Changing the Way We Look at Data

Salesforce Data Cloud Preparation Tools and Resources

Get an overview of the Salesforce^® Data Cloud, learn why it’s important to plan ahead before implementing it, and get resources for preparing your data. This guide focuses on the importance of data quality for business success, especially for small and medium-sized businesses (SMBs). We’ll cover some of the challenges businesses face with their data, and provide solutions to maintaining data quality.

Wayne Dyer once said, “When you change the way you look at things, the things you look at change.” What happens when we change the way we look at data?

Data is the key to maximizing Salesforce, identifying new opportunities and growth. But there are obstacles that get in the way. To begin, data quality is constantly declining, data becomes outdated, people change jobs – it’s a continuous cycle that wastes time and impacts revenue. The Salesforce report “Small & Medium Business Trends, 2024” found:

• 46% of SMB leaders feel overwhelmed by too many business tools
• 84% of SMB leaders say complete and accurate data is increasingly important for business success
• 80% of SMB leaders say improving their company’s data quality would increase revenue

As Forrester explains, “Insight is the engine of today’s businesses. The value of a company can be measured by the performance of its data. As a manager you have to make decisions based on data every day; so, the one thing you want to know is if you can trust the data, as increasingly more is created every day.”

It can be overwhelming. Salesforce says the average enterprise uses 1,000+ applications and the data they collect is in silos. The result is that a company can have 1,000+ variations of a customer and their interactions. “You’ll hear customers say that they have all of their data in Snowflake, AWS, or Databricks. But no data warehouse, data lake, or data lakehouse looks the same—which means that the structure of that data and the connectivity of that data to the customer experience is different too,” adds the Salesforce Data Cloud Trailhead.

Is Moving All Your Salesforce Data to Data Cloud the Solution? 

“Data Cloud allows you to unify all your data on Salesforce without building complex data pipelines, easily take action on all your data across every Salesforce Cloud, and enable trusted AI solutions powered by your data,” explains Salesforce. Sounds great, doesn’t it?

Spoiler alert: Data Cloud doesn’t clean your data for you. And that’s why a successful Data Cloud project requires preparation before you get started, and before importing data into AI or Agentforce. After all, these types of solutions are amplifiers of the data quality.

It’s important to understand Data Cloud’s structure so you can prepare your data before you start implementing. This will prevent unnecessary costs and allow you to leverage clean and accurate data that give you actionable insights.

Data Cloud breaks down silos by bringing data together from other sources like CRM systems, customer service databases, and third-party applications. But the Data Cloud adoption process is complicated, and the pricing is based on consumption credits, which can lead to increased costs. 

What Are Data Cloud Credits? 

Simply, they are the digital currency that you use to pay for Data Cloud services. Since Data Cloud pricing is consumption-based, you’ll only spend as many credits as the services you use. For example, if your company is smaller, then it won’t use as many credits as a bigger company with numerous complex datasets.

To keep costs under control, the best practice is to set up usage guidelines. “Let’s say you dive in, you create a Data Space, set up one or more Data Streams, create the relevant DLOs and fields, then map your DLO fields to DMO fields, and then discover that your segmentation is wrong and you are not getting any records. You then need to unpick all of this and start again. It is not only time-consuming, it wastes credits, and real money,” explains Cloud Elements.

We Can Help You Prepare, Organize and Connect Your Data

Do you feel like you need more Admin help? You’re not alone. A recent Salesforce Ben survey found that data quality ranks fourth in terms of challenges Admins face. First is technical debt, then integrations and balancing out-of-the-box features. 

We get it, and we’re here to help. In addition to overwhelming amounts of new data, old data in your Salesforce org can make it difficult for users to find the information they need. 

Tools like our Data Quality Helper app can help. Data Quality Helper ensures that your Salesforce users are entering or updating complete, valid, non-duplicative, and current data. This proactive approach is a great strategy for reducing or eliminating time spent on fixing and cleansing data later. To learn more, read Prevent Bad Data Creation and Find and Clean Up Existing Data and Data Storage Management Is a Key Component of Elevating Data Quality.

Another way customers are improving their data and getting more insights is by using Lookup Helper to automatically relate Salesforce records. Lookup Helper can also auto-populate lookups using record matching rules, and group data by time period, geography, or any other category.

Rollup Helper elevates reporting with cross object rollups, and custom filters allow you to rollup data to a centralized object. For example, Rollup Helper gives you the ability to identify Names of Child Accounts on Parent Account or find information on the Number of Contacts without an Email Address.

Whether your data is in Salesforce or Excel, data in multiple locations needs to be refined, then accurate across the board and maintained. “We can create custom Visualforce pages — command centers for employees or analytics components — that allow you to see your information in a coherent manner,” said Mitchell Machor, Passage Technology’s Development Services owner.

Related Links & Resources

Getting Your Data in Order

Recommendations for preparing your data from the Salesforce Data Cloud include analyzing your current data, reviewing the Trailhead module Data Quality, as well as reviewing your data sources. To download the checklist, go to Getting Started Using Data Cloud.

Data Strategy & Governance

Tips for Executing an Effective Data Quality Strategy – The First Step for a Data Quality Strategy: Conduct an Initial Audit. The results of a properly conducted data quality audit will inform you of your needs as well as guide your priorities and next steps.

Building and Implementing a Sound Data Governance Program – Admins play a key role in establishing the governance structure, but it’s the users who are best positioned to elevate the data itself.

Compliant Data

GDPR and Using the Salesforce Individual Object – When a customer no longer wants you to retain data about them, or when it’s no longer necessary to keep data, data protection and privacy regulations can require you to delete the customer's personal data. 

Duplicate Data

Got a Duplicate Data Problem? Data Quality Helper is the Solution – Find out how you can create a process for duplicate resolution and ensure higher data quality going forward. 

Quality Data

Why Your New AI Project is Actually a Data Project – Before your organization can experience the benefits of AI, you need to ensure that existing data assets are properly managed. 

Instituting a Uniform, Repeatable Process for New Salesforce Record Creation – When many different users create the records stored in the Salesforce platform, it creates the potential for wide variances of information, and makes it difficult to find actionable insights.

Maximizing Salesforce and AI in Your Business – Outputs from both Salesforce and AI are dependent on inputs—the quality of data being analyzed. Does your company have a data optimization process that leverages all the stakeholders?

Salesforce Admin Career Growth Requires a Commitment to Elevating Data Quality – Admins play an important role in transitioning their companies to better use of AI and it starts with data quality, but first the root causes for poor data quality need to be identified.

True Effectiveness with Salesforce Agentforce Requires Elevating Data Quality First – The first step in Agentforce implementation is improving data and records across the platform—before AI begins its work. 

Connected Data

Transforming Data: Leveraging Connectivity for Innovation – When your organization’s data is connected, this can open up new opportunities. Data that’s connected and working for you can lead to increased efficiency, help you innovate and build stronger relationships. 

Before introducing your data to the Data Cloud and AI, the first step is to clean it, improve the quality, then make sure it stays in sync via automation or notification. Reach out to us to learn which analytics tools you should be building with and let us save you time and money.