Data quality

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Collection of news, articles and features on all things data, from the infoboss team…

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Data-driven or jelly based decision culture?

In this post we explore these questions… Do you make decisions armed with quality data? Or do you rely on your “jellies” to inform the actions you decide to take? Are you at risk of trying to become a data-driven business and failing with all the cost and risk consequences that entails? Simply put, if…
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Data quality dimensions: Validity

In this latest post, we look at one of the DAMA six dimensions of data quality – validity. Definition: Data are valid if it conforms to the syntax (format, type, range) of its definition Measure: Comparison between the data and the metadata or documentation for the data item. Here we’re looking at whether the data…
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Data quality dimensions: Consistency

In this latest post, we look at one of the DAMA six dimensions of data quality – consistency. Definition: The absence of difference, when comparing two or more representations of a thing against a definition. Measure: Analysis of pattern and/or value frequency. Essentially we’re wanting to ensure that where this entity is recorded across two…
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Data quality dimensions: Uniqueness

In this latest post, we look at one of the DAMA six dimensions of data quality – uniqueness. Definition: No thing will be recorded more than once based upon how that thing is identified. Measure: Analysis of the number of things as assessed in the ‘real world’ compared to the number of records of things…
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Data quality dimensions: Timeliness

In this latest post, we look at one of the DAMA six dimensions of data quality – timeliness Definition: The degree to which data represent reality from the required point in time. Measure: Time difference Often this measure is used to assess the effectiveness of a process. Consider a social housing example, we may have…
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Data quality dimensions: Completeness

In this latest post, we look at one of the DAMA six dimensions of data quality – completeness. Definition: The proportion of stored data against the potential of “100% complete” Measure: A measure of the absence of blank (null or empty string) values or the presence of non-blank values. In the main, when we generally…
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Data heaven or hell?

Is your organisation in data heaven or data hell? In this post we’ll explore what characterises data heaven and data hell. If you think you’re organisation is getting too hot (i.e. too close to hell), don’t worry, we’ll provide some suggestions on how to redeem yourself with our 3 steps to heaven… Every organisation is…
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Data quality dimensions: Accuracy

There was an interesting article published recently by data quality consultants DPA on the need to assess data quality from an organisational perspective. Namely, when assessing data quality, to consider not just the data values, but also the requirements of the data within the organisation and the “data subject” i.e., the “thing” that the data…
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Who you gonna call?

To the tune of Ray Parker Jnr, song “Ghostbusters”… Infoboss! If there’s dirty data In your database Who you gonna call? Infoboss! If there’s something weird And it don’t look good Who you gonna call? Infoboss! I ain’t ‘fraid of no dirty-data I ain’t ‘fraid of no dirty-data If you’re seeing data And it don’t…
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The cost and consequences of data munging

Considering the cost and scarcity of a data scientist (or analyst) and given that almost 80% of the activities undertaken by data scientists is “munging data” (collecting, preparing and cleaning), we ask the question as to whether an investment in sustainable data quality improvement is perhaps a smarter move for your organisation? At infoboss we…
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