Imagine a physical warehouse. It can contain the owners or lessees inventory materials, or it can serve as a storage for multiple businesses. The warehouse has labels and shelves per asset owner in order to clear identify to who the stock belongs to. Moreover the warehouse administrator keeps a record (inventory) of everything that is inside the warehouse.
This is the idea for a data lake, datawarehouse and Business Intelligence specialist. The first is the warehouse without any organisation (just the all stocks), the second has a clear structured labeling and everyone knows where everything is, and the later is responsible to produce statistics from all the data inside the warehouse.
The possibilities are numerous. Companies started to leverage the power of data in 2001. Nowadays any Middle-sized and Enterprise company is using Business Intelligence (BI) to improve and develop its products/services and other areas such as financial planning, customer support, corporate strategy, and other fields where their data gave them insights.
Data from data generating software such as Hubspot, Microsoft Dynamics, ZenDesk, Jira, or any other platform that the employees from a company use, generates data that when mixed produces insights to the leadership. These insights are valuable because they can be from business optimisations up to business opportunities.
They are considered the most valuable asset of any company as of the moment.
Contrarily to populat beliefe, in order to set up a Data warehouse and start leveraging the power of data, a company needs to make a very small investment compared to the outcomes that will be receiving. The overall process looks like this:
Implementation.The process of setting up a Data lake that collects data from any data-generator that the company produces, plus the processes to organise that data into the Datawarehouse (following an ETL system) or in more advanced cases an ETL regulated by ISO standards and QMS.
Analytical.The process of recruitment the right business analysts with the right competence or build AI interfaces that can look at the data from the Datawarehouse and produce insights from it.
Risk assessement.The process of training and recruit Business Intelligence specialists that can look at the insights and evaluate if the same will be valuable to the leadership or if the same present a risk to the company operations.
The strategies and technologies used by businesses for the data analysis and management of business information are referred to as business intelligence (BI). Common uses for business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
Enterprises can utilize business intelligence to assist a variety of business choices, from tactical to strategic. Pricing or product positioning are examples of fundamental operational decisions. Priorities, goals, and directions are the most general terms used to describe strategic business decisions. In all situations, BI is most effective when it combines data from firm sources that are internal to the organization, such as financial and operations data, with data derived from the market in which a company works (internal data). When internal and external data are merged, they can paint a full picture that, in turn, produces a "intelligence" that cannot be obtained from any one piece of data alone.
Imagine all these decisions, strategies and technologies automated and giving you the best outcome based on the analysis of all the data inside and outside your company. Yes this is the door to the true AI.