What are the Top 3 Tech Trends for Data Analytics in 2021?
AI-based technologies in jointure with data will make top data analytics trends.
Companies are blind and deaf without data analytics. Today, data analytics help organizations understand their market better to stay ahead of competitors. Data analytics infrastructure will likely grow five times by 2024 because companies are rapidly adopting this technology. Other AI-based technologies like machine learning (ML), natural language processing (NLP), etc., in jointure with data analytics and cloud platforms, are also becoming popular across industries.
Today, stakeholders need to know what is going on in a business operationally, how the business is performing, and how events are satisfying customers more than ever. Access to quality data analytics can ensure how a firm stays strong during unprecedented times like COVID-19. After the uncertainty and seismic market shifts last year due to the coronavirus outbreak, how do data analytics leaders drive innovation in 2021? Following are the top data analytics trends for 2021 they must focus on.
Self-serve Data Analytics may evolve.
Self-serve data analysis stimulates business results and reduces delivery pressure from the IT department. But the problem is the design, construction, operation, and maintenance of a comprehensive self-serve data analytics ecosystem as it consumes dollars and takes much manual workforce. Software development productivity of data analytics software packages minimizes the cost, but it’s still a concern for many firms. Some companies have understood that turning business analysts into software developers may not deliver the expected outcome.
These things are stopping some companies from making the investment and operating the data analytics ecosystem like IT departments. Some are making little investment and are leaving the data analytics environment comparatively informal and ad hoc.
Investing in Data Transformation
With the explosion of data sources and data inside massive enterprises, data teams cannot move fast. A recent survey finds that enterprises spend roughly 45% of their time wrangling and preparing data. It takes almost a week to prepare data for a traditional analytics project. Consequently, 97% of respondents are searching for ways to accelerate the data transformation process to speed up analytics-ready insight.
Data transformation in the cloud reduces time to integrate siloed data, denormalize, enrich, and then apply business logic, which helps companies get insights faster. Transforming data with an ELT solution helps data teams do more and require fewer resources as external factors contribute to creating pressure on budgets and resources.
Analytics-ready data with cloud ELT makes businesses more scalable and flexible, cost-efficient, and ready to leverage data in advanced applications involving the internet of things (IoT) data, ML, and AI that can speculate what comes next and the future movements. Firms must aim to invest in cloud ELT solutions to offer faster insight into their business while keeping pace with the influx of data sources required for actionable analysis.
Relationships increase the foundation of Data and Analytics Value.
As per Gartner, graph technologies are likely to facilitate rapid contextualization for decision-making in 30% of organizations globally by 2023. Graph databases and other technologies focus on relationships between data points. Those relationships are crucial for most things organizations want to do with data analytics. But most relationships are lost when using typical storage approaches. Integrating relational tables takes a lot of resources and lowers performance. Graph technology saves these relationships and enlarges the context for AI and ML. They also enhance the explainability of these technologies.