PWC

The use of data-as-a-service is growing.
DaaS is a data stream that subscribers can access on demand.
It doesn’t require additional costly investments. It also makes data sharing easier.

7. Explainable AI

People have always been cautious about the possibility of machine invasion. It’s not a sci-fi fear anymore. More than 70% of Americans are concerned about robots taking over their very livelihood (Pew Research Center, 2017). This concern could have resulted from the fact that around 400,000 jobs in US factories were lost to automation from 1990 to 2007. What is more, this concern is further fueled when 40% of jobs became lost to bots during the COVID-19 pandemic (Time, 2020).

This is why there’s always the demand for a complete understanding of how AI systems reach the decisions they make. When trust and explainability are lacking, our capability to completely trust AI systems is hampered.

We want advanced computer systems to work as we expect them to. We want them to provide transparency in how they reach decisions. This is the existential cause of the emergence of Explainable AI or XAI.

It’s all about trusting the machines that work for us

A developing field in machine learning, Explainable AI (XAI) seeks to determine how AI systems make black box decisions. It examines and aims to appreciate the various decision-making models and processes involved.

When did the AI system fail, and when did it succeed? Why did it fail, and why did it succeed? Why didn’t the AI system do something else? These are among the key questions that guide this emerging analytics field.

Businesses have been gradually deploying AI systems to help make better decisions. But there are situations when they must legitimize how these systems were able to reach such decisions. XAI fills this gap for the needed interpretation and reasoning.

In due time, we can have both accuracy and capability with the needed explainability and transparency.

The demand for complete transparency of how AI systems work intensifies.
XAI addresses this growing requirement.
This trending analytics field aims to understand better how AI systems reach the decisions they make.

8. Big Data Security Analytics

Traditional strategies for data security can’t cope with how sophisticated cybercriminals have become. Their advanced methods are likely to advance further. With malicious insiders involved in recent major security breaches, the stakes will increase more.

With 72% of data breach victims being large businesses while the remaining 28% are SMEs (Verizon, 2020), today’s security situation requires more efficient detection strategies. Fortunately, big data analytics offers that needed capability.

Experts expect a cyber-attack attempt to happen every 11 seconds (Cybersecurity Ventures, 2019). This is why security experts have designed the appropriate detection approach. This is the reason behind the emergence of a new class of big data security analytics (BI Survey).

Security analytics to the rescue

To be effective against privacy cybercrimes, detection must be capable of determining the shifting use patterns. Likewise, it should be able to perform complex, intricate analysis quickly, close to real-time.

Big data security analytics are capable of collecting, storing, and analyzing large security data in near real-time. It can easily run complicated correlations involving large chunks of data. It can manage extensive sources of data, from user activities and network events to application logs.

This analytics-based security protocol is further augmented by external threat intelligence and supplementary context data. This is different from conventional IT security software: these tools create a few security alerts ranked per severity.

Enhanced with added forensic details, they simplify the entire security analysis. These tools also allow users to quickly detect and mitigate cyberattacks.

Top Challenges in Big Data Security Analytics

Data privacy/security

Costs

Relevant data not collected

Inadequate analytical know-how in the company

Lack of resources/investment

Lack of awareness in the organization

Source: Kuppingercole & BARC

The limitations of traditional security methods resulted in the emergence of big data security analytics.
Big data security analytics can collect, store, and analyze huge data near real-time.
These tools empower business users to detect and mitigate cyberattacks fast.

9. Data Governance

Recently, GDPR compliance helped bring order to the handling of data across the world. It had compelled businesses and organizations to prioritize data governance. Its May 2018 implementation was done so quickly that even up to now, many are still yet to fully comply.

This groundbreaking law has vastly improved consumer data protection. In 2020, another major internet-related law was made effective: the California Consumer Privacy Act (CCPA).

Along with GPDR, this new policy will further elevate business compliance on data security, consumer profiling, and data handling. The pressure will not only focus on the compliance aspect. It will also be on how this new law will affect business operations.

What are the effects of this new data policy?

CCPA provides consumers with comprehensive control over their personal data. It is the most sweeping privacy law in the US by far.

Furthermore, CCPA gives California consumers broader rights to protecting their personal information. These include the legal prerogative to:

secure a copy of the personal data gathered about them
ask for such information to be deleted
reject its sale to third parties.

Moreover, this law gives them the right against discrimination when using such rights. Businesses will be given up to 45 days to act on such requests from consumers.

It’s best to uphold this law and prepare for others that may come in the future. Non-compliance in this age of social media brings businesses too much negative impact. Deploying compliance management software is a good first step.

The new CCPA was enacted in January 2020.
The CCPA is the most all-encompassing privacy law in the US in recent years.
Businesses must follow it to ensure better compliance and prepare for others like it that may come in the future.

10. Graph Analytics

Everything is connected. Each element of the physical and virtual worlds is related to the other elements.

These connections are complicated, inexplicit, and challenging to identify. Thanks to graph analytics, we can now create a clear structure that can represent the relationship using lines and dots.

This type of innovative analytics makes something highly complicated, easier to understand, and use. It makes typically complex activities like analyzing big data to result in visually engaging outcomes.

What are the benefits of graph analytics?

Also called graph database (Cleverism, 2019), this tool leverages graphs for analyzing, codifying, and visualizing data or devices. Graph database emerges as a critical trend to replace the old relational database.

Companies using graph analytics have managed to considerably improve business decision making. They’re able to draw insights from the interrelationship of data. In the process, they’re able to achieve cost and time savings.

Graph analytics usage will grow 100% every year until 2022. You need robust data visualization software to take advantage of this trend.

Graph analytics allows the creation of clear structures that represents relationships among complex data.
This tool uses graphs to analyze, codify, and visualize information and other things.
Businesses’ use of graph analytics enables better decision making and other efficiencies.

How to leverage these analytics trends?

Data has become the new oil that powers today’s digital economy. It propels the engines that run businesses and industries. It has also helped businesses ensure continuity amid the COVID-19 pandemic. This ongoing shift to data will expectedly bring in some growing pains for many organizations.

Investments in analytics will continue to increase. Businesses will have to make considerable adjustments to experience the returns they’re expecting. They will encounter challenges as they scale their analytics use across the organization.

The above analytics trends indicate that the business world is quickly evolving to become data-centric. Be it automation, AI, IoT, or new privacy regulations, knowing these trends is crucial.

To help you sustain your success in the coming years, it’s also great to know today’s relevant analytics statistics.

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By Jenny Chang

Jenny Chang is a senior writer specializing in SaaS and B2B software solutions. Her decision to focus on these two industries was spurred by their explosive growth in the last decade, much of it she attributes to the emergence of disruptive technologies and the quick adoption by businesses that were quick to recognize their values to their organizations. She has covered all the major developments in SaaS and B2B software solutions, from the introduction of massive ERPs to small business platforms to help startups on their way to success.