Introduction to Business Intelligence and Big Data
One of the most fashionable terms in the digital and business world is business intelligence, a term that goes hand in hand with Big Data. Both concepts are part of the movement of digital transformation of companies, and in this article I will make a brief introduction about Business Intelligence and Big Data, so that you can get an idea of the importance of applying it in your company.
What is Business Intelligence?
When we talk about BI, we refer to the ability to transform the data we have into information, and later into useful knowledge for the company. It is the best way to optimize the decision-making process.
From a more pragmatic point of view, we talk about the set of methodologies, technologies and applications that allows us to transform all existing data, whether from internal sources or from external sources, into structured information for exploitation, analysis or knowledge.
Business Intelligence provides a great competitive advantage for the company, since it will obtain privileged information to take the right steps in business.
Relevant Business Intelligence Terms
Next, I show a series of very common terms in the world of BI and Big Data. Terms that need to be known in order to understand a little more about this complicated world:
- Datamart: it is a departmental database. That is, in that database we will only find information about a specific department of the company, be it finance, marketing or logistics. It is characterized because its data structure is optimized to study the specific data of that department.
- Datawarehouse: this is the corporate database. Integrates and refines information from sources, so that it can be analyzed later. The creation of a data warehouse is the first step to implement an optimal strategy for the company. Its main advantage is the way in which information is stored, the most famous being the star or snowflake tables. The information in this case is homogeneous and reliable, allowing a hierarchical treatment of the same.
- ETL (Extract, Transform & Load): extraction, transformation and loading of information. It is the process to get the data to become valuable information, the ETL process. Information is obtained from different sources, both internal and external, subsequently filtering, cleaning and grouping the information is carried out and finally, the data is organized in a database.
- OLAP Datamart: they are based on OLAP cubes, which are built by adding the necessary indicators of each relational cube.
- Datamart OLTP: the most common is this case is to optimize performance, through filters for example, and take advantage of the particularity of each department.
What is Big Data?
When we talk about Big Data, we refer to the large volume of data, both structured and unstructured that exist. Although the important thing about Big Data is not the data itself, but what can be done with that information. The Big Data is usually more related to the external databases of the company, and as I will explain in the next point, the great speed of data generated in a short time is the main problem. You have to know how to differentiate good data from non-good data, because, after all, if we use data of poor quality, the decisions will not be correct.
What are the 5 V’s of Big Data?
The Big Data is composed of 5 dimensions that characterize it, then I define the 5 V’s of Big Data:
- Volume: the volume to be analyzed is massive. Terabytes of information are produced every day, and the capacity of the databases is duplicated every two tables. To give you an idea, all data produced in 2 days, is more information than all generated until 2003. This makes managing this data a challenge.
- Speed: the data flow is not only massive, it is constant. The great speed of generation of data causes that they are out of phase quickly. So companies must be skilled, and they have to collect, store and process that information at a great speed.
- Variety: the origin of the data is very heterogeneous. They come from different sources, internal and external, and can be structured or unstructured, with 80% of the data of Big Data unstructured.
- Truthfulness: there are many data that arrive incomplete or of poor quality, and if we use wrong data, we will make wrong decisions in the company. The uncertainty about the veracity of the data causes doubts about the quality of the data in the future. That is why it is important to make sure that the data collected is valid.
- Value: once the data is transformed into information, you have to know what value they give us. The more value the data have, the more performance we will get to them.
What is Business Intelligence and Big Data for?
It is simple, for decision making. In a world as globalized as the current one, the secret of success lies in the data. We have a large amount of information at our fingertips, which well analyzed will allow us to make operational and strategic decisions for the proper functioning of the company. But always have to take into account the quality of such information.
The main problem that has Business Intelligence and Big Data is, without a doubt, the cost, both to create a correct structure of information and to have the right equipment for the analysis of it. But it is an investment, which in the end will generate a reward for the company.
I trust that my article on Business Intelligence and Big Data, will help you to take the first step in this complex world, but fundamental for any business. If you want to go deeper into the subject, you can always do a master’s degree like the one offered by Telefónica at its technological institute.
See you in the next article!