Get MDM buy-in with this 3-step approach
Customer data can profoundly impact a company's overall financial performance. It also affects the ability to operate efficiently, comply with regulations, and implement various growth initiatives based on customer segmentation.
In our Master Data Management 101 blog post, we explored what master data is and how it helps, as well as a brief overview regarding the downsides of not managing it. This discussion laid the groundwork for understanding the relationship between master data and critical business operations.
What role does MDM play in consolidating customer data?
At its core, MDM's pivotal role in consolidation begins with creating a centralized repository, weaving together data from multiple sources like CRM systems, ERPs, databases, and third-party tools to carve out a 'single version of truth.'
This consolidation goes beyond merely storing data; it actively refines it. MDM identifies and eliminates redundant customer data, ensuring the maintenance of unique, definitive records.
Additionally, through cross-referencing third-party databases and employing validation rules, MDM enhances the completeness and accuracy of customer profiles. This dynamic data, once consolidated, isn't left static. As the business environment evolves and customer information shifts, MDM ensures updates are synchronized across systems, thus perpetually maintaining data integrity and consistency.
The Triggers - the urgency of consolidating and managing customer data
Several events and business needs might trigger the urgency for improved customer data management. Each of these triggers is a reflection of the dynamic business environment and the increasing need for a robust data strategy:
- Regulatory changes: The introduction or alteration of data-related regulations, such as GDPR, CCPA, or HIPAA, often necessitates improvements in data management and consolidation.
- Data inconsistencies and spiraling IT costs: Frequent data inaccuracies and errors coupled with increasing IT costs associated with maintaining disparate systems highlight the need for improved data management.
- Declining marketing ROI and customer engagement KPIs: A noticeable drop in marketing ROI or challenges in achieving customer engagement targets such as NPS can serve as potent triggers to start consolidating your data.
- Security and compliance concerns: Upcoming data audits, failed compliance audits, or security incidents such as a data breach or a near-miss emphasize the urgency for improved data management.
- Operational bottlenecks and product launches: Inefficiencies leading to operational bottlenecks or introducing new products and services necessitate up-to-date information and thus consolidated, dynamic records.
- Implementation of new core systems or mergers & acquisitions: The introduction of new systems often reveals data inconsistencies. It’s challenging for an acquiring company to integrate its data with a new organization. Delivering a seamless experience may be hindered due to scattered and inconsistent customer data.
The Challenges - common roadblocks to customer data consolidation
Organizations that aren’t implementing an MDM solution often face operational and technical challenges that impede progress and pose potential risks. Here are some of the most common internal roadblocks that companies face when trying to maintain an accurate view of customer data:
- Diverse data sources and formats: With the proliferation of digital touchpoints, data pours in from various sources like CRMs, ERPs, websites, third-party vendors, and more. Ensuring harmony among diverse data structures, formats, and quality standards from these sources poses a significant challenge.
- Legacy systems and siloed databases: Many businesses, especially those with a rich operational history, grapple with outdated IT systems and siloed data storage solutions. These legacy infrastructures resist seamless integration, hindering the fluid consolidation and retrieval of data.
- Data quality and integrity concerns: Consolidating data isn't just about bringing it together; it's about ensuring that this unified data set is accurate, timely, and relevant. Bad data quality can result in misguided strategies, wrong forecasts, and misplaced business priorities.
- Manual data handling and workflow inefficiencies: In an era driven by automation, many organizations still lean heavily on manual processes for data handling. Besides being resource-intensive, these manual interventions increase the likelihood of errors, inconsistencies, and inefficiencies in the data consolidation process.
The outcomes - why you should consider implementing customer MDM
Critical data is at the heart of many pivotal business initiatives, which means having an optimized and consolidated data management landscape is a must. Here are some of the data initiatives and business outcomes that you can champion with the help of Customer MDM:
Cost-effective data management
Spiraling IT costs due to data inconsistencies can severely impact an organization's bottom line. Consider marketing software apps, many of which are licensed based on the number of contacts. Here, duplicate data doesn't just compromise quality; it directly inflates costs. MDM tackles this by eliminating redundancies. In systems charging based on processing consumption, MDM minimizes the hardware spend by ensuring datasets are lean, efficient, and free from unnecessary duplicates.
Enhanced customer experience
Having separate customer portals and unconsolidated data across product offerings leads to confused customers and terrible customer experiences. With MDM, you can easily consolidate data from multiple business units and data sources into a unified customer view. Launching unified customer portals based on accurate records can help streamline online interactions and reduce unnecessary call center costs.
Precision in marketing segmentation
Companies like banks and financial institutions that offer a diverse product portfolio can face complex challenges with marketing segmentation. Consolidating customer data from business units like vehicle, home, and life insurance via MDM can unveil valuable insights: Perhaps those with stellar loan repayment records are more likely to buy comprehensive insurance. Individuals with multiple minor vehicle accidents are interested in bundled insurance offers with favorable loan terms. This leads to targeted marketing campaigns and better bundle offerings that cater to various customer segments.
AI and ML-ready data infrastructure
Inconsistent data formats and outdated repositories can be major roadblocks when you're trying to use AI and machine learning capabilities. For example, retail companies that want to implement AI-driven personalization strategies often find themselves held back by data quality issues. MDM ensures that your customer data is consistently formatted, updated, and ready for integration into any advanced machine-learning model. By consolidating data into a clean, unified format, you're empowering your AI-driven initiatives to enhance customer experiences.
Built-in data quality with MDM
Poor data quality can skew strategies and forecasts, leading businesses down unprofitable paths. For instance, companies that primarily depend on shipping can face discrepancies in their tracking information, such as customer or business addresses, resulting in the constant misrouting of packages. This can lead to unhappy clients or partners and extra costs to delivery processes. When implementing an MDM solution, one can validate, cleanse, and continuously monitor data for discrepancies. This way, customer addresses are always up-to-date in the form of a golden record.
