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Business intelligence is not a tool…


…it is a strategy that relies on technology. It is the ability to understand, know and solve business problems

The key elements of bBusiness Intelligence are:

  • Transforming data into knowledge
  • Support for decision making
  • Data analysis
  • Strategy and tools

Business Intelligence will help us in making decisions and subsequently, to discover things that until now we didn’t know.

The benefits to be gained through the use of BI may be of different types:

  • Tangible benefits, e.g. cost reduction, revenue generation, time reduction for different business activities.
  • Intangible Benefits: the fact that we have available information for decision-making will take more users to use this information to make decisions and improve our competitive position.
  • Strategic Benefits: Those that provide us the formulation of the strategy, ie, to which customers, markets or which products to address.


Data is The New Gold


Data is no longer regarded as static or stale. In fact, with the right mindset, data can be cleverly reused to become a fountain of innovation and new services. The data can reveal secrets to those with humility, willingness, and the tools to listen.

There is no rigorous definition of big data although everyone has his own one. Quistor thinks that big data refers to things one can do at a large scale that cannot be done at a smaller one, to create new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and government and more.

But this is only the beginning, Big Data marks the beginning of major transformation. Just as the telescope enabled us to comprehend the universe and the microscope allowed us to understand germs, the new techniques for collecting and analyzing huge bodies of data will help us make sense of our world and business in ways we are just starting to appreciate.

The real revolution is not the machines and calculate data but in data itself and how we use it.

LET DATA SPEAK. We don’t always need to know the cause of the phenomenon; we can let data speak for itself.


Why Do We Need a Data Warehouse?

Bill Inmon’s paradigm: Data warehouse is one part of the overall business intelligence system. An enterprise has one data warehouse, and data marts source their information from the data warehouse.

Ralph Kimball’s paradigm: Data warehouse is the conglomerate of all data marts within the enterprise. Information is always stored in the dimensional model. 

From an IT point of view

  • Maintain the growing volume of data and the rising costs
  • Ensure the quality of the different data collected
  • Provide the necessary performance to meet data requests including real-time
  • Eliminate disparate data silos often stored overlapping and duplicated data
  • Ensure data integration.
  • Data governance
  • Eliminate the extraction and manual manipulation of data.

From a Business point of view

  • Need to promote faster innovation and accelerate the transformation of the business.
  • Have a single version of the truth.
  • Find new approaches to discover information that creates business opportunities.
  • Access to real-time data and advanced analytics to get the most of our data to improve our business


Data integration


Data integration is the process of combining data from different sources into a unified view: starting from ingestion to cleansing, mapping and transforming to a target sink, and finally making data more actionable and valuable to those accessing it.

There is nothing that brings data problems to the forefront like the data-integration process. The process brings to light challenges with data integration when data is incorrect, missing, uses the wrong format, incomplete, and so on. So Data Integration is important because:

  • Save time
  • Reduce errors
  • Data Quality
  • Improve data quality
  • Delivers more valuable data

Uses case

  • Data lakes and big data solution
  • Business Intelligence traditional solutions
  • Creating Datawarehouse
  • Master data management
  • Data migration

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