Measurable Data Quality
- 5 tips for transparency instead of gut feeling
How good do you think your data actually is? All right? Average? Rather modest? There's often nothing more than a vague gut feeling when it comes to the quality of your own data. So how do you make it measurable?
Companies collect vast amounts of data in a variety of ways. The challenge? This data often lies unused and unnoticed, often fragmented, incomplete and sometimes over a very long period of time until it is remembered again. In this way, huge collections of data are created. If you want to analyze the data or use it for sales and marketing campaigns, for example, the term "data quality" quickly comes into play. Then it is often said: "The data feels okay!", "We believe that our data is good!" or "So far, the data has fit quite well."
What these statements have in common is the vague gut feeling that the data will be okay. But there is no clarity, there is a lack of transparency and answers to questions such as:
- How complete is the data?
- How high is the proportion of duplicate and/or multiple data?
- How up-to-date is the data?
- Can I draw analytical results from the data?
- Can I segment the data cleanly?
78% of companies work with outdated data.
Studies repeatedly show that data sets generally have certain deficiencies. Incompleteness (88 percent) and duplicates (82 percent) are named most frequently. This is closely followed by the fact that data also ages (78 percent).
Read our whitepaper to learn how to counteract these shortcomings and gain more transparency about the state of your data.
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"Measurable data quality - 5 tips for
transparency instead of gut feeling"!