Business intelligence (BI) has undergone significant transformation in recent years and certainly is not being superseded by artificial intelligence. In fact, it has become more efficient and user-friendly since moving to the cloud, utilising AI and machine learning, and its embedded implementation.
Today's business leaders know that data is a valuable asset that needs to be used effectively to ensure efficient company processes. They require powerful data analysis tools to help them in the decision-making process. Businesses are embracing sophisticated analytics and data science to gain insights and make more informed decisions. The result – turning your BI department into a profit centre.
BI is a valuable tool for both small and large businesses, it is evolving at a rapid pace and will determine how companies will work with data in the future. It therefore needs to be mobile, flexible and user-friendly. Here are six key trends that will shape the future of BI:
The future of business is in the cloud, and this is also the way big data is now moving. Nearly all BI elements, including data sources and models, analytics, data storage and computing power, have already moved to the cloud. Businesses need a connected cloud strategy to ensure reduced risk and flexibility.
Cloud services are by far the biggest trend in BI, especially with the wide adoption of remote working. Cloud-based BI makes it possible for apps and data to be accessed anywhere and anytime. Software as a service (SaaS) apps are becoming more popular and can be accessed via any web browser, allowing data, insights and answers to be accessed anytime, anywhere and on any device.
Extracting new value and insights from business data is critical, especially to become a fully data-driven organisation, delivering actionable intelligence to the entire organisation.
However, Data Quality is still one of the biggest challenges for data analysts. Good Data Quality is critical when trying to obtain the accurate insights from available data to make the right business decisions. Businesses are realising the huge cost implications of basing decisions on poor data quality and, as a result, they are implementing a data quality management (DQM) policy to ensure efficient data analysis.
According to Gartner, poor data quality costs organisations an average $12.9 million every year. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision-making, as stated in the Gartner 2021 report on Data and Analytics Governance.
Gartner predicts that by 2022, 70% of organisations will rigorously track data quality levels via metrics, improving it by 60% to significantly reduce operational risks and costs.
Automation and AI have the power to get any company ahead of the competition. This can only be achieved through enhancing productivity and precision across business processes, and to focus on higher value work.
Gartner expects that by 2023, organisations will be able to run 25% more tasks autonomously. While much of this will be achieved through the use of robotic process automation (RPA) in front-end offices, critical operations, infrastructure and data processes will need to be automated with more robust orchestration and automation tools that provide programmatic integrations and deeper functionality.
Data collection, cleansing and data repair can be time-consuming, but automation can significantly reduce the time and effort analysts spend on preparing data. The leaders in modern analytics solutions offer visual analytics with powerful AI, data management and collaboration.
Whether it’s for making sales projections or assessing customer value, predictive analytics and reporting are some of the most prominent advances in BI. Based on existing data patterns, businesses can now predict future business trends.
