Data profiles & data maturity
The area of expertise and type of work related to data vary within the organization by profile. Designations of titles or profiles may vary by organization, but often the profiles below are used.
How to serve an experienced data user: with a report or a dataset?
20 nov., 2023
Encourage data literacy by offering the right data
When it comes to insights within an organization, reports often come to mind. Such reports present figures that give someone insight for making decisions. But what if insights are needed into something for which there is no reporting yet? Or what if you are researching a newly launched business line for which no reports are available at all? In such cases, as a data user, you may be better served with a dataset than by waiting for the first report.
In this blog, Jerrold Stolk - Technology Lead within the Data & Analytics Team - takes you through the challenges and opportunities around sharing data within the organization.
Data classification
Data is a broad term and can be classified in several ways. Important characteristics for classifying data have to do with 'data quality'. These can include comprehensibility, completeness and accuracy of data. Using these characteristics, data can be divided into three groups: bronze, silver and gold. The better the data scores on the characteristics, the higher the 'medal'. This subdivision comes from the Delta Lake setup of Databricks.
- Bronze: This level represents the lowest level of data quality, where data is not checked for correctness, completeness and comprehensibility. It is not structured to be easily used by analysts. The structure of this data is often the same as that of the source.
- Silver: This level represents a higher level of data quality, where data is checked for accuracy, completeness and understandability. The data has been cleaned and prepared for use. For example, the data can be modeled after an entity model, which models data after the structure of the business or process.
- Gold: This level represents the highest level of data quality, where data is thoroughly checked and validated. This level ensures that the data is of the highest quality, creating confidence in decisions and avoiding errors.
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Report Specialist
Someone who focuses on preparing and maintaining reports, usually based on data from databases or other sources. In addition to creating clear and user-friendly reports, a Report Specialist also supports decision-making by visualizing data.
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Self-Service Data Analyst
someone focused on democratizing data analysis. Self-Service Data Analysts develop simple and easy-to-use tools and dashboards that allow non-technical users to perform data analysis independently.
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Advanced Data Analyst
Has a deeper understanding of statistics and data analysis techniques than Report Specialists. This group is capable of developing and executing more complex analysis and prediction models.
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Data Scientist
Is concerned with the overview of data to gain new insights and support decisions. Often has a multidisciplinary background, including computer science, statistics and domain knowledge. Applies advanced analysis and modeling techniques to large and complex data sets.
Data authorizations
The data profiles mentioned have different data needs and data literacy. Based on these differences, an overview can be created that combines data profiles and data groups. This authorization gives each data profile access to the appropriate data group(s).
The road to a data-driven organization
Organizations want to be more data-driven, but what does this really mean? Being 'data-driven' means making decisions and solving problems based on data and insights. The focus is on using data to drive business strategies, to inform product design and development, and to measure and optimize results. To become data-driven, an organization depends on a strong data culture and the necessary technology and tools to collect, store and analyze data. There must also be the capacity to turn insights from data into actionable business decisions.
An organization does not become data-driven overnight. It is a process for which the curve below can be used. Organizations are moving from looking back to looking forward, to forecasting, to automating decisions. Data literacy is a key driver of this curve. Organizations can foster this data literacy with investments in behavioral and cultural change to increase data quality.
One way to make sharing data within the organization more manageable is to identify data profiles and make data groups clearly visible. Providing the right people with the right tools and data in the right form, at the right (data-driven) time, is an important step in the process of becoming a data-driven organization.
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