FluenFactors Blog, 2022.
Advanced business users in data or "Business Scientists" are individuals who have a deep understanding of data and its potential to drive business insights and outcomes. These individuals typically have a strong background in business, as well as expertise in data management and analysis. They are able to use data to inform and support key business decisions, and to help their organizations leverage data to gain a competitive advantage.
Advanced business users in data have a wide range of skills and abilities. They are typically proficient in using data analysis tools and techniques, such as SQL, Excel, Python, machine learning and business intelligence tools, and are able to manipulate and analyze large datasets to uncover insights and trends. They are also able to communicate the results of their analysis in a clear and compelling way, using visualization and storytelling techniques to present data to different audiences.
In addition to their technical skills, advanced business users in data also have a strong understanding of business principles and practices. They are able to think strategically and align their data analysis with the goals and objectives of their organization. They are also able to work effectively with different teams and stakeholders, and to apply their data expertise to support decision-making across the organization.
On top of that, Data leaders are responsible for extending the traditional vision of data self-service, which often focuses solely on dashboarding and reporting, to include a richer self-service layer with advanced capabilities. This self-service layer should provide users with the ability to access, manipulate, and analyze data on their own, without the need for specialized technical skills or support from data professionals.
Some key features that data leaders should consider when developing this self-service layer include:
Data discovery and exploration: Users should be able to easily search, browse, and explore data to find the information they need, without the need for technical expertise. This may include tools for visualizing and interacting with data, such as data exploration and visualization tools, as well as natural language processing and machine learning algorithms that can help users find and understand data.
Data manipulation and transformation: Users should be able to easily manipulate and transform data to support their own analysis and reporting needs. This may include tools for cleaning, transforming, and merging data, as well as support for advanced data manipulation techniques, such as SQL queries, data apps and data scripting.
Advanced data analysis: Users should be able to perform advanced data analysis, including predictive modeling, machine learning, and statistical analysis, without the need for specialized technical skills. This may include support for common data analysis tools and techniques, such as R and Python, as well as pre-built algorithms and models that users can apply to their own data.
Collaboration and sharing: Users should be able to easily collaborate and share their data and analysis with others, including colleagues and external partners. This may include support for sharing data and analysis through online platforms, such as data lakes and collaboration tools, as well as support for collaboration and feedback within the self-service environment.
In the following graph, you can see the most demanded features by advanced business users:
To reach these features, organizations need to increase the level of data maturity in aspects like data governance, data segmentation, data privacy, data catalog, cloud security, data observability, fast data access, optimized data models and data quality.
Advanced business users in data are learning quickly and will demand more and more data platform capabilities in the next years. So, Is your organization well-prepared to manage these requirements?