A short summary: BI trends in the age of digitalization

#1 Automation

According to Gartner, by 2020 it will be possible to automate more than 40% of all data science related tasks and thus achieve better profits. This is in line with what is happening in practice with business intelligence providers who are trying to automate large parts of the analytical process. As more and more companies recognize the value of BI, the demand for professionally trained data scientists and data analysts is increasing. Another aspect of business intelligence that makes automation even more necessary is the lack of data analysis capabilities. The analysis of the value opportunities of a company is of permanent importance!

#2 Creating actionable insights

In contrast to the current role of artificial intelligence (AI) in companies, where AI merely collects and stores data, we will use the former to gain actionable insights. For small and medium-sized enterprises, advanced analytics will prevent the critical need for data scientists. Companies will not have to invest time, effort and money in the analysis of raw data with Augmented Analytics. AI will enable companies to respond to key insights. These capabilities will increasingly extend to large data sets. By combining machine learning (ML) and natural language processing (NLP), devices equipped with Augmented Analytics can independently understand and link predefined data. AI facilitates the independent detection of anomalies in the data system regardless of the type of data set, i.e. its size and facets. Companies need only provide the raw data.

#3 Data Governance

Data management is becoming increasingly important in relation to business intelligence tools due to the growing number of data sources and their complexity. In today's business culture, which is driven by data, in most cases, data sets the premises on which they base their business decisions. If the data is of poor quality or inaccurate, the resulting business decisions can have devastating consequences for the company. Through data governance, only a certain group of people has access to the data and can approach it accordingly. This helps to gradually make the data more reliable, which leads to better and more accurate business decisions. The past year has witnessed many publicly disclosed data breaches that have catapulted data governance to the center of data management discussions.

#4 Integrated capabilities

For companies, immediate decisions can often be just as decisive as long-term strategies. In the absence of this, companies often lack fundamental efficiency. As a result, business applications integrate targeted analytical capabilities and content. These embedded analytical capabilities will be a dominant business intelligence pattern in 2020/2021. It will help companies work smarter by integrating data analysis into the existing business framework in the form of ERPs, financial planning, CRMs and marketing automation.

#5 Increasing AI adoption

AI has already become an integral part of a modern business organization. Companies use AI to increase their productivity and improve the corresponding decision making. BI tools break down historical data and its performance, which is then used by data analysts to create reports and then used by managers to make decisions. With the advances in AI development, business intelligence is moving to a more proactive approach to analysis.

#6 Digital professionals will find new opportunities

Another interesting development that is emerging is that employees will be able to access visualizations of work processes and data as well as benchmarking reports via digital assistants. This is in contrast to the fact that they will create the same thing as they do now. In addition, language assistants like Alexa and Siri will use NLP and AI to translate languages and convert them into structured data ready for analysis to gain more insight.

#7 Improved operational efficiency

BI tools bring new data to the attention of the end customer in a clear, simple and easily understandable way. This makes follow-ups just as uncomplicated and easy. In addition, the data is used to define new processes that can make the company more competitive and ensure the success of projects. Business Intelligence is used to compile data and provide a comprehensive overview of the company through reports, charts and breakdowns from data originating from various information sources. By 2020/2021, companies can expect to be able to increase productivity, boost sales and improve logistics through broad-based analysis of the data. This analysis will involve complex perception and intuitive reporting of the data.

#8 Diversification of jobs related to data

As the volume and use of data increases, data-related professional profiles will expand to cover different industrial sectors. This will lead to new data-related jobs such as data ambassadors, data project managers and even data translators. Together, they will have to meet staffing needs and specialise in large data-related areas. New career opportunities will open up.

#9 Predictive analytics

Predictive analytics is a field that is undergoing a profound democratization process. Two different paths are emerging. One is AI, ML and NLP, which is becoming more and more ubiquitous. The commercialization of such AI-supported solutions is growing exponentially and benefits companies of all sizes. Goal-oriented companies no longer need to invest in expensive BI technology and the establishment of data authorities.

#10 Overall efficiency

It's all about creating the conditions for companies to recognize the benefits that BI offers employees to make better decisions, satisfy employees and finances, and provide in-depth analysis. Business intelligence tools will quantify marketing more and reveal its secret while eliminating false assumptions. In 2020/2021, we will make business decisions based on confirmed facts that give companies the vision to work with customers and give them the best way to reach them, with the goal of a better customer experience and increased consumer loyalty and retention.

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