Top Trends That Will Drive Data Analytics in 2022

Monitor with charts with annual data analysis on screen

Data is a vital part of many businesses in today’s world. It drives small and big business decisions that allow innovating and getting ahead of the curve. Since the current landscape is data-driven, data analytics is needed more than ever.

According to International Data Corporation (IDC) analysts, businesses spent an estimated $215 billion on big data and business analytics solutions in 2021, 10% over what was spent in 2020. Meanwhile, according to a 2020 PwC survey, most Philippine companies have already started or plans to start embedding data analytics in their daily operations, with three out of five having data analytics teams. The numbers don’t lie, indicating a booming data analytics market.

Besides the data’s importance, other factors contribute to the rise of data analytics. This blog covers the different trends that will drive data analytics in 2022.

Artificial intelligence (AI)

Gartner has projected that AI is among the top data analytics trends with significant disruptive potential over the next three to five years, with leading business intelligence and data analytics company Qlik at the forefront. This trend has long been in the conversation of corporate heads as the continuous growth of the industry landscape makes the need for more advanced technology clearer. Hence, data analytics leaders like Qlik should continue adjusting business models accordingly to keep their competitive edge over other companies. So as systems become more complex, dependent, and layered, it’s safe to say that more businesses will adopt AI technology.

Besides technological advancements, AI also offers wide applications that drive business value. A great example that showcases this is companies like Alibaba, the world’s largest e-commerce platform that uses AI to automatically generate product descriptions for its website.

Cost-effective data management

Every business that collects data deals with massive amounts of it regularly. They demand a large effort to maintain the data constantly collected. While enterprises may have applications that provide the tools necessary to operate, they can’t handle managing bulks of data regularly.

Thus, businesses of any size will look for affordable ways to manage their data, increasing the demand for data analytics. They may lean on cloud solutions to keep up with the challenging amount of data they must manage. Gartner forecasts public cloud spending to exceed 45% of all enterprise spending in connection with this.

Composable data analytics

Composable data analytics is a process by which organizations combine and consume analytics capabilities across their various data sources to promote effective and intelligent decision-making. Such tools can make analyzing data faster than traditional approaches. It also features reusable and swappable modules that businesses can deploy anywhere.

In addition, composable analytics can also reduce data center costs. Even if a business migrates to the cloud, spending on a data center will cost less with composable data analytics. Gartner analysts predict that 60% of organizations will build business applications composed of components from three or more analytics solutions.

Shift to small and wide data

With the emergence of AI, composable data analytics, and data fabric, a new approach to leveraging disparate data for analytics, organizations can examine a combination of small and large data. They can also apply techniques that look for useful insights within small and micro data tables. Having access to small and wide data sources will be a key capability that will be leveraged for years because it enables more context for analytics leading to intelligent decision-making.

These benefits give these trends more influence in data analytics. They also show that advanced business intelligence is the way to go for data analytics. So if your business needs to improve business intelligence, check out MTC’s analytics solutions which utilizes Qlik and Alteryx for data analytics and predictive analytics respectively to learn more about it.

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