According to Gartner, poor-quality Data costs companies $12.9 million every year... a hidden cost that will continue to grow as companies continue their transition from an intuition-based decision-making model to a 100% data-driven process. How can you make the transition and turn your Data capital into a decisive competitive advantage?Â
Poor-quality data: cascading consequences on decision-making and operational costs
Over and above the direct impact on revenues, poor-quality data leads to complex Data ecosystems, which will eventually require a complete overhaul to put the company's digital transformation back on a sound footing. Over time, the decision-making process loses relevance, with a direct impact on the company's competitiveness and overall performance.
According to Gartner, companies that have not succeeded in integrating Data into their operational decisions by 2026 will be two years behind their market, and decision-makers are well aware of this. In fact, 70% of companies surveyed are already implementing some form of Data Tracking to reduce operational costs.
But in the short term, decision-makers will need to adopt a systematic "Data & Analytics" mindset across the entire Sales and Marketing value chain, as Melody Chien, Senior Director Analyst at Gartner, explains: " Good quality data helps to generate better leads, better understand target expectations and improve customer relations: it's therefore the ultimate competitive advantage that Data & Analytics players need to constantly improve "... provided that this data is of high quality.
The checklist for data-driven decision-making
In a publication co-constructed with Data Intelligence experts, Gartner offers a 12-point checklist to boost Data Quality and turn your data assets into a vector for relevant decisions, even in a turbulent macro-environment. Summary:
1. laying the foundations for Data Quality Management
- Clearly identify the impact of better quality data on business decisions and performance. This is an essential prerequisite for getting employees on board with the project.
- Defining standards for "quality" data Standards that are too restrictive will result in insufficient, and therefore insignificant, data pools. Standards that are too lax will impact data quality.
- Generalize Data Quality standards to all company departments. This is the first step towards integrating data into the entire BI value chain.
2. Define a Data Quality policy, deploy it and test it in the field
- Systematize continuous data profiling. This involves examining data to extract actionable summaries as soon as it is collected. Read more about this in our article on Smart Data.
- Design and deploy Data Quality dashboards. Objective: monitor KPIs on critical data and, more broadly, put Data Management into practice.
- Moving from a truth model to a trust model. In short, this means accepting and anticipating a certain lack of reliability in data collected from external sources. Rather than eliminating them by virtue of quality standards decided upstream, the company should instead subject them to the laws of probability to decide whether or not to exploit them.
3. Designate Data Quality responsibilities
- Integrate Data Quality Management into top management meetings to link data quality to business results.
- Establish responsibilities for Data Quality and define processes to be followed in the event of malfunction.
- Companies with several Business Units (BUs) need to havea Data Manager for each BU.
4 Making Data Quality part of our corporate culture
- Identify best Data practices and generalize them as the company's data management maturity evolves.
- Regularly inform business departments of the positive impact of improved data quality, to consolidate their support and interest in the collective effort.
- Create synergies with Data specialists from other companies to share best practices.
The three pillars of a (good) data-driven decision
In an environment of constant uncertainty and turbulence, decision-making has never been more complex. In fact, Gartner has found that 65% of decision-makers have to deal with more choices and involve more collaborators to reach a decision. " The current state of decision-making is no longer sustainable ", concludes Gartner.
To revise the paradigm and face up to uncertainty, the consultancy has identified the three pillars of the "right decision":
- Connected. No decision can be taken in isolation, since it necessarily impacts all the players in the company, and even in the ecosystem. That's why decision-making must be collegial, not only in terms of hierarchy, but also in terms of competence. This is what Gartner calls "network decision-making", transcending organizational boundaries.
- Contextual. The options on the table must be analyzed in the light of the context, beyond the one-off event or individual transaction.
- Continue. Decision-making is a continuous process that doesn't stop with the choice of an option at "M" moment. The emergence of an opportunity or a threat should prompt decision-makers to fine-tune or reconsider the decision as quickly as possible.
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Data Enso helps you make the transition to Data Driven decision-making
On average, between 15% and 25% of corporate databases are unusable. The cause: erroneous, incomplete, obsolete and duplicated data resulting from months, years or even decades of poor data management.
