Top 5 Trends in Data Analytics in 2022
Introduction
Data drives business decisions small and large, companies that are aware of industry trends can better innovate and be ahead of the curve.
In this article, we’ll cover what Data Analytic Trends in 5 Key Areas we’ve seen gaining traction and we’d expect continued growth and industry adoption you may benefit from.
Affordable Data Management
Companies today from a small business to a large enterprise produce massive amounts of data. To run a business, companies need productivity, marketing, and sales tools such as a CRM platform, website with backend management, payment gateway, and customer contact databases. These applications provide the tools necessary to operate but also demand a large effort to maintain the constant data collected.
Similar to large enterprises, small to midsize companies need a cost effective way to process and analyze this data to make better informed decisions and provide competitive solutions. But the problem is that the smaller companies do not have the same budget or negotiation leverage of a large organization.
“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – Angela Ahrendts, Senior VP at Apple Inc.
Managing large amounts of data is a challenge, companies may look into cloud solutions to keep up with the increasing complexity and secure their data. Cloud solutions provide the advantage of high availability and scalability for the team. The need for affordable data analytics and management in the marketplace will increase in demand. SaaS (software as a service) offerings that help democratize the industry landscape and enable smaller organizations for success will emerge.
Cloud Solutions
Cloud native solutions will be ubiquitous and inevitably replace legacy data analytics methods. The scalability of a cloud and multi cloud environment offers companies more flexibility. As more companies adopt, they will see increases in their productivity and greater returns to the bottom line.
Gartner forecasts public cloud services to grow 21.7% to reach $482 billion in 2022. Additionally, by 2026, Gartner predicts public cloud spending will exceed 45% of all enterprise IT spending.
As more companies adopt cloud solutions, interoperability between solutions is a key factor. Interoperability facilitates different systems to communicate, exchange data, and share workflow in a coordinated manner. Cloud solutions can offer productivity and efficiency benefits, the challenge can be the usability, connectivity, and compatibility of these systems working together across vendors successfully, to get the desired strategic results.
Some companies provide end-to-end solutions to offer customers a holistic approach, while others offer extended capabilities to augment existing solutions. Enabling open API architecture for the solutions that can easily integrate with existing or new solutions is the key and we’ll see more customer adoption and traction in this segment.
Industries will begin to shift investments from maintaining legacy products and move to cloud solutions that can adapt to fit their business goals and needs. Understanding that an investment now will help company growth for years to come.
Artificial Intelligence
According to Gartner, artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years.
“… data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do.” – Rita Sallam, Research Vice President at Gartner
The conversation of AI (artificial intelligence) and ML (machine learning) has been in many corporate boardrooms as the industry landscape has grown and the need for more advanced technology is becoming clear. This trend will surely continue as systems become more layered, dependent, and complex. We can safely say that AI technology will become more widely adopted into business systems in the coming years.
Organizations that choose to understand and implement will benefit from highly adaptive systems that can work with both small and big data sets allowing for scalable solutions and flexibility within distributed applications and components. This level of automation will mean less manual work for teams and faster value to business and ROI.
Gartner predicts that by 2022, 75 percent of new end-user solutions leveraging AI and ML techniques will be built with commercial solutions rather than open source platforms.
As this continues, organizations can begin to leverage AI and ML delivered at an enterprise grade level solution rather than the open source ecosystem.
Predictive Analytics
Predictive analytics is one of the advantages of AI and ML and is the practice of using a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.
Companies can leverage advanced technologies such as the before mentioned AI and machine learning to make roadmap adjustments to align with market forecasts and predictions.
Below are a few examples of predictive analytics in different industries,
Retail
For a retailer, understanding customer behavior that gives insights to how a company should manage inventory, add store locations, or predict shopping trends is extremely valuable. Offering a relevant shopping recommendation that leads to a sale and reduces purchase returns is an edge every store would want. Through predictive data, a store can improve its customer relation and experience and increase their market position in the industry.
Anomaly Detection in Security
Predictive data analytics enabled with AI and machine learning can monitor network activity and detect anomalies that could mean a data breach or exfiltration of sensitive company assets. Security analysts will be notified of any suspicious activity and stop potential breaches before compromise.
Cybersecurity concerns are top of mind for organizations (as they should be). Being able to identify malicious activity with prioritized alerts and reducing false positives for security analysts is key to protecting your company and being resilient against cyber threats. This also has the added essential benefit of increasing your company security posture for both customers and employees alike.
Marketing
Marketing teams can adjust future campaigns depending on the data from past behavior and patterns. Teams gain insights to make data-driven decisions to improve campaign effectiveness and get the most return on investment. As consumers now have more choices than ever before and competition is high, organizations need to stay one step ahead of trends and make adjustments if they plan to succeed.
Automation
Automation describes a wide range of technologies that reduce human intervention in processes to perform analytical tasks. It takes out the manual and tedious workflow for a human and performs complex analysis and processes more efficiently and quickly.
Harvard Business Review says, “Automating Data Analysis Is a Must for Midsize Businesses”.
Every company will become a tech company, says former Cisco CEO John Chambers, and every company has data, usually lots of data. Being able to automate the analysis of colossal amounts of unstructured data is valuable for every business to increase efficiency, productivity, and performance.
We can expect more organizations to see the need here and make updates to company systems to automate tasks and reap the cost and time saving advantages.
Data Visualizations and Dashboards
By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. The presentation of the data enables analysts to analyze a vast amount of data. For a long time, static Dashboards served as the prime solution to visualize the data.
For more complex use-cases, analysts are expected to build a query and formulate a custom dashboard. As the businesses look to consume data to make critical business decisions on a regular basis, there is a need for a better solution.
A few BI companies have started to address this pain point with more dynamic BI Tools that can present insights automated and customized to the user needs with the help of Natural Language processing and delivered to their point of consumption.
Next steps
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Author
TUAN NGUYEN
Tuan is a Product Marketing Manager with 8+ years of industry experience in large Enterprise technology companies and start-up. He is passionate about technology marketing and has experience in Cybersecurity, Cloud Security, and Data Center Networking.