The 5-Step Process to Data Cleansing & Automation

Most vendors have data coming in from many sources across the channel, some of which still use manual forms and spreadsheets that create long reporting lags, contain incomplete or inaccurate data, and are full of errors, duplicates and inconsistencies.

You already know that spreadsheets aren't going to cut it for the massive 12x increase in data you can expect when your business transitions to a cloud-based model. Once you put a data management tool in place, how can you make sure that the data you already have—and the new data that comes in—is accurate, usable, and reliable?

5 Steps to Clean and Effective Customer Data

Clean data is critical for correct billing, customer service, inventory, and overall efficient and effective operations. It feeds into decisions about performance and ROI across product SKUs, market segments and target customers, enabling insight and informed action that can reduce costs and maximize revenues.

Step 1: Prioritize Data Fields

What types of data are most important for your renewals team? Determine the specific fields to focus on that have the most direct impact on customer success and renewals. These may be fields such as:

• Company name
• Contact name
• Job title
• Email address
• Phone number
• Renewal date(s)
• Subscription type
• Product SKUs
• Customer industry
• Revenues

Step 2: Establish a Data Cleansing Process

Document a consistent system that someone should follow to correct data errors or complete missing data fields. This process may direct data entry or administrative staff to conduct a Google search, contact the customer directly, or seek an internal source.

Having a standard process for data cleansing in place will help ensure consistency and accuracy as well as make sure that your data cleansing outreach aligns with customer service standards.

Step 3: Cleanse Existing Data

While correcting data errors and removing duplicates are obvious aspects of data cleansing, those aren't the only important steps. You'll also want to standardize terms and formats across a given field (called "data normalization") to help identify matches, standardize records and establish a consistent user experience. In addition, strings of data such as addresses or purchase records should be broken into multiple, individual fields to make these records consistent and easier to analyze.

Step 4: Institute Data Rules & Workflows

Clearly documented and straightforward governance practices and organizational standards are essential to maintaining quality data. Data validation systems can automatically standardize fields, cross-check accuracy, issue alerts when errors or blank fields occur, and can be applied to existing and new data. As part of this process, a team (or individual) can be assigned responsibility for data accuracy, accessibility and consistency.

Part of this process should enable data sharing across the organization. All business units should have direct access to the data they need, with centralized management to monitor overall data accuracy and quality.

Step 5: Regularly Review and Update Data Quality and Procedures

Best practice is to conduct a data quality and procedure review each quarter. This process can correct for bounced emails and address increasing bounce rates. It can assess changing data priorities and check on the status of data health across the organization.

Having access to the right data that is reliably accurate and complete is an integral (and complex) part of transitioning to a cloud-based XaaS model.

For further guidance on transitioning to the cloud, download our free eBook, "Transition to Cloud Consumption."