Dive Deep into Salesforce CRM Analytics and Data Quality

How Does Salesforce Help Businesses Get The Most Out Of Data?

A Customer Relationship Management (CRM) platform like Salesforce goes beyond just helping businesses manage, organize, automate, and synchronize sales, marketing, and customer support activities. With the underlying thoughts that modern business users are always connected and mobile, Salesforce enables customer-facing agents and business users to utilize data effectively from any device—desktop, tablet, smartphone or even smart watch. It’s the combination of data-driven insight, accessibility, and mobility.

The business users gain data-based insights from two powerful solutions—Reports and Dashboards and Wave Analytics.

Reports And Dashboards

Reports and Dashboards generate operational reports on 90-day historical Salesforce data. It empowers business users with:

  • Basic daily business metrics in real-time such as track daily sales activities, check leaderboard, identify high-priority cases, sales team performance, volume of qualified leads, conversion rate, etc.
  • Customized reports for real-time view.

Wave Analytics

An integration of Salesforce CRM with legacy ERP modules, programs, and applications help businesses maximize the benefits of enjoying a 360° view of the customer. The data lying idle in legacy applications and programs can be integrated and converted into a valuable and efficient resource for the business.

Unlike Reports and Dashboards, Wave Analytics is an analytics platform that connects to any type of data—structured or semi-structured—from any data source. It enables business users to view interactive visualizations and trending and to ask new questions such as:

  • How often does a customer log on to the website?
  • Why do customers prefer shopping through mobile apps to the online store?
  • Is there a correlation between an email marketing campaign and increase sales?
  • Can a premium pricing strategy be sustained through value-added services like a simpler ordering process, faster deliveries, and quicker refunds?

What Good Is All Of That Information When You Have Dirty Data?

The following are important aspects of the data quality that help you define good data quality standards within the organization.


The data must follow a consistent and standardized format in all applications, programs, modules and platforms. From dates to addresses, the parameters must be implemented in a consistent manner irrespective of its source or target program.


A direct mail campaign that does not reach the intended recipients due to the absence of zip codes can lead to significant wastage of resources and loss of opportunities. Incomplete data will—sooner or later—lead to issues and complications in executing operational plans.


Even an error like addressing a lead or prospect by the wrong job title can affect your brand’s credibility. Data imported from legacy systems must be verified for accuracy before being incorporated into the Salesforce platform.


Merely relying on internal data will not help your business grow. The organization should assimilate external data in a sustained manner to gain greater insights on leads and customers. However, it is important to verify external data to avoid quality issues.


Once external data has been verified, it is important to ensure it matches the standard and uniform parameters applied throughout the organization. Standardization is essential to ensure managers and employees don’t waste time and effort modifying data, and can simply focus on deriving logical conclusions in an efficient manner.

Sustaining Data Quality

This is a very important aspect of data quality that, if ignored, can make it impossible for any business to benefit from Salesforce (or any CRM) deployment. Policies, processes, and standards must be devised to ensure the quality of the data is maintained at all times. A one-time clean up will end up as a waste of time and money. Managers must understand the necessity of sustained emphasis on all aspects of data quality.

Data Management Tools For Salesforce

Data management tools help businesses manage data more efficiently. There are many options available depending on the complexity of your data management requirements.

Import Wizard

Salesforce provides wizards to import and update data into both standard and custom objects. Import Wizard is an easy-to-use online tool. Salesforce admins can import Account, Contact, Leads, Solutions, and Custom Objects up to 50,000 records at the time while other users can import their own Accounts and Contacts up to 500 records at the time. Import Wizard is a good data management tool as it always checks the deduplication and provides an option to trigger workflow rules.

Data Loader

Data Loader—previously known as “AppExchange Data Loader“ and “Sforce Data Loader”—is a powerful client application for update, upsert, delete, or export data up to 5 million records. Data Loader is a native Salesforce app that allows loading all kinds of objects. The current version is version 35.0, winter ’16–available in both Salesforce Classic and Lightning Experience for Enterprise, Performance, Unlimited, Developer, and Database.com. Unlike Import Wizard, Data Loader doesn’t deduplicate records and it requires to turn off validations and trigger workflow rules.

If there are over 5 million records to load, Salesforce recommends working with a Salesforce partner or using one of the apps from AppExchange. Depending on your data management and data quality needs, there are many good tools available through AppExchange such as Data Loader for Salesforce by Mulesoft Inc, DemandTools by CRM fusion, Jitterbit, Informatica Cloud Customer 360 (formerly Cloud MDM), Birst Cloud BI & Analytics etc.

Benefits Of Quality Data

Accurate data removes uncertainty from the decision-making process. The sales team can segment and classify  leads and customers accurately. This will further help the marketing team deploy targeted campaigns offering relevant offers and services.

Comprehensive data will help the sales team create cross-selling and up-selling opportunities in an aggressive manner. Decisions related to premium pricing and value-added services can be synced, tracked, and analyzed with other facets and aspects of customer behavior.

Reliable data will help managers assess leads accurately and execute the right follow-up strategies for revenue maximization. Further, predictive analysis will help middle management recognizes trends early, and take steps to manage accounts in a proactive manner.

Studies have indicated that businesses that that ignore data quality can end up losing a quarter of the potential revenue that can be generated from the CRM deployment. A 10-12% fall in the top line is inevitable if data-related issues plague the CRM platform. Of course, the resulting loss of goodwill and credibility can prove to be very expensive over the long run.

Challenges Involved In Maintaining Data Quality

Why is it that even big enterprise with the latest technologies and highly qualify workforce find the task of maintaining data quality to be challenging? Cleaning data and preventing quality issues is a difficult task due to the following reasons.

Unprecedented Increase In Volume Of Data

Today, corporate data is growing at a rate of 40% per year. Unlike the past, businesses need not get rid of old data to make space for new information. Instead, databases are growing bigger and businesses are enjoying access to quality historical and current data. While this growth offers tremendous benefits and advantages, it also creates numerous challenges for managers seeking to keep the data clean and reliable at all times.

Omni-Channel Operations

Businesses of all sizes rely on multiple channels for communicating and interacting with leads and customers. In the past, small businesses used to rely on low-budget low-reach tools like flyers, brochures, classified ads, and even word-of-mouth publicity to attract new customers. Today, even small businesses can use email marketing, website with online chat support, social media profiles—such as Facebook, Twitter, YouTube, Instagram, etc— along with conventional channels to cater to existing and potential customers.

Greater Negative Impact Of Bad Data

A data-driven organization stands to lose a lot by relying on inaccurate or unreliable data for its business decisions. The increasing reliance on big data analytics has created a high-risk high-return scenario where businesses stand to either gain a lot or lose a lot due to its reliance on dynamic data. This makes maintenance of data quality a challenge because there is very little margin for error or mistakes. Further, poor data can result in a vicious circle where bad judgment calls lead to further bad decisions.

Necessity For Real-Time Analysis

Businesses that can leverage the power of analytics to slice and dice the customer’s behavior data—into different geographic, demographic, psychographic, etc.—are likely to succeed in today’s competitive environment.

On the other hand, businesses that rely on the conventional approach of using data solely for determining future tactics and strategies will, ultimately, lose out to the competition. The requirement of real-time analysis enhances the importance of implementation of rigid quality standards throughout the organization.

Common Reasons For Data Quality Problems In Organizations

Ad-hoc Processes And Procedures

An ad-hoc approach when finalizing procedures and processes for data entry and access can easily lead to issues like duplicate data, incomplete records, entry of inaccurate information, and the presence of outdated data. A process or strategy that works well for a particular department may lead to issues and complications when deployed throughout the organization.

Careful analysis of the flow of data within the organization and proper understanding of the requirement of the various user departments will help in the creation of an efficient procedure for entering, using, and verifying the data.

Non-Compliance Of Quality Standards

Preparing detailed standards that are never followed will lead to inevitable quality issues. This is where user acceptance and employee buy-in makes a huge difference. The end users of the data must understand and appreciate the importance of following set standards and processes.

Migration Errors

Errors often creep in when businesses are migrating data from legacy platforms to integrate CRM modules and applications. This process often involves the integration of external data and information from third-party systems that may not comply with the strict standards applied to internal data. This leads to a cycle where flawed data leads to further errors and complications.

Lack Of Documentation

Organizations shifting to a CRM platform often invest time, effort, and manpower to clean legacy data before shifting the same into the new platform. However, data quality suffers when modifications take place without proper documentation.

Lack Of User Training

User training should encompass the increased awareness of the importance of maintaining data quality, practical training for tackling issues, and the procedure to be followed when rectifying errors or upgrading procedures.

Tackling Data Quality Problems

Even the most advanced technologies will fail unless the organization invests in educating its employees on the importance of quality data. This process should provide a communication channel where employees can provide feedback on various practical difficulties that they face when collating, tracking, measuring, and interpreting information in real-time.

Along with a motivated and educated workforce, the organization must identify the right processes and procedures that will ensure the information stored in various platforms and applications remains accurate and reliable at all times.  The procedure for entering, accessing, and modifying the data must be designed to match the operational requirements of the organization.


With Cloud Computing, Big Data, Internet of Things, wearable devices, and other innovations becoming an integral part of an organization’s operations, underestimating the importance of data quality can prove to be a very expensive mistake.

What truly important for CRM, Business Intelligence, and other enterprise solutions is the validity and reliability of measurable data. Data quality is all about remembering the 1-10-100 rule. If data entry costs $1 and data cleaning costs $10, then ignoring the problem will, sooner or later, set the organization back by $100.

Salesforce analytics and data quality go hand in hand. An attempt to transform the business into a data-driven organization with Salesforce CRM can’t be accomplished unless data quality is maintained in an effective manner at all times.

This article originally appeared on Kairos Technologies Inc’s Blog.