Business Intelligence Trends for 2019Nov 20, 2018 | 281 views
The end of the year is coming, and it's time to think about the 2019 budget. The market is changing, and business intelligence trends are constantly updating. In addition, rapid advances in artificial intelligence offer more effective solutions and have converged with BI tools. While technologies are not applicable to all companies, it is important to be aware of where your market is heading. Undeniably, different industries can take advantage of these tools, reducing costs and increasing their productivity.
Among the trends of business intelligence for 2019, factors related to data-driven culture stand out. This is the case with perspectives such as data quality management, data discovery and data visualization. Therefore, companies that give priority to BI invest in this culture, stimulating the practice in all areas and hierarchical levels. Thus, if all the members of the business are aware of the data, the potential that the feedback can provide is potentiated.
The importance of having high quality data, always at hand, appears as a concern of professionals in the industry. On the other hand, this need brings relevance to data governance, essential for the effectiveness of BI. The services sector is alert to these characteristics, which still need to be valued more by the industry.
Where are we
To find out where we left off, let's look at some BI data published by Forbes from the 2018 Wisdom of Crowds Business Intelligence Market Study:
- Executive Management, Operations and Sales are the three main roles driving BI adoption.
- Dashboards, reports, end-user self-service, advanced visualization and data warehousing were the top five technologies and strategic initiatives in the area.
- Small organizations with up to 100 employees have the highest penetration rate or adoption of BI.
To support the decisions for the coming year, we list below some of the key trends of business intelligence presented by the market.
Business Intelligence Trends: Quality Above All
The right decisions can only be made on the basis of correct data. Thus, in a data-driven business, operational actions rely on reliable information. This factor is essential for BI efficiency and therefore for operational excellence.
Data integration systems exist to bring together elements from various sources, such as ERP, CRM and SCM. BI mechanisms can help reveal data quality issues, and it is important to pay attention to this. The question becomes more acute when it comes to collecting data from third parties or online services.
Some solutions, such as the data quality cycle, aid in the management of master data and are being incorporated into BI software. To ensure the high quality of data, it is necessary to determine responsibilities, quality assurance processes and to continuously monitor the quality of company data. These aspects stand out in data governance, which also targets company strategy and security.
Trends in business intelligence: convergence between artificial intelligence and BI
Technologically, there is more and more integration between BI tools and artificial intelligence. Gartner has elected 2018 as the year of AI's democratization. In a survey of CIOs in 98 countries, 46% of respondents were already testing such initiatives or have short-, medium- or long-term technology plans.
Tableau points out as trends in BI for 2019 two aspects related to artificial intelligence. The first relates to explainable AI: the need for transparency is motivating the practice of understanding and presenting transparent displays of machine learning models. The second deals with natural language processing (NLP), which has been incorporated as support for analytic conversation. This is true both for capturing data and for providing information to BI users. According to Vidya Setlur, development manager of Tableau's natural language team:
Business Intelligence Trends: Data Discovery and Data Visualization
When using BI tools, we generally search the Big Data ocean for answers to our business questions. With data discovery, the process is the reverse. Data discovery is the method that allows you to answer the question: what does all this mean? Through it, the business user can discover discrepant patterns and values in data.
Data discovery features aid in data cleansing, enrichment, and modeling, creating assemblies for analysis. As a result, it is possible to perceive unexpected patterns, which may be answer to questions not yet formulated. Machine learning is increasingly being used to guide analysts in the preparation and analysis of information.
In addition, the data visualization works to create visual representations that can immediately reveal patterns or relationships. Tools and techniques make it harder to perceive aspects of the underlying data. This is especially useful when information needs to be evaluated quickly for decision making.
Both methods, data discovery and data visualization, are presented as business intelligence trends for the coming year. Therefore, companies that already use such tools must invest in its improvement, especially with regard to the user interface. Other sectors, which are still more inexperienced in BI, need to pay attention to these resources when deploying their programs.