There are probably a variety of strategic reasons why you have (or are considering) adopting the Salesforce platform. Common hurdles include driving effective and consistent processes, enhancing visibility across teams, and scaling your model to meet market demand. Whatever the driving concepts are, the underlying issue that touches them all is Data Management.
Getting your team to understand and think strategically about data management is key to your ability to gain insights. When armed with insights, you can begin to build a bridge to such things as predictive or big data analytics. Here are a few of the areas at a grassroots level in Salesforce that you might want to start focusing on now.
Salesforce Data Management Terms
Record IDs – Each record in Salesforce has a unique ID that is often referenced when managing data.
Data Recovery – It is important to understand that deleted records are stored in your recycle bin for 15 days.
Import Wizard – Use to import up to 50,000 leads, accounts, contacts, solutions, and custom object records per job.
Dataloader – Use for larger uploads via the Saleforce API.
Upsert – This function can allow you to maintain external ID relationships of records when uploading to Salesforce. (link to demo)
Validation Rules – Use to conditionally prevent a record from being saved unless it is in the proper format.
Lookup Filters – Filters that can be applied to relationship fields to ensure related data meets certain criteria.
Dirty Data – Inaccurate, incomplete, or erroneous data in your Salesforce platform.
Good Data Quality – This data goal is driven by having an understanding of your data, preventing poor data, and cleansing existing poor data.
Data Export – Salesforce allows you to save your data once a week.
Data.com – A service offered by Salesforce (for an additional cost) that assists you in finding new leads and cleaning existng lead and contact data.
As you start to get your team focused on data management within Salesforce, use these key areas as tools. Have open discussions with everyone on why it is important to understand your data, prevent dirty data, and clean existing data that is inaccurate, incomplete, or erroneous.
Ready to get a handle on big data?
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