This article is written by Mariam Oyebanji, Consultant at VFP. Discover her insights on the importance of implementing guidelines and strategically utilizing tools to ensure that companies maintain high-quality data standards. By doing so, organizations can enhance their decision-making processes and foster business expansion. making and business expansion.
Summary of the article:
Successful data management relies on using the tools and establishing guidelines, for organizing data effectively to meet business objectives. By giving importance to data and setting criteria for identifying redundant information companies can improve the quality of their data streamline their processes and make well informed decisions.
Data quality is paramount in the age of information, and maintaining it requires a clear framework supported by the right tools. With well-defined rules and a strategic approach to data classification, organizations can improve data hygiene, streamline operations, and focus on what truly matters for business outcomes. Below are essential tips and best practices for organizations aiming to streamline their data management processes, improve data accuracy, and ensure the reliability of their data.
Define what constitutes as “Old” or “Duplicate” data
The tools available will only be effective if they’re aligned with clear, organization-specific rules on what counts as “Old” or “Duplicate” data. By defining what constitutes “Old” or “Duplicate” data, organizations can configure tools to follow these guidelines, saving time and avoiding manual clean-up tasks.
- Define “Old” Data: Old data typically refers to records that are no longer relevant or necessary for current operations. Organizations should consider the type of data and set time-based rules (e.g., sales data older than five years) or relevance-based criteria (e.g., customer accounts inactive for more than two years). This helps focus on data that is current and useful, archiving or removing what’s no longer relevant.
- Define “Duplicate” Data: Define matching criteria for what constitutes a duplicate. Salesforce offers native duplicate management features within its platform, allowing administrators to configure rules, match data, and merge duplicates. Establish rules for merging duplicates to ensure that only one accurate, complete record remains.
Leveraging Existing Tools to Maximize Efficiency
The Good and the Bad of Tool Variety: With so many data management tools available on AppExchange, there’s no shortage of options for businesses looking to streamline their data quality processes. The good news? These tools are powerful, versatile, and often specialized in tasks like duplicate management or archiving old data. The bad news? It can be overwhelming to know which one is the best fit, and without a strategic approach, organizations may not maximize the value of these tools.
Prioritizing Data Hygiene Based on Business KPIs
Focus on high-priority data – For data hygiene to be truly effective, it needs to be aligned with the broader strategic goals of the business. This approach ensures that data hygiene efforts align with the business’s values.
Businesses can classify data as follows:
- Required: This data is essential and needs to be complete for 100% of records. Missing required data can impact regulatory compliance, customer insights, or core operations, so this data should always be prioritized.
- Preferred: Data often requested by management but not essential for operations or KPIs.
- Conditional: This data is essential only when certain conditions are met. For example, “Certain fields” may be required only for billable projects. Different validations can be configured to require this data only in specified situations, reducing unnecessary data entry while ensuring completeness when needed.
- Optional: This is data that, while collected, is not essential for KPIs or operations. Optional data can be deprioritized for hygiene efforts, as it may be more about managing “tech debt” than maintaining data quality.
Organizations can streamline data management efforts by classifying data into these categories, ensuring that time and resources are focused on high-impact areas that align with business goals.
In conclusion, effective data management is not just about choosing the right tools; it’s about aligning these tools with clear rules, business priorities, and a structured classification system. By leveraging available tools wisely, defining clear data management rules, and prioritizing data hygiene efforts based on business KPIs, organizations can maintain high data quality while minimizing the time and resources spent on cleanup.
This approach not only improves data integrity but also ensures that data remains a valuable asset, supporting better business decisions and fostering efficient, data-driven growth.