Whether Star Wars fans like it or not, data problems are an integral part of our galaxy.
The amount of data is constantly increasing. Yet organizations, regardless of their field of activity or size, do not realize the strategic importance of data quality.
On average, respondents believe they lose 12% of their revenue due to insufficient data quality, according to the Experian Marketing Services white paper.
Among the different data types used to increase revenues and achieve sales objectives, contact information 👤 plays an essential role: lists of prospects, leads, customers...
Their sources are numerous (company websites, call centers, sales teams), and data will only be gathered in Excel spreadsheets in a second step, with very variable quality.
Accurate and sufficient information, however, ensures real added value in the business relationship.
The effectiveness of your prospecting will be directly impacted thanks to consolidated information: from personalization of emailing campaigns to the follow-up of the customer relationship.
Shit in, shit out 💩
The use of data is becoming more and more popular as solutions flourish to exploit, harvest, or visualize them.
According to Experian Marketing Services:
Software tools can be implemented to verify structured customer information, such as email addresses, postal addresses, and mobile phone numbers. Standardized and validated information makes it easier for companies to find existing accounts (...)
When data are used correclty, they enable IT, sales, financial services, or marketing teams to make the most out of them in their professional context. All this is only possible if they are using quality data!
Indeed, the quality of the raw material 💎 is the most important when building a data strategy: it is impossible to build anything viable without solid and healthy foundations!
Reporting tools are another excellent example: they only reach their full potential if the data is clean ; hence the famous quote in data processing: "shit in, shit out".
This type of tool represents an expensive investment that will not pay off unless we use them properly. 💸
Ten years later, we often feel the same disappointment regarding big data because nothing has changed for data processing: algorithms need "clean" data to work more than ever.
Source: lespetitescases.net
A considerable waste of time ⏰
One statistic comes up regularly on the subject: "data scientists" spend almost 80% of their time selecting, cleaning and preparing data.
Regardless of the source of data inaccuracies, correcting them is an additional workload that could be eradicated.
Whatever the sector, we spend hours on repetitive tasks with no added value 😩: they take up 80% of the time spent on data processing in general.
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