WHY DC
– the definition
Like DQ and DG, Data Competencies DC contribute significantly to credibility in data.
Like the utilitarian ideal of a homo oeconomicus, where preferences determine the utility of a good, DQ and DG shape the conception of a homo data. DCs however go beyond the homo data - they also consider and integrate the human bias beyond linear reasoning of data citizen behaviour. This is why competencies have stabilizing impact on a promissing and solid communication.
Competencies equilibrate transparency and authenticity. Transparency and authenticity induce commitment. Competency is extension of its sceleton competence. Moreover, competencies are a response to speech acts. Speech acts will be introduced in What is DC.
Competencies, like any other mental process, make use of attributional processes about some-one and some-thing.
Good competencies contribute significantly to cooperation with reduced mutual verifications and reimbursed credibility between data citizens.
WHY DC
- transparency
Visualizations
about data are less invasive than words. Visualizations are pointing at something instead saying something.
Wittgenstein even includes rule-based compositions of signs to words and words to sentences to (less mental, rather logic) visualizations of objects. To Wittgenstein visualizations share the same logic form of what someone wants to describe.
Visualizations activate powers of representation / pictures. Visualizations are proposals and stimulus of narratives (nudging). They can turn monotony to curiosity. They most popular visualizations shape data to a form of narratives that have crisp meanings and high density of information (semantics). Visualizations harmonize data, giving data a face, even personality, but still giving the freedom to contemplate at something with a certain degree of sensualization. Thus, shapes that are visualized, acknowledge powers of representation.
In practice data visualization processes can produce logically conditioned pictures:
- Data Analysis graphs (drill-in / drill-down)
- Data Synthesis graphs (aboutness with meta data)
- Organizational Excellence
- Data Marketing
- Data Journalism (epics, user stories)
Speech acts can enrich and extend the logic form information with advanced transparency intentions using 3 extrapolation techniques:
- Data Arts
- Infotainment (storytellings, fables, satire)
- Campaign Mgmt.
Visualization methods represent competence, means logic data visualization techniques that follow pure intellect. Speech acts enrich logic data visualizations with competencies, means mental decorations, that follow human intelligence to tell a story about the logically built data shapes.
Speech acts do not neccessarily point at something, they rathter say something.
The interpretation of Wittgenstein's tenet is based on the Tractatus and the Philosophical Investigations. His writings are heavily used in NLP area. But the interpretation of his tenet is customized to technicians' needs. Wittgenstein himself would disagree with the systematic presentation of his ideas.
Wittgenstein would insist on the philosophical understanding that the meaning of a word or a sentence lies in its use in the language. One cannot guess how a word functions. One has to look at its use, and learn from that. Even grammar is a product of words' use, as it is acknowledgement of use and agreement on rules. Therefore Wittgenstein states Our language can be seen as an ancient city: a maze of little streets and squares, of old and new houses, and of houses with additions from various periods . . . .
Consequently you have to walk up the city, to use its equipment, to talk to its population, etc. to understand the meaning of the city.
WHY DC
- authenticity
Data becomes credible when its owner exudes authenticity. Authenticity is attributed by others. Authenticity is an offer for a trustful conversation.
Someone can achieve authenticity with advanced self-confidence (external image) and a corresponding self-awareness (self-knowledge, self-esteem, resilience, self-image). The balance of self-confidence and self-awareness is crucial for authenticity and a robust relationship. However, complementarily, self-confidence is significantly impacted by self-awareness, means self-awareness is an endogenous variable of self-confidence and pays significantly into self-confidence.
Data becomes credible when data citizens / owners are perceived authentic, justifing data with self-confidence. The main source of the self-confidence is mainly rooted in self-awareness.
Good authenticity needs a good balance and a best fitting dose of self-confidence and self-awareness.
Authentic data citizens are dignified as trustful carrier of information.
While self-confidence is hard to train, there are a lot of well-proven techniques to improve self-awareness.
The form of justification is essential for a positive external image. Therefore a robust self-awareness becomes key.
WHY DC
- business acumen
Portfolio, components, the creation of the components, core capabilities, and engagement of competencies are crucial to develop business acumen.
- Portfolio --> What are the root pillars that need to be tackled to develop competencies?
- Components --> What are the keys to unlock access to other people?
- Creation --> What is the form of components / keys (solution) to deliver capabilities?
- Capabilities --> Which skills need to be deployed to approach business acumen with acknowledged data competencies?
- Engagement --> Which are the components to maintain the competencies?
Competencies have vital impact on business, but especially on the portfolio of culture and practice.
