The data driven approach and use of big data have become increasingly important for companies of all sizes that aspire to grow and thrive. With Business Intelligence (BI) and Advanced Analytics (AA), businesses can gain insight from the data they collect and use it to make better strategic decisions. This is the data driven approach: having a more informed view of your business, your customers and the market, helps companies identify growth opportunities and improve operational efficiency.
However, choosing the right Business Intelligence & Analytics solution can be really difficult and expose the company to the risk of undervalued investments (which will not be able to generate the expected benefit) or overestimated (infrastructure that is too complex or poorly used, significantly reducing ROI).
Business Intelligence, Advanced Analytics and Data Visualization: let’s clarify
Business Intelligence, Advanced Analytics and Data Visualization are three distinct but closely related concepts in the field of data analysis.
- Business Intelligence (BI) focuses on collecting, organizing, and analyzing business data to provide useful information to decision makers. The BI includes the analysis of historical data and the creation of customized reports, drawing also from different sources.
- Advanced Analytics focus on data processing in order to derive predictive and prescriptive information. This includes using Machine Learning and Artificial Intelligence algorithms to identify patterns and trends within the data and use them to make predictions and recommendations.
- Data Visualization tools are designed to present data in a visually appealing and intuitive way, facilitating the understanding of data for end users as well as the practice of self-analysis.
A valid business intelligence solution based on Artificial Intelligence (AI) and Machine Learning (ML), therefore, allows to obtain detailed and predictive information, identify patterns and trends within the company data, and to identify real growth opportunities.
Some examples are:
- Predictive Analytics: Use machine learning algorithms to analyze historical data and make predictions about future business trends.
- Sentiment analysis: Use AI to analyze data from social media and other sources to understand how customers perceive the company and its products.
- Anomaly Analysis: Use AI to detect abnormal behavior within business data, helping to identify any problems or fraud.
- Virtual Support: Use AI and Natural Language (NLP) algorithms to provide immediate answers to user questions, improving the customer experience. These include Chatbots from Helpdesk (like our own Andy) or Customer Experience (like Kayla) and numerous other solutions.
Importance of choosing the right BI solution
Choosing a BI solution that meets your business goals is a real competitive advantage, whereas a wrong choice can cause problems such as delays in producing reports, lack of important information, unreliable data and loss of business opportunities (as well as economic).
To choose the right BI platform for your needs, there are some key parameters to evaluate:
- Scalability: the BI platform must be able to handle large amounts of data and grow with the company.
- Interoperability: the BI platform must be able to integrate with other business applications, such as ERP and CRM systems.
- Ease of use: the BI platform must be easy to use for end users, both for data analysis and reporting.
- Customization: the BI platform must allow the creation of customized reports according to the specific needs of the company.
- Security: the BI platform must ensure the security of company data, protecting it from unauthorized access.
- Support and support: the BI platform must be supported by a competent support team and available to provide support in case of problems or difficulties.
Assessment of the necessary tools and skills
Moreover, the realization of a Business Intelligence (BI) project requires the use of a series of specific tools and skills:
- ETL/ELT(Extract, Transform, Load/ Extraction, Load, Transform) tools: they are needed to extract data from different company sources, transform them into a usable format and upload them to the database.
- Relational databases: they are used for the storage of company data and for their access by end users.
- IT architecture and infrastructure: needed to support process development.
- Programming skills: needed for customization and creation of customized BI solutions.
- Data analysis skills: essential for the interpretation and analysis of company data, in order to identify trends, patterns, areas of improvement and select the right mode of representation.
- Project management skills: these are necessary to coordinate project activities, assign tasks and ensure collaboration between team members.
While some of these skills are available internally to the company team, others must be integrated through a reliable and constantly updated BI partner (discover our team here).
Platform validation, release and adoption (and measurement)
The success of a BI project is closely linked to the adoption rate: people must be enabled to use the tools to their full potential. To achieve a high adoption rate by all business stakeholders, you need to follow a number of steps.
- First of all, it is essential to involve the various roles and departments from the initial stages of the project, in order to understand their needs and expectations. In this way, a tailor-made solution can be created for the company, which meets the real needs of the various departments.
- In addition, it is important to create a user-friendly BI solution that is easy to use and understand even for less experienced users. This can be done through the adoption of a business dictionary (Data Literacy) and the creation of clear and intuitive reports and dashboards, allowing users to view and explore data easily and immediately (in addition to encouraging self-service analysis practices, essential for companies that adopt data driven processes at all levels).
- Another important aspect is user training. It is essential to provide adequate training to end users, in order to allow them to use the BI solution effectively and exploit its full potential.
- Finally, you must constantly monitor the use of the BI solution, allowingyou to identify any problems or areas for improvement. This way, you can take timely action to resolve any issues, improve the user experience and maintain a high adoption rate (closely linked to the positive impact of the BI solution on the business).
The steps of choosing the BI solution
Although it is not easy to navigate between different software solutions, hardware structures, integrations with CRM, ERP and Data Visualization platforms, in Blue BI we have devised a method based on the analysis of 3 fundamental aspects:
- Assessment of the degree of digitalization and analytical maturity
- Validation of architecture/methodology already in use
- Selection of technologies and BI platforms adapted to the business strategy
We work with the most important software and platforms of BI and Data Analytics, this allows us to create comparative benchmarks based on customer needs: a fundamental tool to support the customer in the selection of the most suitable technology for his specific case.