Describe features of analytical workloads – Describe core data concept

Describe features of analytical workloads

Analytical workloads play a pivotal role in extracting valuable insights and patterns from data to support informed decision-making and strategic planning within businesses. These work-loads involve complex data analysis, aggregations, and transformations to uncover meaningful information.

18 CHAPTER 1   Describe core data concept

Analytical workloads encompass a range of activities, such as data exploration, statistical analysis, data mining, and predictive modeling. The process begins by identifying relevant data sources and extracting the required data. The advanced analytics techniques, such as data visualization, machine learning, and statistical algorithms, are applied to gain insights and patterns from the data. The results are interpreted and translated into actionable business intelligence, which results in data-driven decision-making.

Analytical workloads serve different data personas within organizations.

• Data analysts: Data analysts leverage analytical workloads to explore and analyze data, uncovering trends, correlations, and patterns that provide valuable insights. They use statistical techniques and data visualization tools to communicate their findings effec-tively to stakeholders, resulting in evidence-based decision-making.

• Data scientists: Data scientists go beyond analyzing data and utilize advanced analyti-cal methods to develop predictive models, machine learning algorithms, and data-driven solutions. They leverage analytical workloads to build models that forecast future trends, identify opportunities, and optimize business processes.

• Business executives: Business executives rely on analytical workloads to gain high-level insight and make strategic decisions. They rely on reports, dashboards, and visual-izations generated by analytical workloads to monitor key performance indicators, track business metrics, and assess the effectiveness of strategies.

• Data engineers: Data engineers support analytical workloads by designing and implementing the data infrastructure necessary for data analysis. They ensure that data is ingested, processed, and made available in a format that facilitates efficient analysis.

They collaborate with data analysts and scientists to ensure data quality and reliability.

Analytical workloads are supported by various technologies and tools, including data plat-forms, machine learning frameworks, and business intelligence tools. These solutions provide capabilities for data exploration, modeling, visualization, and advanced analytics.