Top 10 Trends In Business Intelligence BI For 2019

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Trends in Business Intelligence are fast approaching, and we are going to review the emerging Trends in Business Intelligence and technologies that will shape the BI sector and open new horizons for businesses. The year 2018 was marked by innovation and improvements in products and services, encouraging companies to prioritize a modern approach to find Trends in Business Intelligence BI and reorganize their business to realize the full potential of their data.

Top 10 trends in Business Intelligence BI for 2019

To better understand the changes ahead, Tableau has interviewed its specialists, who stay abreast of industry developments and collaborate with thousands of users around the world, as well as external experts in the implementation of platforms. Here are our main Trends in Business Intelligence and predictions for 2019.

1) The Advent of Transparent AI

The promise of AI is to strengthen our understanding of data through automation of decision-making. As we rely more and more on AI and machine learning, we wonder about the reliability of the recommendations generated by automatic models.

Indeed, many machine learning applications do not offer a transparent way of understanding the algorithms or the logic on which such decisions or recommendations are based.

As Adrian Weller explains, a researcher at Cambridge University, transparency is essential to facilitate the deployment of smart systems on a large scale. This need favors the development of an AI capable of presenting machine learning models in a transparent way. If it is possible to question a human decision, we must also have the possibility with machine learning decisions.

Business unit managers expect data science teams to use more transparent templates and provide documentation or history detailing their creation using Trends in Business Intelligence. The impact of AI depends on the trust we place in it, and the results it produces must be simple and intelligible and respond dynamically to the questions we ask to understand our data.

2) Natural Language Humanizes Your Data

Natural language processing allows computers to interpret questions in human language. Trends in Business Intelligence BI solution providers integrate natural language into their platforms to provide more natural interfaces and visualizations. In parallel, natural language evolves and plays an increasingly important role in analytic conversations, that is, a human conversant with the system about its data.

The system taps into the context of the conversation to understand the intent of the user and advance the dialogue. Thus, the conversational experience seems more natural. For example, when someone has an additional question about their data, they do not have to rephrase their question to expand on it or clarify an ambiguity.

Natural language represents a radical change in the way you can query your data.The ability to interact with a visualization in the same way as with a human interlocutor allows more users to deepen their analysis, regardless of their skills.

By gaining ground the Trends in Business Intelligence sector, it will facilitate the adoption of analytics and help companies implement a data-driven self-service approach.

3) Explicit Analyzes Contextualize The Data

Users need to access their data and make decisions as a result of their discoveries without changing workflow. As a result, Trends in Business Intelligence platform vendors are expanding their offering with mobile and integrated analytics, dashboard extensions, and APIs. Built-in analytics puts data and information at the heart of workflows.

So users do not need to use different applications or servers, and dashboard extensions provide access to external systems directly from the desktop. ‘a dashboard. Mobile analytics, on the other hand, allow users to access their data even in the field.

4) Collaborating Around data, Significant Social Impact On Trends in Business Intelligence

In addition to their impact on private companies, data is transforming NGOs and non-profit organizations. The Data for good movement will gain momentum as organizations become aware of the benefits of using data in their social initiatives.

To support this, Gartner points out 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 networking. society.

Before, public sector organizations lacked the resources to invest in sophisticated data infrastructures or data teams. Today, thanks to the flexibility and cost-effectiveness of cloud computing, NGOs and non-profit organizations can develop sophisticated data environments without investing heavily in on-premise solutions, and thus better carry out their mission by relying on the data.

This approach has also enabled the creation of platforms dedicated to sharing and collaboration between associations, to enable them to better fulfill their mission. It also opens the dialogue on aspects contributing to building trust on these platforms, including the responsible use of data.

5) Codes of Ethics Update on Data Issues

With the advent of regulations such as the RGPD, management teams are questioning the evolution of corporate ethical practices in terms of data use. Confidentiality is a topic that will continue to animate conversations, especially as users become aware of the issues of sharing their personal data. When it comes to using data on a daily basis in a business, there are two ways to approach ethics and confidentiality:

  1. Codes of ethics:many professions are already governed by a code of ethics (lawyers, doctors or accountants, for example), but the proliferation of data drives companies to think about how to apply these same principles to their practices analytical.

Chief Data Officers (CDOs) participate in the establishment of such codes of ethics to create an environment that facilitates decision-making on governance and recruitment infrastructure. For example, Gartner’s 2017 survey of the role of CDOs reveals that “the number of CDOs for whom ethics is part of their responsibilities has increased by 10 points between 2016 and 2017”.

  1. Business Process Changes:An in-depth review of the data lifecycle allows you to periodically evaluate the data management strategy and verify that it complies with internal regulations and codes of ethics. As Accenture points out in its report on Universal Principles of Data Ethics, “Governance processes need to be robust, known to all and regularly reviewed” to fit the growth and evolution of the business.

With modern Trends in Business Intelligence BI platforms democratizing data analysis, more and more different service employees will have the responsibility to comply with ethical principles. For this reason, good knowledge of the data will require a good knowledge of the applicable ethical principles.

6) Data Management Converges With Modern Trends in Business Intelligence

As data sources become more diverse and complex, and more and more people rely on data to make decisions, data management is more essential than ever. Businesses are turning to data curation, a practice that includes capturing, cleaning, defining and harmonizing disparate data, to better bridge the gap between data and its actual use.

Curation tools and processes (such as data catalogs and semantic governance) now converge with BI platforms to establish a link between data and the business context and to ensure appropriate governance. Analysts and content users can check and analyze the source of their data, and Data engineers and data managers can better control the impact of changes to datasets.

In the end, the curation of the data will provide a solid foundation for the entire analytical pipeline and help users go beyond the scope of their data to ask more specific questions about their activities.

7) Data Storytelling, New Trends in Business Intelligence Language

To make a real impact with you analyzes, you need to communicate your findings effectively. This is where data visualization comes into play. Storytelling will become an essential skill for any analyst wishing to communicate effectively and clearly the path followed in his analysis to reach his discoveries.

The definition of storytelling is evolving as companies develop their analytic culture. Instead of presenting a conclusion already established, storytelling methods are now focused on opening a conversation around data.

This participatory approach to analytics shares the responsibilities between the creator of a dashboard and its audience, to come to a conclusion together based on the data presented and collect different perspectives through Trends in Business Intelligence before making a business decision.

Integrating storytelling into different business roles will amplify the impact of data and facilitate the discussion, communication, and application of new ideas across the enterprise.

8) Companies Interested In Adopting Analytics

It’s not enough to allow everyone to access Trends in Business Intelligence (BI) to find solutions to foster adoption, just as you will not be sure to generate value with your platform simply because your users have access. And assuming that an occasional report will automatically favor decision making can actually hinder the progress of your analytical efforts.

To have a real impact on your business, your management teams need to measure how your BI platform is being used. Some companies have set up internal user communities to boost engagement.

This is particularly the case for JPMorgan Chase, whose center of excellence has allowed thousands of analysts to be incorporated to boost the foundation of users of its BI platform.

These users then gain in expertise and can help to share best practices and ensure that everyone has the same definition of data. With all this, you can maximize the impact and ROI of your BI solution, boost the efficiency of your employees and gain competitiveness.

9) Data Democracy Raises Role Of Data Scientist

According to a study conducted by LinkedIn in 2017 in the United States, many new roles are appearing on the market, such as machine learning engineer, data scientist or Big Data engineer.

More and more different types of services and types of employees are using the data, which has increased employee skill levels in this area, changed the definition of data science and reduced the gap between classical expertise and knowledge of a business domain.

Today, data scientists must have knowledge of machine learning as well as advanced statistics, while retaining a strategic approach to the Trends in Business Intelligence and an in-depth knowledge of the sector.

Instead of just presenting results, data scientists are now participating in application of these results to business issues. They must also have the skills to communicate their findings to their management and collaborate with other users to help them use the data.

This collaboration is carried out in particular with data scientists whose main role is to generate models outside the field of statistics, to develop and test hypotheses. Self-service analytics tools help these different roles better understand the data to generate actionable information that has a strong impact on business operations.

They must also have the skills to communicate their findings to their management and collaborate with other users to help them use the data through Trends in Business Intelligence.

This collaboration is carried out in particular with data scientists whose main role is to generate models outside the field of statistics, to develop and test hypotheses with Trends in Business Intelligence.

Self-service analytics tools help these different roles better understand the data to generate actionable information that has a strong impact on business operations. They must also have the skills to communicate their findings to their management and collaborate with other users to help them use the data.

This collaboration is carried out in particular with data scientists whose main role is to generate models outside the field of statistics, to develop and test hypotheses. Self-service analytics tools help these different roles better understand the data to generate actionable information that has a strong impact on business operations.

10) Accelerating Data Migration in Cloud Favors Adoption of Modern Trends in Business Intelligence

As part of modernizing your data strategy, you need to think about their storage location. For many companies, it’s a question of considering their migration to the cloud, to enjoy increased flexibility and scalability, as well as a lower total cost of ownership. The cloud also makes it easy to capture and integrate different types of data.

Josh Parenteau, Tableau’s Director of Business Intelligence, explains, “Moving to the cloud boosts agility and opens up new possibilities for applying Trends in Business Intelligence and analytics, which contributes to modernization efforts. The concept of data severity suggests that services and applications are attracted to where the data resides.

It is therefore natural that analytics will move towards the cloud, where more and more data are stored. Management teams are therefore turning to a modern approach to find the Trends in Business Intelligence, opting for a platform that can manage analytics entirely in the cloud.

Not all companies are ready to take the plunge, but many are testing hybrid solutions to take advantage of both the diversity of data sources and the benefits of the cloud.

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