How to optimize your business with augmented Business Intelligence?
Business Intelligence (BI) is an approach to decision support based on data, at the service of business. It is also known as business intelligence.
This approach has existed for about fifty years. Its development coincides with the democratization of IT tools in organizations.
BI is based on technologies and applications for collecting, integrating, analyzing and presenting information.
The predominant approach is a "system and process" approach. But, given the explosion of data volumes around organizations, this approach is reaching its limits.
At Loamics, we advocate a data approach to business intelligence. This approach, based on facts, relies on artificial intelligence and machine learning to bring out the business issues and give the means to deal with them. This is called augmented business intelligence.
What is augmented BI? What benefits does it bring compared to traditional approaches?
What is augmented BI?
The challenges of Business Intelligence
Organizations generate and store large volumes of data from their various departments and processes. For example, a typical company generates customer data, financial and accounting data, data related to human resources, logistics, production data, operating data, etc.
However, all this data is only useful if we are able to extract meaning from it and transform it into decision-making tools. One of the difficulties lies in the fact that the data sources are multiple and disparate.
The role of Business Intelligence is to extract useful information from this mass of heterogeneous data. The goal is to provide decision-makers or business experts with a unified view with dashboards, reports and real-time analysis that will support decision-making.
However, data visualization is only the tip of the iceberg. Upstream, it is all the work of extracting, processing and analyzing data that makes the data usable and intelligible and allows decision-makers to draw insights to optimize business uses.
- Business Intelligence solutions support this work on data and offer many benefits to the company:
- Accelerate and improve decision making
- Optimize internal business processes
- Increase operational efficiency
- Achieve budget savings and/or increase revenue generation
- Gain a competitive advantage
However, traditional BI solutions, based on a "system and process" approach, do not yet fully exploit the potential of data.
Augmented BI, a truly data-driven approach
Augmented BI relies on the most advanced data processing and analysis technologies to meet the challenges of business. It contributes to optimizing the quality of data to generate more reliable analyses and, ultimately, more relevant decisions.
The use of Machine Learning and NLP (natural language processing) are at the heart of Augmented Business Intelligence. These technologies allow a better analysis of available data.
With the Loamics infrastructure, which is based on this approach, data is extracted, cleansed and analyzed to detect patterns and relevant information that, in turn, will lead to automatic responses, without requiring the intervention of a data scientist or data analyst.
Adopting this approach means industrializing data processing. The solution itself detects problems based solely on the available data and generates insights in real time to define and monitor strategic or operational objectives.
What are the differences between "traditional" BI and Loamics augmented BI?
Traditional BI is based on a system and process approach.
The systemic approach consists in considering the organization as a set of elements in dynamic interaction in order to achieve certain strategic objectives. The process approach is a method of breaking down activities step by step to study their functioning and interactions in order to improve the organization of the company.
In order to identify the problems from this approach, it is necessary to model the organization, its processes and their interactions. It is only from this theoretical framework that patterns and possible deviations can be identified.
Consequently, this approach has several drawbacks:
- A high cost and resources of implementation
- Long implementation times (since the modeling has to be done beforehand)
- Late detection of problems
- Reticence on the part of the teams
In contrast, the Loamics data approach breaks with this theoretical approach. It is a bottom-up approach that consists of detecting managerial problems through data analytics.
The solution automatically analyzes all available resources to extract relevant information. The data teams are thus relieved of creating and testing models. These tasks, which were traditionally the responsibility of analysts and data scientists, are now automated thanks to machine learning. Augmented BI optimizes data analysis at all stages of the data journey, from preparation to presentation of indicators.
What are the benefits of augmented BI?
Accelerating data analysis
For companies, mastering data has become a major challenge that goes far beyond IT services. Today, as the digital transformation affects almost all sectors and impacts all departments of the company, data is the driving force for making the right decisions, optimizing processes, improving the customer experience or even gaining competitiveness.
But for this to happen, the data must be easily accessible and usable by decision-makers. To be relevant and actionable, insights must also be available in real time.
Augmented BI perfectly meets this need for reliability and immediacy. Data science and artificial intelligence complement each other to ensure faster data preparation. Insights are easily and continuously accessible for users, who thus benefit from up-to-date information to support their decisions. In addition, the data approach makes it easy to combine different data sources for a more complete analysis.
Improve the quality of insights
With augmented BI, insights are no longer derived from theoretical models generated on the basis of assumptions. Instead, AI algorithms generate relevant information from the data.
As a result, Augmented BI brings up all the necessary information and sometimes even insights that you would not have thought to look for.
Moreover, the solution is able to analyze a multitude of data sources to detect correlations or deviations that will then be passed on to users.
Finally, it is notable that the quality of the insights increases further the more you use the solution. Indeed, the more the algorithms are fed with data, the more reliable their performance becomes.
Democratize the use of data within the organization
Data management is no longer the exclusive domain of IT. Today, all departments have an interest in relying on reliable and relevant insights based on data.
Augmented BI makes data easily accessible and usable for everyone, which encourages decision-making.
In addition, IT departments are freed from time-consuming tasks and can focus on strategic issues.
Augmented Business Intelligence: some use cases
The Loamics infrastructure, with its 3 bricks (data collection, warehousing and algorithms) adapts to any type of data. Augmented analysis contributes to solving managerial problems in e-commerce, marketing, logistics and, of course, in financial services.
Here are some examples of uses we have developed based on our solution.
Augmented BI and Industry 4.0
Augmented BI is integrated to solve problems in the industry sector.
With augmented analytics via Loamics, AI quickly detects problem sites because they are behaving abnormally compared to other sites. Decision makers can then take quick action on these sites.
The AI also allows to cluster sites with similar behaviors to facilitate their management. Thanks to this clustering, the decision maker can apply the same treatment to a homogeneous set of sites. This greatly accelerates decision making.
Augmented BI also facilitates the classification of sites according to priority levels. You can see in real time which sites require rapid action.
Another application: prediction. AI predicts the activity for the coming year for each site based on the results of previous years. Budget preparation and operational monitoring are no longer a headache.
Finally, machine learning algorithms facilitate the implementation of a predictive maintenance system on equipment and dynamic alerts in case of drift.
Augmented BI and e-health
Augmented Business Intelligence also has applications in the field of e-health.
Indeed, augmented data analysis facilitates the detection of disease-carrying genes.
The treatment of skin cancer using epigenetic biomarkers is an example of a concrete application of augmented BI. In this use case, machine learning automatically performs an RNA comparison of benign and malignant cells.