How to implement an effective Business Intelligence

Bi Efficace

In the recent years we assisted the rapid growth of Business Intelligence (BI) systems, which are more and more often adopted by companies that want to improve their business in several aspects.

BI’s adoption has become one of the most important technological and organizational innovation that a company may pursue to improve its decision-making process and embrace a data-driven philosophy.

The concept of a technological system to help businesses in decision-making is relatively old, and traces back to the 1950s, but it is in the last years that Business Intelligence has become very popular among companies, thanks to the recent developments in technology, which allow Big Data management and real-time visualization and analytics.

At the same time, more and more managers are trying to exploit data to extrapolate insights with the aim of optimizing processes and creating value for their companies. As both product and firm life-cycles are getting shorter, the last years have seen the emergence of the necessity to make swift decisions in order to gain competitive advantage.

In this context, having an efficient Business Intelligence architecture could be of great help for top managers, who need to retrieve key information from various sources to take effective tactical and strategic decisions. The way in which Business Intelligence architectures are constructed may vary according to the specific nature of the company or the industry in which it operates.

However, there are few essential elements that should be present in every BI system that pursues an enhancement of the decision-making process in company’s both short- and long-term strategy.

Let’s see them.

The essential ingredients of an effective Business intelligence system

1 – Data sources

Certainly, data are the basic ingredient of Business Intelligence. For Data we mean every way to codify and store information, whether it is structured or unstructured. Structured data are data that we can represent in relational tables, while unstructured (or semi-structured) data are those not always suitable for relational databases such as images, videos, emails, tweets, audio files etc.

Typically, every business has operational sources of data, such as the ERP or CRM, which produce structured data, and other sources, as social media, IoT devices, websites… that may provide different kind of data, both structured and unstructured.

Therefore, it is important to have a clear idea of where our data “physically” exist and which key metrics we need to monitor and analyze.

2 – Data Warehouse (DWH)

When talking about Business Intelligence, a common mistake is to identify BI only with reporting and visualization tools, which are the “final instrument” that constitutes a well-designed BI system. As a matter of fact, without a solid Data Management system behind reporting, BI could be irremediably impaired and potentially become counter-productive. Therefore, a key component of an efficient BI system is an instrument able to centralize data to ensure a coherent vision across the entire company and the transparency and efficiency of data manipulation tasks. Based on that we can define the Data Warehouse as the beating heart of a Business Intelligence system, at least with respect to “traditional” business data (i.e., clients, sales, products…). In addition, a company may choose to implement a Data Lake, which is generally more flexible and best suited for advanced analytics, although higher expertise is required.

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3 – Data Quality

As far as we know, and it is good to stress it again, data are the basic ingredient of Business Intelligence. Hence, in order to have an efficient BI system it is crucial to pay attention to Data Quality. For Data Quality we intend the accuracy of information, which is the matching between the metric recorded and the actual event, and the consistency and completeness of data. Having consistent data mitigates the risk of having conflicting reports, which produce different versions of reality. Completeness is related to the grade of exhaustiveness of information, namely ‘how well the data describes the actual event’ (for example, it may be necessary to know not only the date but also the time of a specific event). Thus, attention to Data Quality is a key factor in realizing an efficient BI system. To ensure data of high quality is necessary to adopt the proper instruments, as a Data Warehouse, ETL tools (Extract, Transform and Load) and Data Cleansing / Data Entry instruments to comply with the quality standards. Eventually, it would be beneficial to spread a data-driven culture to every employee at all levels (in this respect, it may be useful to appoint some “data stewards” – people that are responsible for Data Quality).

4 – Attention to users

Business Intelligence makes use of various instruments, which may differ for scope, function, and expertise required. At the same time, different units of the same company may use BI for different purposes: improving logistics and customer satisfaction, reduce supply costs, or increase sales in a particular region are just few examples. Therefore, there could be different strategies depending on the specific goal that Business Intelligence is pursuing. Some companies prefer sharing their data across the entire company, giving access to every employee without restrictions, while others choose to filter data and instruments depending on the role or profile of the final user. Having highly accessible data may result in more dynamic and rapid analyses, but proper restrictions can improve the decision-making process and avoid confusion and common mistakes. That is precisely the case of relatively complex infrastructures as Data Warehouses or Data Lakes, which require more attention and expertise compared to front-end reporting tools. A good strategy could be granting access to real-time data for those who deal with BI at the operational level, while giving to executives the final figures to elaborate long term strategies. Either way, attention to users represents a crucial element for an effective Business Intelligence.


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