In B2B, data quality is crucial to a company's competitiveness and success. Unlike B2C, where direct customer interaction facilitates data collection, B2B involves longer sales cycles and more complex interactions with several intermediate systems and databases. This article explores the proactive approach to data management, and explains why a proactive strategy is essential to avoid the loss of critical information.
Reactive data management: have you fallen into the trap?
Companies that adopt a reactive approach to data management often react to needs and changes as they arise. They are therefore subject to the vagaries of change.
On the one hand, data loss is common, as information can get lost between the different phases of the sales cycle if it is not properly captured and documented from the outset. This can have an impact on a company's productivity, as well as its reputation with customers.
On the other hand, the absence of clear standards and established upstream processes for data collection can lead to inconsistencies and delays in decision-making, affecting the company's overall competitiveness.
Why is a proactive strategy the key to B2B competitiveness?
Conversely, adopting a proactive approach means not only anticipating future needs, but also putting in place robust systems for collecting, storing and analyzing data from the outset. Indeed, such a methodology is essential for maintaining data integrity and consistency throughout the sales cycle. What's more, by investing in technological tools such as advanced CRM, companies can not only improve customer relationship management, but also use accurate data to make informed strategic decisions. In addition, continuous monitoring and regular auditing of data enable anomalies to be identified and processes to be constantly optimized.
Microsoft case study: a proactive data governance strategy
Microsoft identified that its data was fragmented across silos, making its use inefficient and increasing the risk of non-compliance. The company wanted not only to improve data quality management, but also to facilitate the democratization of data across the organization, while meeting compliance and regulatory requirements.
Microsoft has therefore adopted a proactive approach to data governance based on two main pillars:
- Data standards integrated into engineering processes: The company has developed clear data standards, which it has integrated into its development processes. This automates and proactively manages data governance issues.
- Centralization of data in an Enterprise Data Lake (EDL): Microsoft has moved data from different sources to an Azure-based EDL, creating a single, reliable source. The EDL enables data to be analyzed centrally and uniform governance controls to be applied.
These measures enabled Microsoft to proactively scan data for quality, security and compliance issues.
Thanks to this proactive approach, Microsoft has been able to reduce data duplication, improve the efficiency of data access and use, and build greater confidence in data quality and compliance across the organization. Implementing this strategy has also helped the company automate the measurement and monitoring of data compliance, which has helped strengthen Microsoft's data governance posture.
How do you move from reactive to proactive management?
To move from a reactive to a proactive approach to data management, it is essential to develop a common understanding of data-related tasks and their strategic impact on the business. This requires a data governance body bringing together all stakeholders, including project managers, business teams and data managers. This body must foster regular communication on priorities, document key projects, explore the motivations behind data requests, and ensure transparency on the time required to complete tasks. For example, project managers can define objectives and deadlines, while business teams can provide feedback on data use. What's more, embedding a data team member within business teams helps to better understand specific data needs, and to provide tools tailored to their requirements.
Strategies for proactive data management
In order to move to proactive data management in B2B, several strategies are recommended:
- Training and awareness: It is crucial to educate staff on the importance of data quality, and on best practices for data collection and management.
- Use of advanced technological tools: The integration of powerful CRM and data management systems makes it possible to centralize and secure information.
- Establishing standardized processes: Developing clear protocols for data collection, verification and updating is essential to ensure consistent quality.
- Regular monitoring and auditing: Implementing data governance that encompasses robust mechanisms for continuously monitoring data quality, identifying gaps and taking proactive corrective action is essential.
In conclusion, adopting proactive data management is essential in B2B to maximize operational and strategic efficiency. By investing in practices and tools that promote effective data collection and use, companies can improve their competitiveness, optimize their decision-making processes and respond effectively to the complex needs of their customers.
It makes sense to combine the two approaches, because we can't rely on proactivity alone; there are a lot of uncertainties and mistakes out there. Proactivity will enable companies to detect anomalies before they become a major problem, while reactivity helps to avoid a repetition of the same problem.
By Johny Makhlouf
