Business Intelligence Process Software

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Business Intelligence Process Software – Business Intelligence (BI) – using data about business operations, behavior, marketing, etc. to make informed business decisions – is a foundation for successful digitalization in the microfinance sector and other places. Customer data dashboards are key business intelligence tools for microfinance institutions (MFIs), allowing them to use existing data and improve understanding of their customers’ behavior. However, these dashboards require a lot of effort to analyze the data they provide, which leads us to ask, which AI tools will be better suited to ensure that MFIs, regardless of size and resources, are able to gathering insights from customer data dashboards and other business intelligence tools?

Offered by cloud companies, Software-as-a-Service providers, and private technology entities, AI tools will become commonplace in the near future. Below, we outline how AI can help business intelligence today, and where we believe it is becoming irreplaceable. For that purpose, we will use the data analysis journey developed for the microfinance digitization project explained in the video tutorial (see Figure 1).

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An AI model, especially Native Language Processing (NLP), can help capture and interpret requirements. For example, AI chatbots can engage with stakeholders to ask key questions and record responses, ensuring consistency and gathering precise requirements.

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The process of understanding a business problem and aligning data analysis with strategic goals requires human involvement. Interactions between product owners and data analysts are critical to understanding trends, priorities, and context behind requirements.

Data analysts can ensure that the required data points exist in the data warehouse and assess which data points can be replaced when the data is no longer needed. Machine learning algorithms can analyze the metadata of the data warehouse and present the relevant datasets for a given problem statement. AI can automate the discovery of relationships and dependencies in datasets to reveal additional data points that should be collected based on previous projects.

The final decision about the relevance and validity of the data often requires a human analyst who understands the business context. For example, there may be legal or ethical reasons to delete certain data points, which AI may not be able to anticipate.

Data analysts write codes in different programming languages ​​such as Python, R, and SQL for data extraction. AI can help with debugging and building blocks of SQL queries (and other programming languages) which can reduce the time spent writing complex queries and can reduce errors in when optimizing the performance of queries.

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The application of business logic and the translation of business requirements into technical requirements are best performed by data analysts. In addition, interpreting and managing situations and other things, especially those related to context, often requires people to understand and make decisions.

Using output data, analysts convert it into charts and graphs while choosing the best view to address the problem statement. AI can provide a very useful way to view specific datasets based on their characteristics. For example, AI can determine whether a line graph or a bar graph is better for a given set of data, improving the speed and quality of visual selection.

A good presentation should be supported by a well-controlled narrative by data analysts. Understanding what the data is telling and the best way to present that message to key stakeholders is often a human need. People are also better able to understand and adjust to the needs and understandings of the news audience.

Data analysts and stakeholders review the disclosure and verify if there are areas of concern, for example, missing data or reliability issues. AI models can be used to predict potential issues in data or interpret observations. By learning from previous validation sessions and stakeholder feedback, AI can identify potential issues before the validation phase (ie, requirement gathering or availability) and save the time and reduce the number of change cycles.

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Validating the data and analysis process requires stakeholders to bring their business knowledge and contextual understanding to bear. They understand the results of the findings and challenge them in ways that AI cannot.

Data analysts manually check for error messages and failures when updating the report. AI can be used to monitor the deployment of dashboards, checking for errors or anomalies that may occur. AI models can also change the update frequency based on data value and business needs, balancing time, and resource usage.

AI can monitor and manage errors during deployment, but when errors occur, humans are better at troubleshooting and finding solutions, because they have a better understanding of the overall system that control of delivery.

Data analysts distribute the report to a broad audience, customizing the format based on audience and delivery context. AI can help personalize reporting to different audiences based on their interests and past interactions. AI voice modeling can be used to write clear, concise summaries and insights from perspectives.

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The final manipulation and interpretation of data is often human-centered. People are better at understanding other people, so they are better able to communicate complex findings in terms that stakeholders can understand and agree with.

In conclusion, we think that knowledge is already good enough to improve business intelligence. This is especially true for tasks on the data analytics journey that need to be predictive rather than judgmental, which can easily be automated. However, (for now) AI tools may not solve fundamental weaknesses such as outdated, inefficient, or non-existent banking systems. And they may not be able to convince MFIs (or other financial service providers) of the need to deploy staff with strong technical and data analysis skills.

Please share your thoughts and experiences with AI or BI using the comments below or by emailing

Microfinance institutions benefit from generating value for their businesses and their clients through digitalization that includes these activities in business intelligence. This Technical Note describes an approach for improving business intelligence and interventions that require minimal investment in technology. also offers a customer dashboard library with detailed instructions for data groups and a tutorial video.

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It’s a technical process that helps analyze data and bring insights to organizations. Therefore, the use of BI affects strategic, operational, and organizational decisions.

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Business Intelligence (BI) analysis can be divided into 4 parts. These include Image analysis, analytical analysis, prescriptive analysis, and predictive analysis.

Overall, these are the four types of analytics used to drive decision making from day-to-day operations to key strategic business decisions.

These are the 4 steps a BI system performs to help companies turn data into actionable insights.

With the help of a Business Analytics tool, companies can turn raw data into something useful for real-time decision-making purposes.

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With these benefits, BI helps to make better business decisions that allow organizations to increase their performance, income and most importantly gain a competitive advantage over other businesses.

Organizations are increasingly realizing that effective use of BI tools and technologies can turn their business data into valuable information or insights. These insights can be used to make decisions that can accelerate business growth and increase profitability.

Today, organizations in every business sector need to manage and monitor their data. It can help you make better and faster decisions with diverse BI tools like Microsoft Power BI, Tableau, and SAP BI. As an experienced service provider with sophisticated business analytics, we help you analyze customer behavior patterns and market trends, optimize road operations, and ensure a more efficient workflow.

Founded in 1996, in Michigan, Inc. an information technology company specializing in IT information, and staffing. Over the past two years, the company has offered innovative technology solutions such as AEM, Liferay, Business Intelligence, and more to global customers. With a vision of the future, it has led innovations in Healthcare IT solutions and implemented Industry 4.0 in the Box project, IIoT is a major project in collaboration with AWS (Amazon Web Services ).

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