The importance of data quality in an insights driven economy
The insight-driven economy
In today’s information-driven economy, businesses are increasingly demanding trustworthy data. Every business manages and collects data, however, it is businesses which can access and leverage quality information that will have a true competitive advantage in the years ahead. Better data quality helps fuel innovation and improves customer experiences by allowing brands to create more intimate, meaningful relationships with their customers.
95% of C-Level executives believe that data is an integral part of forming their business strategy—a sentiment that has grown by 15 per cent over the prior year, however, 84% of CEOs were found to be concerned about the quality of the data they base their decisions on. And it’s no small concern – according to Experian “dirty data” directly impacts 88% of companies, with the average company losing 12% of revenue per year.
Recently quality of data in the marketplace has come into focus, with worrying reports that the accuracy of available 3rd party data is hardly better than chance. With marketers and C level executives relying on this data for crucial business decisions, poor accuracy can have a major impact on businesses and consumers alike.
Impact of poor quality data
The impact of poor quality data can be immense – according to Gartner’s Data Quality Market Survey, the average financial impact of poor data quality on organizations is $9.7 million per year. This impact will only increase as companies become more increasingly dependent on data and information environments become progressively complex. Gartner reported that in 2017 alone, the average financial impact of poor quality data increased to $15 million, ultimately costing the US economy more than $3 trillion.
However these costs are not wholly financial: poor quality data can see an increase in churn and a loss in reputation due to irrelevant brand experiences, missed opportunities and higher-risk decision making.
On the other hand, innovative brands such as Airbnb, Amazon and Netflix, rely on proprietary first-party data to truly understand and serve their customers. “Good quality data empowers business insights and starts new business models in every industry. It allows enterprises to generate revenue by trading data as a valuable asset.”
Poor data quality in the 3rd party marketplace
DMPs and 3rd party data providers have long been promising marketers the equivalent of the holy grail – accurate consumer data, at scale. However, recent studies have found this data to be wildly inaccurate.
Deloitte’s 2017 survey on the prevalence of bad data being sold to marketers found that only 29% of consumers found their data to be at least 50% accurate or better, with one-third of respondents finding that the information gathered on them was between 0 – 25% correct.
Most worryingly it was found that “the data for nearly half of the variables examined was only 50 per cent or less likely to be accurate—equivalent to the accuracy obtained by tossing a coin.”
With marketers and businesses relying on mere chance when making decisions and contacting their customers, the relationship between customer and brand is at risk.
A new standard for data quality
This is why Pixoneye constantly strives to create high quality, accurate data that exceeds the industry standards in terms of both quality and accuracy. Pixoneye does this through:
- Constantly testing and improving the accuracy of our machine-learning algorithms by using ground-truth data collected from first-class global survey partners.
- Ensuring that each new user characteristic that Pixoneye provides is at least 80% accurate before being offered to clients. Thereafter, the accuracy will be further improved.
- Continuously updating our understanding of each individual user by analysing new smartphone data as soon as it exists on their device. Because of this, our data is never out of date.
- Providing complete transparency about data accuracy. Unlike other providers, we know how accurate our data is, and are more than happy to share that information with our clients. Get in touch for more information on the quality of our characterisitics.
- Ensuring that our algorithm works in every cultural context by building training datasets from consumers all over the world.
- Prioritising accuracy over reach. In technical terms, we prioritise specificity over sensitivity. Because most user datasets are built for PPC advertising purposes, the vendors try to include as many people in their segments as possible. This makes the datasets almost useless for increasing relevance for your own audience because they contain far too many false positives. A telling example is that, in such datasets, you typically find that around half of users are identified as both male and female. Pixoneye was built for communicating with a brand’s own users, and as such provides very few false positives. Our gender characteristic, for example, gives only one gender to each user and is correct 86% of the time.
As the reliance on data increases, it is vital the data that businesses rely on is trustworthy and accurate. Being data-driven can unlock many competitive advantages, but poor quality data can hinder and even damage a brand. To ensure maximum ROI and impact from their data, businesses should aim to understand more about the data they rely on.
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