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Taking a holistic approach to data requires considering the entire data lifecycle – from gathering, integrating, and organizing data to analyzing and maintaining it. Companies must create a standard for their data that fits their business needs and processes. To determine what those are, start by asking your internal stakeholders questions such as, “Who needs access to the data?” and “What do each of these departments, teams, or leaders need to know? And why?” This helps establish what data is necessary, what can be purged from the system, and how the remaining data should be organized and presented.
This holistic approach helps yield higher-quality data that’s more usable and more actionable. Here are three reasons to take a holistic approach at your organization:
1. Remote Workforce Needs Simpler Systems
We saw a massive shift to work-from-home in 2020, and that trend continues to pick up speed. Companies like Twitter, Shopify, Siemens, and the State Bank of India are telling employees they can continue working remotely indefinitely. And according to the World Economic Forum, the number of people working remotely worldwide is expected to double in 2021.
This makes it vital that we simplify how people interact with their business systems, including CRMs. After all, we still need answers to everyday questions like, “Who’s handling the XYZ account now?” and “How did customer service solve ABC’s problem?” But instead of being able to ask the person in the next office or cubicle, we’re forced to rely on a CRM to keep us up to date and make sure we’re moving in the right direction.
This means team members must input data in a timely manner, and others must be able to access that data easily and make sense of it, whether it’s to view the sales pipeline, analyze a marketing campaign’s performance, or spot changes in customer buying behavior.
Unfortunately, the CRMs used by many companies make data entry and analytics challenging. At best, this is an efficiency issue. At worst, it means people aren’t inputting the data that’s needed, and any analysis of spotty data will be flawed. That’s why we suggest companies focus on improving their CRM’s user interface, if it isn’t already user-friendly.
2. A Greater Need for Data Accuracy
The increased reliance on CRM data also means companies need to ramp up their Data Quality efforts. People need access to clean, accurate information they can act on quickly.
It’s a profound waste of time when the sales team needs to verify contact information for every lead before they reach out, or when data scientists have to spend hours each week cleaning up data before they analyze it.
Yet, according to online learning company O’Reilly’s The State of Data Quality 2020 report, 40 percent or more of companies suffer from these and other major Data Quality issues:
- Poor quality controls when data enters the system
- Too many data sources and inconsistent data
- Poorly labeled data
- Disorganized data
- Too few resources to address Data Quality issues
These are serious systemic issues that must be addressed in order to deliver accurate data on an ongoing basis.
3. A Greater Need for Automation
Data Quality Management is an ongoing process throughout the entire data lifecycle. We can’t just clean up data once and call it done.
Unfortunately, many companies are being forced to work with smaller budgets and leaner teams these days, yet the same amount of data cleanup and maintenance work needs to get done. Automation can help with many of the repetitive tasks involved in data cleanup and maintenance. This includes:
- Standardizing data
- Removing duplicates
- Preventing new duplicates
- Managing imports
- Importing/exporting data
- Converting leads
- Verifying data
A Solid Business Case
By taking a holistic approach to Data Management – including simplifying business systems, improving data accuracy, and automating whenever possible – companies can improve the efficiency and effectiveness of teams throughout their organization. These efforts will help organizations come through the pandemic stronger, with a “new normal” for data that’s far better than what came before.