People – Data Democratization, Governance, and Security

People

As part of self-analytics, business stewards and IT data steward teams are the core units of work within a democratized framework, and IT data stewards’ teams are the core teams that support a wide range of internal and customer-facing data challenges and work within a democratized delivery model. They share accountability for technology and business outcomes. Technology becomes important and essential to achieving growth and gaining competitive advantages.

Hybrid teams manage business capabilities in multiple domains, such as order management, supply chain, and so on, which includes ERP, payroll, or an internal employee portal. This hybrid team also manages and acts as doorman for access points, tools, and processes to digital services, such as campaign management.

Hybrid teams working in the product area manage commercial products that either augment existing ones or are new revenue streams; e.g., customer prediction models, product engagement applications, etc. To succeed in the long run and to be confident in its end-to-end capabilities, it needs to offer excellent services. To sustain delivery and content, it needs to fit with the business case to gain wide acceptance across organizations. Only by adoption and getting wide acceptance can data governance be sustained for the long term.

Tools and Technology: Self-Service Tools

There are different layers at which self-service is provided, as follows:

•     Access to data: All tools for self-service. There are different tools and technologies that can be used for providing self-service capabilities to business users.

•     BI layer: This is the layer that is used frequently by power users. Self-service capability means enabling users to provide access to data within tools so that users can create ad-hoc reports. All BI tools have a semantic layer of data for summarization purposes and the data model associated with it. Power BI, Tableau, and Cognos are few tools but not all of them.

•     Semantic layer: Providing read access to the semantic layer enables users to do ad-hoc data reporting. This solution works for end users who are business decision-makers and want to get more granular data for further analysis.

•     Data server layer: This layer of data is present in the data server. Again, this is providing access to the data server layer itself, which could be bronze, silver, or gold, or a combination of these. Because there are raw data, structured or unstructured data associations are required by data scientists and advanced users or IT business analysts.

•     Source layers layer: Access to this layer is provided to highly skilled users and developers through logical layers to explore the source level of data. This approach is used when data is not centralized when collected. Logical layer/data fabric layer is connected through multiple sources or through one-by-one sources painfully.

From a security perspective, row-level security can be built into data platforms and BI platforms.

The different layers require different data governance tools.