Sources and Data Processing
In order to get a full view of the market and its phenomena, it’s important to process all data from all sources available to the company. Data is heterogeneous and disharmonious by source and definition, aggregated and disaggregated with different levels of granularity, in terms of quantity, but often arguably with regard to quality. The first step in the strategy is therefore to collect the data in full, transform it and often clean it in order to then analyse the data interconnections. This process, defined ETL (Extract – Transform – Load), is the basis for any Business Intelligence project.
In a fluid reality, the Data Warehouse concept has its limits
In 2010, James Dixon coined the term “Data Lake” to indicate the set of raw data available from different sources, which cannot be managed separately.
Extract
Data quality is the focal point
On-Premise Data, Data Warehouse, Open Data, Data Could, IoT Data, and Big Data: the information available to your company comes from diverse sources, so are likely to come in different formats. From Data Lakes to Data Warehouses, ITReview can help you achieve top- quality collection and processing of raw data, providing you with “data as a service”. This step creates the right basis for any Data Analytics and/or Business Intelligence project.
Trasform
Like tuning the instruments in an orchestra before a concert
Regarding Big Data and Data Lakes, ITReview relies on Trifacta, the Data Wrangling platform able to manage raw data coming from multiple sources in surprisingly quick time. Thanks to its Machine Learning process, Trifacta can classify and transform the data of interest to the end user. Based on self- learning algorithms, TriFacta creates a “Data as a service” catalogue, which is ready to be used by Data Scientists or by more traditional Data and Business Intelligence Analysts.
Load
Raw data has now been refined
Thanks to ITReview’s platforms, end users can fine-tune parameters and explore complex information, getting immediate feedback. This is fundamental for the next step: Data Analytics.
