Ingredients for an effective Business Intelligence solution


In the last 10 years, the Business Intelligence (BI) and Data Analytics market have seen rapid and continuous growth; the amount of data and information available has dramatically increased, and so has the need to monitor, tidy up, and analyse it. Today, adopting and implementing a BI solution represents one of the most important and challenging innovations for organisations from both a technological and organisational aspect to streamline the decision-making processes and embrace a data-driven approach.

The concept of a corporate reporting system to support the decision-makers has been around for a very long time. But it is only in the last two decades that BI has garnered incredible popularity and adoption thanks to new technologies which can manage and transform huge amounts of data and visualise reports and results in real time.

Meanwhile, the need, the curiosity, and sometimes the fear of falling behind competitors is pushing managers to exploit the most from their data and information as they try to bring value to the business and optimise the internal processes. Furthermore, as company and product lifecycles are becoming shorter, getting to proactive and correct decisions quickly may give an advantage against the competition.

To achieve this, it is fundamental to build a proper BI architecture which can provide top management with essential data from various sources to efficiently support strategic decisions. There are many ways to build your own Business Intelligence solution depending on the type of business and market. Nevertheless, there are some fundamentals that should always be adopted in order to realise an effective strategy that is able to lead to winning long term decisions.

Let us see which are the fundamental ingredients for a proper BI solution.

1. Data sources

Obviously, data is the base ingredient for any BI solution: data is whatever can provide information and it can be available in different formats, either structured or non-structured.

Structured data is core and can be represented in a tabular format; typically, every company stores its operational and financial data in management tools such as ERPs or CRMs.

Non-structured data is that which cannot be represented in tables, such as documents, attachments, videos, and emails. This data can be produced and captured, for example, from websites, social media, and IoT devices. The real skill is to understand where all this information is stored and to then combine core data with any other extra information in order to provide your business with essential and clean information; in developing a BI solution, filtering and understanding that 20% of information represents 80% of your business is paramount.

2. Data warehouse

When referring to BI, the common mistake is to identify it only with the front-end reporting, consumed by analysts and management. However, if the data management system behind our reporting is not solid and well structured, creating extra value for the business through a BI solution can become a long and painful process, and sometimes even be self-defeating.

It is then necessary to own (and if not yet, to design and develop) a structure capable of centralising your data in a single point, to ensure great performances in querying data; to provide clarity and flexibility in managing all the ETL processes; and ultimately to guarantee consistency across all reports.

Ultimately, the Data Warehouse (or sometimes, in different scenarios, a Data Lake) could be identified as the core part of a BI solution, where all the different data to be analysed is merged in a controlled and organised way.

3. Data quality

Once again, data is the key ingredient; clearly, to get to an efficient and solid BI system it is essential to put the best effort into ensuring the quality of the data. What does this mean?

Data must be accurate, meaning the measure stored must correspond to the real event. Data must also be consistent, meaning it has to be the same across the whole BI solution in a way that different reports on the same data are not in discordance, avoiding the risk of generating confusion and different visions of the real facts. But data must also be completeit must provide all the details to properly describe an event. On this last point it is important not to identify all the possible data, but only the most relevant: ultimately, we need to take into consideration the performances of a BI solution as well.

So, data quality is a key factor for an efficient BI solution which provides relevant information to the users. To grant this quality, it is important to adopt the proper tools: a Data Warehouse; solid ETL processes; and any additional tools and processes for data entry and data cleansing.

Users also play a role: they must participate and be active in the process, reporting any data issue or inconsistency. Usually, different level of users are designated, with some responsible for checking the quality of the data; it is important to spread out this culture for data at all levels within the organisation.

4. Focus to the end-users

Organisations implement BI solutions for either a specific division or many different divisions, each one with different objectives: they want to improve their financial reporting and planning, increment revenues, reduce their procurement costs, streamline the logistic processes, etc. Different strategies are therefore used to achieve different objectives within the same company.

It is often a requirement to restrict access to sensitive data to specific users who are competent for that matter. This approach can improve the decision-making processes avoiding confusion and errors. However, accessing the information without restrictions could produce a quicker and more dynamic reporting and analyses.

While front-end reporting solutions are relatively simple for any user to use, the data model and processes behind the scenes can be quite sophisticated and require specific expertise. This is why a good strategy would be to allow direct access to real time operational data to whomever follow the operations of the business, while top management should get final and summarised reporting to focus on mid to long-term strategies.

Most importantly during a BI solution implementation, you need to get all the end users involved, on a daily base, in the development of the application; you could create the most technically perfect solution, but if end users don’t know or don’t like the new way they have to interact with their data, rest assured…the project will be a failure. Developing a BI solution is a learning process for anyone in the business; the final objective is to enhance work and performances changing the existing ‘old’ processes. End users need to feel part of the changing processes and to have the ability to provide constructive feedback for a flexible solution across the business.


In this blog we explained some of the main ingredients to be considered when implementing a BI solution. Obviously, realising an efficient BI solution that can provide the expected results is not a simple task; there are many more factors that should be considered and could also interfere during the entire process for a successful outcome. To outline a winning strategy, proper detailed planning is required: clear vision, clear sources, clean data, a strong architecture, and good teamwork.

If you are looking for a data analytics platform to help you improve your reporting process, we invite you to explore Microsoft Power BI, a leading analytics and BI platform from Microsoft.

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