What are the Business Intelligence Trends to Watch Out in 2019?
Technologies such as new data quality management practices, predictive and prescriptive analytics tools, and data discovery techniques are regarded to be among the top trends, which are impacting business intelligence and analytics.
Fremont, CA: Businesses are in the digitalization era, and the amount of data available compared to the speed of the production of new data has been rising rapidly for years, and the business models, together with process improvements, highly rely on data and analytics. Most businesses are already reaching their limits by thinking that more data means a better approach and are unable to leverage the benefits they expect due to a lack of data quality or analytical skills.
Here are the most powerful analytics and business intelligence trends of 2019.
Data Quality Management
Data quality management is emerging to be a crucial practice that companies are adopting for the sake of their initial investments. The implementation of company-wide data quality processes helps the companies leverage business intelligence as well as acquire a competitive advantage, which lets them increase the returns on BI investments.
There has been a boost in data discovery since 2018, and the empowerment of business users has emerged to be a strong and consistent trend. It is imperative to remember that the data discovery tools are depended on a process. The generated findings will then bring business value. That demands an understanding of the relationship shared between data in the form of data preparation, guided advanced analytics, and visual analytics.
Machine learning and AI is changing the way in which we interact with our analytics’ and data management. From static, we are evolving into passive reports of things that have already taken place to proactive analytics with live dashboards, which helps businesses see what is going around every second and send alerts when there is suspicion.
Predictive and prescriptive Analytics Tools
The predictive analytics tools include the thought of future data and hence, usually, have a possibility of errors from its definition. Predictive analytics points out what the future may hold along with reliability, inclusive of a few alternative scenarios and risk assessments. Prescriptive analytics, on the other hand, is an advance step in the future. It analyzes data or content and plans out a perfect decision to be made to achieve a fundamental goal. It tries to determine the effects of future decisions to adjust the choices before even making it. As a result, it improves decision making because the future outcomes are considered before making the predictions.