AI & Analytics: Trends 2022-2023 and Market Research

1 Leading Analytics Trends and Forecasts to Consider for 2022/2023

2 The best ways to take advantage of these analytics trends

3 AI Market Research

Leading Analytics Trends and Forecasts to Consider for 2022/2023

In our digital economy, data has finally taken center stage. It’s clearly obvious how data and analytics are transforming the commercial sector. According to research, nearly half of firms agree that big data and analytics have changed how they do business in marketing, sales, and other areas (McKinsey, 2018).
Businesses must have three interconnected features to survive in today’s highly competitive market (Harvard Business Review). Disruptive business models, agile product innovation, and actionable customer insights are all things they require.
The world produces 2.5 quintillion bytes of data per day. More than 90% of all available data was created in the last five years (Domo). Globally, roughly 463 exabytes will be created every day by 2025, according to estimates (Visual Capitalist, 2019).
With so much raw data available, extracting the signal from the noise is the most difficult task. Analytics should aid businesses and organizations in sifting through massive amounts of data to uncover meaningful insights and create value, especially at a time when companies are scrambling to stay afloat in the face of the epidemic.
New developments have developed, though some old issues have taken on new forms. As new technologies improve analytics, other obstacles emerge, further complicating the situation. As a result, it’s critical to delve deeper into the business analytics trends that will rule in 2021 and beyond.

1.1 Big Data Analytics Automation

The world is still being transformed by automation. In the business world, automation has sparked reforms that have resulted in long-term efficiency gains. Automation of big data analytics has become one of the most important capabilities of automation in recent years. This paved the way for analytic process automation (APA), which is thought to enable anyone to discover predictive and prescriptive insights, resulting in faster wins and ROI (Alteryx).
Automation of data analytics is a truly disruptive factor. According to one survey, 48% of CEOs regard data analytics to be crucial (Snowflake, 2018).
Every 18 months, the total amount of information on the planet doubles. This technology will assist boost productivity and making better use of vital data.
Business computing power = analytics + automation
Businesses can benefit from the automation of big data analysis in a variety of ways. It will allow CEOs to make more accurate predictions in the future. This will assist them in steering their businesses in the right direction by employing the right analytics to support decision-making.
Automation of data analysis has further advantages, such as increased scalability of big data technologies and improved self-service modules (Dataversity, 2017). It also aids in increasing operational efficiency and lowering operating costs.
It may search for categorical data to build a set of features with relevant values, which is a remarkable feature. It can function as a numerical identification in the e-commerce business and thrive across large databases.

1. 2 In-Memory Computing

In-memory computing is another key topic that is projected to gain traction in 2021. (or IMC). IMC is now a prominent technological option (GigaSpaces, 2021) that offers numerous advantages in analytics, thanks to recent decreases in memory costs.
A centralized database is widely used to store data. Data is stored in RAM across several computing units with IMC (HPE, 2019). This innovation enables real-time data scaling and agile performance. It can alleviate bandwidth constraints in today’s systems and processes, such as analytics (Semiconductor Engineering, 2019).
More enterprises will turn to IMC for computing needs starting in 2021. Business intelligence solutions can benefit from this type of analysis.
There will be no more space issues.
IMC successfully addresses enterprises’ actual speed and large scalability requirements (The New Stack, 2018). As a result, businesses are better able to deal with difficult needs. Addressing real-time regulatory compliance, multichannel marketing, and digital transformation is among them.
Today, it is a proven enabling technology (Cognizant), but it is projected to evolve further. To support high-performance business activities, IMC provides a highly robust mass memory.
The memory-centric design includes IMC (ZDNet, 2017). This wider technical endeavor intends to help people make better use of memory and other types of storage.

1.3 Augmented Analytics

The rising use of augmented analytics is one of the primary predictive analytics developments today. It transforms how analytics data is generated, analyzed, and shared by utilizing artificial intelligence and machine learning protocols.
This trending analytics platform may deliver context-aware insight ideas, automate tasks, and promote conversational analytics by deploying powerful algorithms (Qlik). As a result, businesses’ long-standing reliance on data scientists and analysts may be significantly reduced.
Major advancements will be sparked by augmented analytics.
Augmented analytics will be a major driver of the expansion of analytics and BI platforms in 2021 and beyond. It will also play an important part in the development of embedded analytics and data science systems.
One of the key drivers of augmented analytics implementation is the growing number of company data (Oracle, 2019). Similarly, the growing demand for essential insights from customer data is fueling its wider use.
The need for augmented analytics continues to develop in several industries due to its sophisticated application portfolio. The aerospace, defense, and transportation industries are among them.

1.4 Natural Language Processing and Conversational Analytics

Conversational data reveals how individuals communicate with a chatbot or device. Conversational analytics aids in the analysis of these important datasets. You may track and analyze these data in real-time with an AI-based analytics tool and respond appropriately.
Most BI and analytics tools can process and analyze questions that are placed on a page. However, NLP-conversational analytics takes this ease to the next level. Users can ask inquiries that are as basic as having a conversation with a digital assistant or conducting a Google-like search. As organizations use data and information to build future strategies, NLP is expected to play a crucial role in monitoring and tracking market intelligence in 2021 and beyond (Analytics Insight, 2020).
NLP-conversational analytics will play a significant role.
With more complex queries and responses, anyone can search or make inquiries using speech or text. These analytics solutions must be more accessible and user-friendly in order to expand their use and development.
With an easy-to-use analytics tool, employees may swiftly examine complicated data combinations. You merely need to enter a basic search query into an NLP-conversational analytics tool to get results.
Furthermore, corporate users can conduct discussions with virtual assistants to retrieve data (Chatbots Magazine, 2017). The NLP-conversational analytics platform’s capabilities will develop rapidly with each encounter.

1.5 Integration of IoT and Analytics

By 2021, there will be more than 35 billion Internet-of-Things-connected gadgets (Security Today, 2020). The expansion of the Internet of Things is predicted to have a substantial impact on a variety of corporate activities. Data analytics will be one of the most affected industries.
As the number of IoT sensors that are connected to devices grows, so does the amount of data produced. These data, on the other hand, can only be beneficial if they are handled and processed correctly. This is why data analytics will be critical in unlocking the huge potential of these new massive databases.
Advanced IoT analytics solutions are predicted to become more popular among businesses. These advanced tools can supply the general public with useful data and the necessary data openness.
What is the impact of integrating IoT analytics?
Businesses can reap a slew of benefits and opportunities by combining data analytics and IoT. IoT devices, for example, will generate vast volumes of data with a variety of data structures on a regular basis. As a result, data analytics software and premier business intelligence tools will enable businesses of any size or structure to evaluate data.
Similarly, combining IoT with analytics provides a valuable supply of actionable market knowledge. This will greatly aid in the creation of a smooth consumer experience, which will result in increased revenues. In the long run, deploying the IoT analytics combination can create a competitive advantage.
For years to come, this innovation will most certainly be one of the consumer analytics trends.

Leading Analytics Trends and Forecasts to Consider for 2022/2023

The best ways to take advantage of these analytics trends
The new oil that drives today’s digital economy is data. It powers the machinery that keeps businesses and industries running. It has also aided businesses in maintaining operations during the COVID-19 pandemic. Many organizations will experience growing pains as a result of the ongoing shift to data.
Analytics investments will continue to rise. Businesses will have to make significant changes in order to see the expected results. As they expand their analytics use across the organization, they will face hurdles.
According to the aforementioned analytics trends, the business sector is rapidly transforming to become data-centric. Knowing these trends, whether it’s automation, AI, IoT, or new privacy legislation, is critical.

2.1 Growth of analytic trends

The artificial intelligence (AI) software market has been growing at a breakneck pace; for example, the current artificial intelligence market projection shows that the sector is being pushed by an increase in the category’s use cases. Growth will not be limited to the software business, as AI is projected to have a favorable economic impact as well. The following are some important artificial intelligence market data to be aware of.

2.2 AI and marketing

AI has become a critical component of marketing, giving rise to the term AI marketing. This entails using AI to assist organizations in determining the best way to reach out to their clients. Of course, the process’ major purpose is to improve targeting, personalization, and customer experience. This is accomplished by using consumer data as a foundation. Search engines, information filtering/analysis, search engines, picture recognition and visual search, sentiment analysis, and social listening are all common uses of AI in top marketing analytics solutions.

AI Market Research

In order to accelerate their digital transformation, 40% of SMBs have not invested in any new techniques in 2020. AI is used for marketing by 28% of top-performing companies. 2018.
In 2020, targeting and personalization were expected to be prioritized by 28% of mainstream firms and 25% of CX leaders. 2020. 64 percent of major firms use AI to automate specific marketing-related processes, up 20% over the previous year’s figure. 2020 Only 12 percent of SMBs use AI and bots to drive campaigns and experiences, compared to 29 percent of large enterprises. 2020.
The Artificial Intelligence Platform Market report is a comprehensive examination of the market’s features, size, growth rate, segmentation, regional and country analysis, competitive landscape, company shares, development trends, and business strategies.

3.1 Statistics about AI market research

This study examines and analyses regional market trends, as well as consumer demographics, which are the bedrock of any business. The market study includes expert views on global industries, research elements, new products, company profiles, and market trends.
According to the analysis, the market will reach a spectacular value during the projected period of 2022 to 2028, with an exponential Compound Annual Growth Rate.
During the forecast period, between 2022 and 2028, the global Artificial Intelligence Platform market is expected to grow at a significant rate. The market is likely to grow at a steady rate through 2021, thanks to key players’ increasing adoption of tactics.
Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, as opposed to the natural intelligence (NI) displayed by humans and other animals, and is the subject of this article. Artificial Intelligence Platform refers to software that can act intelligently. Simulating a variety of capacities, such as thinking, learning, problem-solving, sensing, and knowledge representation is a key component of developing intelligent software
The global Artificial Intelligence Platform market is expected to grow at a CAGR of 28.0 percent between 2022 and 2028, from USD 22600 million in 2021 to USD 141990 million in 2028.
With a market share of almost 33%, the United States is the largest Artificial Intelligence Platform market. China is a latecomer, with a market share of around 24%. Google, Baidu, IBM, Microsoft, SAP, Intel, Salesforce, Brighterion, KITT.AI, IFlyTek, Megvii Technology, Albert Technologies,, Brainasoft, Yseop, Ipsoft, NanoRep(LogMeIn), Ada Support, Astute Solutions,, Wipro, and others are among the leading manufacturers. The top three corporations controlled around 55% of the market.
The study makes a spectacular endeavor to disclose important opportunities available in the worldwide Artificial Intelligence Platform market to help businesses achieve a strong market position, with industry-standard precision in analysis and excellent data integrity. The report’s buyers will have access to validated and trustworthy market forecasts, such as those for the worldwide Artificial Intelligence Platform market’s overall revenue size.
Overall, the study proves to be a useful tool for firms looking to get a competitive advantage over their rivals and achieve long-term success in the global Artificial Intelligence Platform market. All of the report’s results, data, and facts are verified and reauthorized using credible sources. For an in-depth examination of the worldwide Artificial Intelligence Platform market, the analysts who prepared the report used a unique and industry-best research and analysis approach.

AI & Analytics: Trends 2024 and Market Research

A new year brings new technological challenges and business opportunities as well as accelerates the digital transformation in the corporate landscape.