• by Admin

Big Data brought to existing decision support systems, such as Data warehouses, new ways of generating, ingesting and analyzing corporative data. It opened the possibility of extracting useful information from existing structures in formats never considered before. It empowered businesses to make more accurate decisions based on valuable insights from a better comprehension of their data. With the help of Machine Learning Models and Data Science tools, these new platforms can successfully enhance the existing Data warehouse systems transmuting them into robust analytical platforms.

The Data Science professional plays a decisive role in this new world, as they can extract and integrate all these new data ingestion pipelines. This professional is responsible for taming all these data to optimize computational resources and handle inconsistent data using the most performant way. This professional is responsible for building up its own personal Data Science tool stack, not limited by possible technical constraints to enhance existing or create new BI platforms while lowering their operational costs.

The Data Science professional is the one that can orchestrate all the data pipelines from existing Operational Systems into the Enterprise Data Warehouse to be consumed by their existing OLAP servers, thanks to the successful implementation of the Data Analytics Toolkit. This professional can use open-source tools such as Python or Kubernetes to architect and deploy the machine learning implementations. They are building up BI Platforms with less cost, providing the same, if not better sometimes, results. This professional also acts as a decision-making consultant on the technological stack to correlate the data, and its metadata, into coherent data marts due to siloed business logic responsible for its ingestion at the source systems.

The Data Analytics toolkit grants the Data professional the necessary knowledge on enhancements needed by BI tools to explore new subjects. Knowing how to extract valuable information with techniques such as have Machine Learn models trained from your labelled data to help make decisions from uncategorized data, creating self-healing, more accurate data pipelines that learn with minimal human intervention.

The Data Analytics Toolkit also allows you to clean and wrangler your data from your sources to extract precise insight. You are adding to the mature BI environments the extension on their Data Science capabilities. Being able to enhance the reach of existing decision-making BI solutions into a higher level of quality never thought before. The Data Analytics Toolkit allows the possibility of analyzing streaming data into your existing BI solution, giving chances to apply available tools to statisticians or mathematicians only, expanding exploration of the data consuming less computational power.

The Data Science professional enhances the Data Analytics capabilities, tailoring more captive storyteller dashboards or more adaptive KPIs. Thanks to the correlation of data never integrated before due to technical backlogs. The Data Analytics professional has the necessary tools to work not only on integrating this Data but also in a secure environment. They enhance the decision-making solutions to be adaptive from new patterns with less need for human intervention. Reaching an audience never thought before, the data professional can create reliable OLAP Servers and better model ODS from more concise data from better-integrated data sources. Deploy all stages of your data warehouse more fluidly, allowing the users to experiment with new products more dynamically.

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