After much discussion about its possibilities and new horizons, Big Data today shows its results. Companies are collecting, storing, and combining data from different sources and formats to achieve competitive differentials. Whether structured or not, the diversity and the large volume of data demand adequate methods and solutions to manage, process and analyze this data to support decision making.
Customer browsing on sites, location and movement captured by mobile devices, social networks, blogs, collaborative sites, and other Internet channels are examples of sources that generate valuable information to improve customer focus or even design of new products.
The capacity of current architectures to submit large volumes of data to complex processes enables the expansion of the data domain considered in the projects. You can use much longer historical data on company transactions, gather and cross data from departmental silos, calculate new metrics for entire databases, use ever-increasing data volumes in studies and tests, aggregate data from digital sensors or log files, interpret unstructured data, and use complex algorithms to analyze and extract relevant information from these data.
The use of algorithms of artificial intelligence and machine learning is another great step achieved thanks to the commoditization of powerful computational resources, enabling the Big Data projects. The use of such algorithms presents concrete and increasingly common cases of more relevant and natural communication with clients, offers based on actions, profiles or behavioral history, identification of frauds and opportunities.
The adoption of Data Lakes is already taking place in organizations that seek competitive differentiation from the data. This centralized repository must be organized and its data layers clear so that the goal of having all the information available for analysis and generation insights does not become a huge amount of data difficult to understand and use – the Data Swamps. The organization of the layers for data ingestion, validation, consolidation and availability of the different views of consumption is paramount for the successful structuring of Data Lakes. Paying special attention to Data Preparation and using appropriate methodology and solutions for the structuring of the Big Data ecosystem is decisive so that the data is available in the appropriate time and with the necessary quality for assertive decisions.
Assesso guarantees the choice of the best strategies and the most suitable models for success in the implementation of Big Data projects. Both through specialized consulting, supported by a well-defined methodology and used in a large number of projects, and through DataCare®, which is fully compatible with Hadoop, it guarantees adequate validation and processing of large volumes of data with great performance.