The golden rules for Data Quality to boost overall performance

The massive digitization of economic activity has multiplied the volume of data to be collected, processed and exploited to streamline decision-making. In an increasingly Data-Driven world, poor quality data impacts the entire corporate value chain, from sales and marketing to HR and accounting. Results Time wasted, hidden costs, frustration and competitiveness at half-mast.

Towards a 100% Data-Driven BI model by 2026

Having negotiated the digital turn, the business world is now tackling the decision-making process. According to a Gartner forecast, 65% of B2B companies should complete their transition from a model based on flair and intuition to one based on a fully Data-Driven process by 2026. It has to be said that the stakes are high, as structures that fail to demonstrate an effective framework for the operationalization of Data by 2024 will be at least two years behind in terms of competitiveness. As with any transformational dynamic, this shift towards systematically Data-Driven decision-making is accompanied by a number of frictions:

• According to a joint report by Michael Page and Arktic Fox, almost half of all marketers (46%)concede major gaps in their data and analytical skills;
• According to Nielsen, access to data and analytics is "extremely difficult" for 36% of of marketers;
• According to Gartner, sales people are the employees least trained in Data. The "State of Sales Analytics" report estimates that 57 % of salespeople have to contend with well below-average Data skills. This percentage remains high for marketing teams (51%) and the accounting and finance departments (47%).

These gaps in Data skills are exacerbated by the poor quality of the data collected. As we move towards decision-making models rationalized by numbers, the poor quality of Data will become a real drag on competitiveness.

Poor data quality: decision-making biases and hidden costs

Estimating the true cost of poor Data quality is a laborious exercise, as there are many outputs:

• A waste of time (manual database cleaning, ad hoc searches);
• A under-utilization of sales resources which is already scarce and expensive, particularly in France, where there is a shortage of 200,000 sales profiles (according to Michael Page). A flawed or incomplete database will require repeated interventions by sales staff to to complete the missing fields before prospecting begins. According to HubSpot, sales reps waste an average of one hour a day on manual tasks, including database cleaning;
• A opportunity cost with data that is not 100% usable;
• A operational and strategic decisions by false, incomplete or obsolete data;
• A damaged brand imagepoor-quality data complicates targeting, and makes it impossible to to activate "customer knowledge" to personalize the to personalize the experience.

According to an MIT study, the absence of a Data Quality Management policy costs between 15% and 25% of a company's sales. And yet.., 47% of new entries on databases are still suffering critical errors (Harvard Business Review).

The golden rules of Data for sales performance

Data Enso offers you best practices to transform your Data capital into a real competitive advantage:

1. Set up dashboards to evaluate Data quality on an ongoing basis: variance and standard deviation, error rate, completion rate, data not found rate, data in expected format rate, duplicate rate, time variance, etc. ;
2. Clearly identify the internal impact of poor-quality data. Gartner recommends listing the problems associated with Data Quality and their impact on sales, sales and marketing productivity, etc. This preliminary step makes it possible to define the stakeholders involved in a possible Data strategy, to make them aware of the impact of poor data quality on performance, and to release the necessary budgets from top management;
3. Identify the "customer" impact of poor-quality data. In an e-mail campaign based on a database full of duplicates, you'll send the same e-mail several times to the same person, which will annoy them;
4. Investigate the causes of poor data quality: unreliable sources, lack of reconciliation between several resources, human errors, lack of communication between company departments, etc. ;
5. Defining what is meant by "quality" data. Each business sector may have different standards in this area. It is therefore important to define the expected quality objective beforehand, to avoid over-investing;
6. Accept that prospect files have an obsolescence rate obsolescence rate of 25 to 30% per year and act accordingly (change of job, change of employer, retirement, etc.);
7. Start by sanitizing the existing system using batch treatments (curative);
8. Complete your technological stack with a Data Quality Management tool capable of both curative and preventive action.

Data Enso, for revenue-generating Data

Build reliable Data capital, save time, eliminate hidden costs, boost ROI for sales and marketing teams and streamline decision-making. That's all you risk when you entrust your Data challenges to Data Enso! Our simple, 100% RGPD-compliant solutions enable you to activate the full power of quality data to serve customer relations, sales actions, marketing campaigns, the HR department and accounting. Test us !