Business Intelligence And Analytics Trends For 2019

The business intelligence (BI) landscape is evolving and the future of BI will be determined by these five emerging trends in 2019.

Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics, but what is the best BI solution for their specific business and industry. Organizations no longer wonder if data visualizations improve analyses, but what is the best way to tell each data-story.

2019 Business Intelligence and Analytic Trends

1. Data Quality Management.

DQM consists of acquiring the data, implementing advanced data processes, distributing the data effectively and managing oversight data.

DQM is the key factor to efficient data analysis, as it is the basis where all the analytics start from. According to Gartner, poor data quality is estimated to cost organizations an average of $15 million per year in losses. The consequences of bad data quality are numerous; from the accuracy of understanding your customers to ddeploythe right business decisions.

2. Data Discovery.

This is understanding the relationship between data in the form of data preparation, visual analysis and guided advanced analytics. With data visualization tools you can produce relevant insights and create a sustainable decision-making process.

3. Artificial Intelligence.

AI is the science aiming to make machines execute what is usually done by complex human intelligence. The demand for real-time data analysis tools is increasing and businesses today want to go further. With the emergence of predictive analytics, which is powered by AI, this will be a trend to watch out for during 2019.

4. Predictive Analytics Tools.

Business analytics of tomorrow are focused on the future and try to answer the questions: What will happen? How can we make it happen?

Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. It’s an extension of data mining which refers only to past data. Predictive analytics indicates what might happen in the future with an acceptable level of reliability. In business, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products, and partners, and to identify potential risks and opportunities for a company.

5. Prescriptive Analytics.

This goes a step further into the future. It examines data or content to determine what decisions should be made and which steps taken to achieve an intended goal. It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics and machine learning. Prescriptive analytics tries to see what the effect of future decisions will be in order to adjust the decisions before they are actually made.

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