6 Best Big Data Analytics Trends and Predictions for 2022

Data analytics is becoming more popular in companies to reduce costs, improve customer experience and optimize processes. Machine learning and artificial intelligence are changing how we work. This is what we can expect in the field in 2019.

The quiet murmur of big data analytics has become a roaring ebb in recent years. Wikibon statistics predicts that the market for big data analytics will grow to $103 billion in 2023, according to Wikibon statistics.

Data science is being applied in many industries, resulting in technologies like artificial intelligence, machine learning, and natural language processing that are quickly changing how we work.

This field will continue to grow as more companies adopt data analytics to reduce costs, improve customer experience and optimize current processes. Here are the six trends in big data analytics for 2022.

6 Best Big Data Analytics Trends

1. Predictive Analytics

By 2026, the global predictive analytics market will have reached $28.1 billion. Predictive analytics can be described as the practical outcome of big data and business intelligence.

Predictive analytics is used by many companies to apply machine learning/artificial intelligence algorithms and conduct data mining and predictive marketing to optimize their processes.

Digital transformation technologies have transformed the traditional approach to digital transformation into an integrated, modern approach.

Businesses will be forced to invest in predictive analysis due to the increasing internet proliferation, cloud technologies, as well as connected systems.
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2. Humans to Drive AI Development

Companies that employ AI to analyze data have a higher chance of success than those that don’t. Fear of losing your job was one of the greatest obstacles to AI adoption.

Many people viewed technology as a threat to their jobs and feared that they would be replaced by robots.

Although their fears were not unfounded, machines are faster, can eliminate errors, and are more efficient than humans, AI’s true potential can only be realized when humans and machines coexist.

This is why humans will play a crucial role in the evolution of AI to more accurate and sophisticated algorithms. Human involvement in Artificial Intelligence will shift from mundane repetitive work to a more strategic role.

While humans will continue to be required to determine the best way of scaling existing technologies, machines will do repetitive work.

3. Higher Use of Business Intelligence Software

In 2022 and beyond, businesses in all industries, including retail, manufacturing, and business services, will see an increase in the use of business intelligence tools. These tools change the way that organizations approach data analysis.

Big data is made more accessible by business intelligence tools, which reduce the amount of computation required and the expertise needed to interpret it.

Even if users don’t have a background in IT or data mining, they can still perform analytical functions like exploring data sets and performing data-mining tasks. Only one prerequisite is that you know how to use these tools.
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4. Growing Cloud-Based Solutions

Cloud-based technologies enable companies to shift away from large enterprises and smaller businesses as a result of the pandemic. They also help them save money on legacy tools and bottlenecks.

Cloud-based technologies are now mainstream. This trend will not slow down. Cloud-native analytics solutions will be preferred by many companies to increase their competitive advantage with simplified analytics and business intelligence.

5. Growing Budget

One thing is certain: Many businesses will increase their big data/BI budgets by up to 50%, as per all of the forecasts.

This is especially true in the technology, financial and retail sectors. According to Allied Market Research, big data analytics in the retail sector worldwide generated $4.85 Billion in 2020. This number is expected to rise to $25.56 Billion by 2028.
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6. Welcome Data Universe

The data universe is a term that describes the vast amount of data organizations today have to deal with. Data management architectures like data mesh, data lakehouse, or data fabric make up the data universe.

These architectures show how industries can quickly and easily convert growing data volumes into actionable insights and actions.

Businesses can access more data from multiple sources in a shorter time and foster collaboration between teams by having a central repository that stores both structured and unstructured data. These are the future of data infrastructure and will soon replace data warehouses.

Businesses must embrace change if they are to thrive in the future, stay competitive and grow in this changing technological environment.