You've probably heard the old adage "Garbage In, Garbage Out or (GIGO)". Well it may be an old school and over-used expression, but you'll have to agree that its all very true. Let's face it, GIGO applies to many things beyond technology, but in the case of CRM ignoring data quality can be catastrophic.

HAF places particularly important emphasis on data quality, not only for obvious reasons that impact your enterprise sales, service, finance, and operations, but because any serious inroads into AI are going to demand data management discipline. If you hope to compete with transformative technologies embraced by your competitors, prioritizing data management has got to be near the top of your business model future state.

Maintaining good quality data is a lot easier if best practices can be put into place (as early as possible), and if data model and the standards you select can be implemented across the entire enterprise. Look, there's no denying that scraping together data from countless disparate sources can be serious grunt work, but the benefits of quality data will be realized exponentially down the road.

Here are some statistics that support the need for quality data and good information management practices (from Salesforce):

• Inaccurate or incomplete data can lead to 20% stalled productivity.
• The average company loses 12% of its revenue as a result of inaccurate data.
• Forty percent of all business initiatives fail to achieve their targeted benefits because of poor-quality data.

Here are some other delightful pitfalls that have been linked to bad quality data (from Salesforce):

• Lost revenue
• Missing or inaccurate insights
• Wasted time and resources
• Inefficiency
• Slow info retrieval
• Poor customer service
• Reputational damage
• Decreased adoption by reps
• Artificial Intelligence:
  • Unpredictable training results
  • Lack of reliability
  • Destructive or bias outcomes

HAF is currently developing AI that will address the burdensome (but essential) effort involved in data cleansing and improving data quality, and although it is still a work in progress, this technology will be an integral part of any engagement we have with your company (stay tuned!!). Our objective here is to transform customer relationship management into intelligent CRM.

Let's talk about some best practices that can be put into place before data makes its way into your CRM system. Some are simple and effective habits that will contribute to many information management efficiencies, others are more complex at an intermediate level, and some require major commitments to your business and technical model that you may not be prepared to take on in an early project phase.

• Assessing how common data points can be standardized across business departments
• Build an effective data model
• Data input standards
• Data validation rules
• Duplicates
• Data status
  • Incomplete data
  • Stale data (not to be confused with older data, but rather with out of date record information: changes in contact, address, phone, email, etc.)
  • Complex data
  • Unstructured data
  • External data
  • Data in motion
• Data security & storage
• Master Data Management (MDM)

There are some strategies that can help provide a high-level analysis of data quality if you already have a Salesforce org. Of course this doesn't address data residing in other systems or raw data awaiting importation, but it can produce a snapshot of your current CRM state.

Salesforce uses several methods to import, export or update data:

• Data Import Wizard
• Dataloader.io
• Data Export Service

Best Practices for Managing Large Data Volumes (LDV) in Salesforce

Ensuring Optimal Performance and Scalability

As organizations grow, so does the amount of data they manage. For Salesforce customers handling Large Data Volumes (LDV), it's crucial to implement best practices that ensure performance, scalability, and data integrity. At HAF Technology Solutions, we apply industry-leading practices tailored to meet the unique challenges of managing LDV in Salesforce environments.

1. Data Archiving and Purging

Managing LDV effectively requires a clear strategy for data archiving and purging. By implementing policies to archive old or infrequently accessed data, we help you maintain optimal system performance and reduce storage costs.

2. Optimizing Data Model Design

A well-structured data model is essential for managing LDV. We ensure that your Salesforce data model is optimized, using best practices like proper indexing, normalization, and efficient relationships between objects, to enhance data retrieval speed and overall system performance.

3. Leveraging Bulk Processing

For operations involving large datasets, bulk processing is more efficient than handling records individually. We configure Salesforce to utilize bulk APIs and batch processing to manage LDV efficiently, minimizing the impact on system resources and ensuring timely data processing.

4. Implementing Efficient Search and Query Practices

As data volume grows, so does the complexity of searching and querying data. We implement best practices in SOQL (Salesforce Object Query Language) and indexing to ensure that your searches and queries remain fast and effective, even as your data grows.

5. Data Governance and Quality

Ensuring data quality is paramount when dealing with LDV. We help you establish data governance practices, including regular data audits, deduplication, and validation rules, to maintain the accuracy and reliability of your Salesforce data.

6. Regular Performance Monitoring

Managing LDV effectively requires ongoing monitoring and optimization. HAF Technology Solutions provides continuous performance monitoring services to identify potential issues before they impact your system, ensuring that your Salesforce environment remains responsive and efficient.

Why Choose HAF Technology Solutions?

With our expertise in Salesforce and LDV management, we provide the strategies and tools necessary to keep your Salesforce environment performing at its best. Trust HAF Technology Solutions to help you navigate the complexities of LDV, ensuring that your data remains an asset, not a burden.

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