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    Readiness of Data Pipelines

    Readiness of Data Pipelines

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    Observatory
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    Data maturity
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    Astronauts
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    Readiness of business data
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    Data driven level business models
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    Data driven level operational models
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    Data driven level human resources
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    Data driven initiative pipelines
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    Value of launched initiatives
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    Readiness of information factory
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    Readiness of data pipelines
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    Benchmark

    Data Pipelines source the raw data internally and externally for the Information Factory to build the Information Bricks requested by the business domains. The maturity of the Data Pipelines thereby needs to be assessed in regards to its ability to deliver on the requested Information Bricks (e.g., not assessing data quality in an abstract way, but in light of which aspects of the data need which quality for the specific business purpose). Thereby, the Data Pipelines are assessed along 4 criteria: 1) The quality of the data, 2) the compatibility of the data, 3) to which degree data owners assume their responsibility, and 4) to which degree business KPIs are standardized across the organisation to speak the same language.

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    Quality of Data

    :: Assessing the quality of data in light of the requirements of the Information Bricks requested by business domains.

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    Why care about this?

    Data quality is often misunderstood in companies and improved in general instead in regards to the business objectives. Assessing the data quality in regards to the requirements of the Information Bricks requested by the business domains allows for a more lean and value focused discussion about data quality.

    21% as data quality score is rather low. There is much scepticism from the business side that the data has sufficient quality to be leveraged.

    Data Quality Score

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    Compatibility of Data

    :: Assessing to which degree the same data from various domain areas are compatible to be leveraged together.

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    Why care about this?

    Unifying data from various sources is critical to achieve speed and scalability of Information Factories.

    64% as data compatibility score shows an area that could become a strength within X.

    Data Compatibility Score

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    Ownership of Data

    :: Assessing to which degree internal data owners managing their data responsibly.

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    Why care about this?

    Internal data is owned by specific business domains. They need to understand their role as data suppliers to the Information Factory and treat their data accordingly to make it useful to the rest of the organisation. Always in regards of the requested Information Bricks.

    40% as data ownership score sends an alarming signal that people owning data do not treat data with sufficient care to make it accessible to others.

    Data Ownership Score

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    Standardisation of KPIs

    :: Assessing to which degree KPIs are standardised across the organisation.

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    Why care about this?

    Data-driven value is specified by business domains. Business domains optimise to meet KPIs. Ensuring that the KPIs are standardised across the organisation allows to systematically increase the compatibility of the data that is produced and captured across the organisation. Standardisation furthermore allows for alignments of interests for data owners fostering the exchange of best practices and facilitates synergies.

    41% as KPI standardisation score raises the need to align on how to steer the company in order to harmonise approaches and value logics leading to a higher compatibility of use case approaches and framing of data.

    KPI Standardisation Score

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