Why Credibility in Data matters

Credibility is attached by others. Thus, credibility cannot be trained directly, rather its essential capabilities DQ, DG, and DC can. If all 3 capabilities are sustainably trained, experienced, and equilibrated the use of data becomes significantly improved. The contribution of each capability DQ, DG, and DC to CID depends on the business case. Read why at all you should consider the balance of DQ, DG and DC:

1. The 3 CID capabilities DQ, DG, and DC make credibility in data possible at all. While DQ and DG are equilibrial in dose and organization, DC establish mutual recognition / acknowledgement between data stakeholders and their customers.
2. CID develops robust and sustainable corporate data cultures with an agreed level of degree between DQ, DG, and DC. Digital transformations that follow the CID capabilities create solid data-driven decisions.
3. CID illuminates data as crucial component for digital transformations. Beyond Why CID responds also to the questions what and how to achieve credibility in data.
   
4. CID generates a bunch of activities and deliverables that are required to succeed in transformations to data-driven cultures.
   

Know-how: "Find an effective mix of the capabilities DQ, DG, and DC to create efficient and powerful Credibility in Data, CID"

Once WHY and WHAT have emphasized the values of DQ, DG, and DC stakeholders can cut their attention to the HOW. The HOW represents all connections between the 3 capabilities.

HOW shows in a very transparent way that DG and DC serve DQ, making DQ robust and sustainable over time. It demonstrates also that information from DG and DC is transitioned and plugged directly into the DQ Lifecycles of Principles, Work Instructions or User Journeys. This is why DQ Lifecycles are hot spot for successfully data driven decision-making. This is why DQ dominates the other capabilities DG and DC from economical perspective, but needs their input to prevent from fallacy.

What are the Sources of the CID Capabilities

Each CID capability (DQ, DG, DC) is shaped by a triple of sources and linked via Decisions D to its parent. The impact (size and weight) of the sources depends on the business case.

Data Quality is build on 3 sources

• I = Integrity
• A = Assessment
• CP = Canonical Process
  

Data Competency is build on 3 sources

• S = Syntax
• S = Semantic
• P = Pragmatics

Know-how: "Establish a trustworthy mentality that returns curiosity in data"

Commonly practiced Taxonomy of the Credibility Capabilities

The graph schemes the maze of Credibility in Data CID by taxonomy.

However, like business, CID is dynamic: While DQ and DG are quite advanced, DC remains experimental at the level of a lab. DC is quite fluid due to its nature of elaborated and evolutional experience inside corporate cultures. DC journeys are also quite different outside corporate cultures and cannot be simply transferred from one company to another. It remains a silhouette of experience for the next corporate culture.

The CID website is not dedicated to data passionists only. It mainly addresses those, who want to validate and verify real life with data.

Know-how: "Determine activities by roles & responsibilities. Categorize vocabularies by capabilities, concepts & terms."