Written By:
The CRM Firm
But why is it so important to keep our data fresh and vibrant?
Data, like any other asset, depreciates over time, leading to decreased adoption, inaccurate predictions, and ultimately, a decline in the value of the insights we draw from it. The longer it sits at the back of your repository, like it’s pressed to the back of your junk drawer, unused and/or unmanaged, the more likely it is to become outdated, inaccurate, and irrelevant. Studies have shown that 70% of data becomes stale after just three years, which means that if it isn’t regularly updated, reconciled, and maintained, the information you rely so heavily on may be misleading and unreliable.
What Are the Risks You Face with Stale Data
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- Decreased Usage: As data ages, its relevance diminishes, leading to reduced usage. If users find that the data isn’t reliable, they’ll be less likely to turn to your repository, creating a vicious cycle of declining adoption.
- Unused Fields: Unused fields are a clear sign of aging data. These fields could represent outdated categories or irrelevant information that no longer serves their original purpose.
- Reconciliation Challenges: When data becomes outdated, reconciling it with current data sets becomes increasingly difficult, leading to discrepancies and inaccuracies.
Information Infusion: Rejuvenating Your Data
Just as you would counteract deficiencies within your own body with an infusion, you can do the same for your data by employing strategies to ensure your data repository stays in top condition:
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- Metadata Management: Regularly review and update your metadata to ensure it accurately reflects the current state of your data, much like maintaining your body’s essential nutrients. This practice makes it easier for users to find and utilize the information they need effectively.
- Regular Audits: Implement routine audits to identify and eliminate unused fields, correct inaccuracies, and update descriptions. This process acts as a cleansing treatment, maintaining the integrity of your data and ensuring its continued relevance.
- Adoption Monitoring: Track how often your data is being used and by whom. If adoption is declining, it’s a sign that your data might be aging and in need of a refreshing boost. Use this information to make necessary updates and keep engagement high.
- Accurate Prediction: Aging data can skew predictions, leading to faulty decision-making. Infusing your repository with accurate and up-to-date data ensures you can make the most reliable forecasts.
- Preparing for AI: As artificial intelligence becomes more integrated into data analysis, the importance of fresh, accurate data cannot be overstated. Preparing your repository for AI means ensuring that your data is both current and of high quality, akin to setting a strong foundation for future growth.
- Data Analysis Tools: Utilize advanced data analysis tools that act like a health monitor, helping you keep tabs on the vitality of your data repository. These tools can alert you to potential issues before they become significant problems.
- Record Ratings: Implement a system to rate the accuracy and completeness of your records, prioritizing which data sets need attention and which are in good shape—just as you would assess your skincare routine.
- Metrics and KPIs: Establish clear metrics and KPIs for your data management efforts, allowing you to track your progress over time and adjust as needed, ensuring that your data remains fresh and ready to thrive.
The Importance of Preparing Salesforce Data for AI
Preparing Salesforce data for AI has rapidly become crucial, because AI-driven insights and automation rely heavily on the quality and structure of the underlying data. Salesforce contains a vast amount of customer information, transaction records, and business processes, which form the foundation for AI tools like Salesforce Einstein/AgentForce. These tools analyze patterns, predict outcomes, and generate insights to improve decision-making and automate routine tasks. However, if your data is incomplete, inaccurate, or poorly structured, which older data can be, it can provide misleading insights, flawed predictions, or ineffective automations. Getting your data right will help ensure that AI models function accurately and reliably.
Consistency is Key
When it comes to AI models it is all about consistency. Your Salesforce data can come from any number of sources—manual entry, integrations, third-party platforms, etc.—and can be inconsistent in format, structure, and completeness, which in turn can confuse AI learning models, leading to biased outcomes. By standardizing and cleansing this data, AI algorithms can compare apples to apples and work more efficiently to generate more actionable insights and provide you with the best possible results.
Align With Your Business Goals
Additionally, preparing your Salesforce data for AI involves aligning it with business goals and KPIs. Before implementing AI, it behooves you to define the specific objectives the organization seeks to achieve, such as improving sales forecasting, enhancing customer service, or optimizing marketing efforts. From there you align your data with these goals, making sure the relevant fields are populated and correctly linked to performance metrics. This bit of prep also involves understanding which data points are critical to the AI analysis and eliminating irrelevant or obscuring data that could distort results. When your data alignment is clean and clear, what AI puts out will be directly tied to your desired business outcomes.
Prioritize Privacy & Security
Data privacy and security are also key considerations when prepping your Salesforce data for AI. When leveraging AI, you must ensure that your data handling processes comply with GDPR and CCPA regulations, which protect customer information from unauthorized access or misuse. With that in mind, anonymizing or masking sensitive data, setting appropriate access controls, and safeguarding this information so that AI models adhere to data governance policies is essential. Failing to secure and manage data properly could lead to legal issues and damage customer trust.
Scale for the Future
Finally, all this preparation allows you to prepare for what the future holds, simplifying the next stage of technological evolution and scalability. AI models are not static; they learn and evolve as the pool of data continues to increase, which means your data pipeline will need consistent attention as it updates with high-quality information. Understand, this is NOT a one-time process, this is one that requires ongoing attention to maintain data integrity, adjustments as business needs change and pivot, and continuous refinement of the AI models with fresh, relevant data. By creating a regular SOP to address this, your organization will stay competitive and agile in an environment where AI-driven innovation is increasingly becoming a key differentiator.
Not that any reminder is required, but no one wants to grow old, including your data. By taking proactive steps to manage and revitalize your data repository, you can ensure that your data remains a valuable asset that drives accurate insights and informed decision-making. Invest in data health and be prepared for its next evolutionary stage!
