Recent research suggests that the demand for data analytics will continue to grow over the next five years.
More and more organizations are recognizing the power of analytics. Over half of data leaders said they’d increased their budget for this technology while only 8% reported cuts.
Meanwhile, the demand for data analytics is steadily climbing. Experts believe it will reach almost $300 billion in value by 2030, over triple what it’s worth now.
A DBT report finds that the majority of data analysts have already incorporated AI into their daily workflow.
Two in three data analysts have incorporated AI into their everyday work. Many expect to integrate the technology further in the near future.
Over half say they think AI will significantly impact self-service analytics. This capability allows more non-technical roles to explore the system without assistance, reducing the burden on data teams.
Microsoft remains the most popular type of business intelligence tool on the market according to HGInsights.
Demand for business intelligence tools has exploded as companies look for more ways to collect, process, and report on data. Global spend is expected to reach $59.7 billion by the end of 2024.
Several solutions are dominating the market. Microsoft holds the top spot with over 200,000 client companies followed closely by Tableau and Open Source.
Gartner experts argue that synthetic data could help businesses overcome one of the top barriers to implementing AI.
39% of organizations struggle with a lack of data. Gartner believes synthetic data is the answer as it involves creating your own scenarios using AI rather than sourcing information.
Experts say synthetic data will soon become more popular than real, structured data.
While you can still use it to test scenarios and predict outcomes, there are fewer complications.
For example, companies don’t have to worry about anonymizing customer and employee data to keep them safe.
6: Edge computing
Sending data to a centralized server and waiting for it to process and return the results can lead to significant delays.
That’s why more and more businesses are turning to edge computing. Instead of transferring all the data they create, they process it on devices or services close to its source.
Gartner experts predict over 50% of companies will incorporate machine learning in their edge computing by 2026.
Machine learning can process the data locally and decide what needs to be transferred and what’s irrelevant to the company.
When considering the next big trend in data analytics, I feel it’s less about a new technology and more about timing. I believe that customers and consumers seek instant gratification.
Real-time data, such as labor tracking, enables us to make informed decisions more quickly, without waiting to compile reports. The faster we receive real-time data, the sooner we can make crucial decisions that either help or hinder the business.
Recent research shows the demand for Data Mesh is steadily growing, especially in the US market.
Data mesh is gaining traction as companies search for more efficient ways to manage analytics.
Instead of having a core team handle everything, they decentralize the function and make individual departments responsible for their own data.
Healthcare and life sciences are the fastest-growing sectors in data mesh. No doubt this is due to the large amounts of information required by most hospitals, healthcare centers, and laboratories.
Sqream research found that machine learning is the most popular kind of platform or tool used by analytics teams.
65% of data analytics teams use at least three machine learning tools. It helps them manage large, complex, and dynamic data sets more effectively.
The challenge is that machine learning demands a lot of time and resources. Respondents say insufficient resources are the number one reason why projects fail.
Many say they regularly get a shock from their high Cloud Analytics bill as their work exceeds the budget.
A SalesForce survey discovered that the overwhelming majority of analytics teams say sourcing reliable data is a top priority.
Analytics tools require a lot of information to run smoothly. Moreover, the majority of experts agree the quality of their work depends on the reliability of their data sources.
That means IT and analytics teams face increased pressure to find a continuous supply of trustworthy data.
The good news is most of them think they’re up to the task. Half say their data meets industry standards and over a third say they’re “best-in-class”.
10. Sustainable systems
Analytics tools require a lot of resources to process and store data.
For example, researchers at the University of California calculated that ChatGPT consumes 500ml of water for every 5 to 50 prompts it answers.
OpenAI draws this water from the nearby rivers to keep its supercomputers cool enough.
As businesses become aware of the high impact, experts say many will switch to sustainability-enabled monitoring services.
They predict the demand for these services should increase by 35% between 2024 and 2027.
Data Analytics Statistics
While data analytics is moving at a fast pace, its impact on business success can be unclear.
Often it’s a feature of another software like accounting or CRM so we can’t easily separate its value from the broader system.
With that in mind, let’s look at statistics that suggest how data analytics is performing in this section.
The culprit? As they introduce more technology into their system, they leave themselves more vulnerable to threats.
Data security isn’t a new issue. Since businesses moved to the Cloud, they’ve been struggling to stay one step ahead of the cybercriminals.
The number of attacks has been increasing year on year as hackers develop new ways to gain unauthorized access to systems.
14. The democratization of data
Business leaders can instantly access data, which should lead to faster decision-making.
However, it still takes them 20 days on average to implement strategies.
The challenge is that many companies aren’t sharing information with employees.
One in five senior leaders even says that they alone should have access to all the company data.
We’ve democratized data, but with a strategic filter. It’s important that everyone has access to data that’s relevant to their role, but it’s also crucial to avoid overwhelming teams with unnecessary metrics.
We ensure that data flows freely across departments but is tailored to each team’s needs. Developers focus on performance metrics, while leadership gets insights on impact and ROI.
The balance is key—data democratization without context is just noise, so we’ve tailored it to enhance clarity and drive smarter decisions at every level.
A recent DBT survey discovered it’s typical for small teams to be responsible for data analytics.
As you might expect, the number of workers responsible for data analytics depends on the company size.
Small to medium businesses usually have one to five people handling tasks.
The results indicate organizations only tend to build out their departments when they reach 500 employees.
DBT’s survey shows that data teams have a range of responsibilities too.
Most departments are tasked with organizing data sets, maintaining platforms, and generating reports. Some also build and manage machine learning models.
Wrap Up
Data analytics continues to move at a fast pace. The main barrier to implementation is the high costs and extensive resources required to maintain these functions.
As we find more efficient ways to handle analytics, we can experiment more with data analytics and realize its true potential.
The big question over the coming years won’t be what’s next but how are businesses harnessing the technology.
