Predictive Analytics in 2022: What Can We Expect?

Founded on the principles of probabilities, statistical and technical methods are used to develop custom algorithms in predictive analytics to unveil hidden value by manipulating data.

As a result, despite a brief deceleration during the Covid-19 pandemic, the predictive analytics market is expected to grow at a CAGR of 20% from USD 10.01 million in 2020 to USD 29.97 million by 2026. One of the main key drivers for the growth of the predictive analytics services market is that it helps businesses identify potential future trends and be prepared to face them with strategies derived from facts. It has become popular across industry segments for Business Intelligence (BI) that can help to increase productivity and sales using automated solutions.

Top 8 Trends in Predictive Analytics

In a self-feeding cycle, the increasing demand for greater accuracy in predictive capabilities is spurring advancements in data and analytics technologies. The advancements in turn are increasing adoption. As a result, the data and analytics field is seeing rapid changes that can leave businesses a little behind their more agile competitors. Keeping abreast of the developments and investing in future-proof technologies is crucial to leverage the latest developments while protecting existing investments.

Indium Software, a cutting-edge data and software solutions provider, brings to you the top 8 trends in predictive analytics that will impact the future of your business.

Trend #1 Scalable Artificial Intelligence: Historical data is being replaced by smarter and scalable Artificial Intelligence and Machine Learning techniques that are used to create highly adaptive algorithms using small data sets. They meet the organization’s need for privacy, speed, and quicker return on investment, facilitating automation and minimizing human intervention.

Trend #2 Personalized Solutions: This has become important due to the different analytics needs of different industries, different businesses, and even different functions. This has led to greater demand for efficient data preparation and self-service.

Trend #3 Flexible and Agile Data & Analytics: Agile data and composable analytics empower businesses with multiple data analytics, AI, and ML solutions that provide a flexible, user-friendly, and smooth experience facilitating growth through digital innovation and differentiation. In addition to getting actionable insights, it also allows different teams to collaborate, improves productivity and agility enhancing the analytics capabilities of the organization.

Trend #4 Hybrid Cloud: The demand for hybrid cloud services and cloud computation has been on the rise due to being cost-effective while ensuring security as well. This is also enabling the creation of a centralized database that is secure, compliant, and scalable.

Trend #5 Data Fabric: A data fabric architectural framework and data services enable standardized data management practices across hybrid multi-cloud environments for a consistent experience. In the complex world of data, the data fabric facilitates the reuse and combining of different integration styles, technologies, and data hub skills. The time for designing, deploying, and maintaining data systems is also reduced by 30%, 30%, and 70%, respectively.

Trend #6 Edge Computing to Speed up Analysis: Analytics today involves large volumes of data, requiring a high computing power with greater security and privacy. Edge computing leverages the laws of quantum mechanics to process enormous volumes of data using less bandwidth at high speeds.

Trend #7 Customized Views: Dynamic, modern, and automated BI tools will provide customized insights based on user needs and deliver it to their point of consumption. Static dashboards are on their way out due to their low utility as the need for self-service by business users and lower maintenance costs increases.

Trend #8 Engineered Decision Intelligence: Decision intelligence is required for informed decision-making and encompasses AI, conventional analytics, and complex adaptive system applications. Along with composability and common data fabric, it augments decision-making with data-based insights.

Indium for Predictive Analytics Solutions for Your Business

Indium Software provides data engineering and analytics services for enabling businesses to make data-driven decisions through the smooth and rapid movement of data from source to target with minimum distortion. Indium helps businesses invest in the right technologies best suited for their needs to improve their ability to predict and forecast trends and improve innovation for revenue generation, driving competitive advantage, and customer satisfaction.

Our team of experts with experience across domains assesses the current needs, the growth roadmap, and existing technologies to design a future-proof data and analytics solution that can help meet your unique growth needs.

Our range of services include:

● Data Engineering Solutions to design and develop data pipelines, data products with APIs, and facilitate data quality management

● BI & Data Mobilization Solutions to provide high-quality, enriched data for informed decision-making

● Data Analytics Solutions to uncover opportunities for growth and innovation

● AI/ML Solutions to automate the generation of actionable insights

Using predictive models, we can help you build and develop products and services using cutting-edge methodologies and AI techniques.

Learn more: 5 Ways how Predictive Analytics can help you