11 Business Intelligence Trends in 2019

The rules of Business Intelligence are evolving, and this has made a huge impact in some sectors, not only because of the speed of technological advances, but also because of the new techniques of extracting value from data.
2018 was a year of innovation as well as product and service enhancements, leading organizations to an analysis of how to prioritize a modern BI approach that drives the company to get the most value from their data. Thinking of the people ahead, Adriano Chemin, vice president of Tableau, a data visualization company, brought together the main trends of Business Intelligence.

Let’s look at the topmost trends of Business Intelligence for 2019 and beyond

Explainable Artificial Intelligence

That Artificial intelligence (AI) is here to stay is a fact. Thanks to machine learning companies are able to create clusters of behavior, identify market trends, assess risks, make quick decisions and automate millions of activities that previously consumed time and resources. There is no denying that AI has opened up a world of possibilities for the BI universe.
On the other hand, the more we depend on AI, the greater is our distrust of the credibility of model-based recommendations, since most tools that use machine learning do not provide a transparent way of looking at the algorithms or logic behind decisions and recommendations. This is where AI Explainable comes in, the practice of understanding and presenting transparent displays of machine learning models. If it is possible to question human beings, why not have the same option with machine learning in making decisions?
Explainable AI enables executives, scientists, and data analysts to understand and question how machine learning is applied to the day-to-day running of a business, creating more transparency and trust in results.

Natural language humanizes the data

With the advancement of Natural Language Processing (NLP) systems, everyone can have natural interactions with the data. Natural language represents a paradigm shift in the way people ask questions about the data. Being able to interact with visualizations in a way that interacts with people, makes users aware of areas of the analysis pipeline previously dedicated only to data scientists and experienced analysts.
Users are not limited by their own analytical skills; what defines this limitation is the complexity of the questions. With this, advanced users can also answer more detailed questions in less time and present more interesting panel features to others.

Actionable analysis: data mobility drives actions

Speed is the keyword in the lives of those who work with data analytics today, whether in access to information or in response time to perform the necessary action, everything needs to be aligned in a single workflow and available in place so that the Scientist/Data Analyst can act fast.
BI platforms evolve so that the data helps people to take concrete actions. The convergence between analysis and action will reduce the time and effort between information and decision-making. In addition, it will make data more widely available in workflows, stimulating more and more people to incorporate data into everyday decisions.

Collaborative use of data has a significant positive impact on society

The community brings together Data Scientists and Developers whose goal is to use their expertise to solve large-scale social problems. Targeted efforts by public and private sector organizations strengthen the “Data for Good” movement. Although the challenges are the same in large-scale collaborative projects, the “Data for Good ” movement demonstrates the altruistic potential of data sharing. Together, advances in technology, the development of data empowerment, and the focus on collaboration create a fertile environment for solving some of the world’s most difficult problems.
In addition to their impact on private companies, data is transforming non-profit organizations and NGOs. To support this, Gartner indicates that the Data for Good movement on social media platforms grew by 68% last year, and the public is becoming more aware of the positive impact of data on social media.

Codes of ethics catch up to data

The debate on data privacy has become increasingly prominent and consumers are more aware than ever of sharing personal data. This affects the way business deals with monetization, data collection and sharing. New regulations such as GDPR require reflections on data ethics and privacy to be part of day-to-day business practices. This includes:
Codes of ethics: Areas such as law, medicine and accounting have long been dealing with professional codes of ethics. As data is proliferating in all areas of business, companies are beginning to evaluate how to apply these same principles to data analysis practices. As measured by Gartner, “The digital age of business has overshadowed the boundaries between technology and business.” Data is now a key piece of the strategic puzzle.

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