RevOps Strategy

Making the Business Case for Data Automation

If you’re reading this, it’s likely you’ve encountered the data chaos beast. Perhaps you’re worn or shaken and could use shelter from its fiery breath. It’s a lot to go up against alone. That’s why it can be helpful to collect your wits, sharpen your tools, and possibly gather a team of adventurers to help. These could be revops pros, or a mix of sales ops, MOPs, or otherwise great tactical and logical thinkers with a penchant for technical depth.

The first step in addressing your data issue is building a business case to do so, which unlocks the company resources you’ll need to go deeper than band-aid solutions which, as we now know, only exacerbate the problem. To do this, you have to understand the problem well enough to explain it to others.

In this post, we’ll explain how to build a business case to address the root of the problem—even if you’re empowered to find a solution and don’t need others’ help to begin. This planning work is crucial and the only way to move fast is to start slow and build a proper case.

Step 1: Identify one data pain that hits close to home

For practical purposes, it helps to select one issue to solve that’s within your domain that is already quite clear to you. Perhaps that’s your company’s lead and contact data, long- neglected and polluted by years of questionable list purchases. Perhaps it’s your customer account data—picked over by several generations of account managers, each with their own syntax and formatting habits. Maybe it’s that behavioral data in your application doesn’t match customer data, or that the sync between two systems is old and rusted. Or perhaps the people on your team spend an inordinate amount of their time on manual data cleanup and entry that you suspect may need not exist.

Pick the thing that’s close to home—something you can imagine fixing in the near-term—and begin there.

Ideas for getting started

  • Assess your data fitness
    Spot check a random sample of 20 records. Assess if those records are complete, accurate, up-to-date. Check for duplicates. Share your assessment with others and discuss impact. Measuring where you are today will help you better understand where to go next.
  • Normalize standard fields
    Define and enforce standard values and formats for common fields like name, email address, phone number, and billing address. Feeling adventurous? Take on industry and job title.
  • Enrich NULL values with commonly available data
    Third-party enrichment services are great for helping you complete the picture for accounts, leads, or contacts that look a bit empty. The safest approach that protects your existing data is to only enrich NULL values.

One thing to remember: don’t bite off more than you can chew. The road to unfinished data projects is paved with sprawling scopes. Far better to pick something that’s solvable and can be repeated than to tackle it all in one fell swoop. For example, “Refresh our entire CRM of 10 million contacts” is probably unreasonable and a lower priority than ensuring new leads coming in are accurate across all systems, or that your ABM contact-routing is firing correctly.

Step 2: Identify others who are affected

Who else is feeling the heat of the data chaos beast? Talk to them. If you picked the prospect contact data problem, get virtual coffee with a sympathetic SDR manager and ask how their team spends their time. 

Look for allies downstream from you—those who are impacted by the data issues after your team touches it—but also upstream. Who’s buying those lists? Who’s entering duplicate records? Often, you’ll find individuals who, like the SDR manager, perceive the effects of bad data as a feature of their role.

Often, it’s not them. It’s the system. They’re hamstrung by procedural baggage, old habits, aging applications, and messy, inherited instances. Like the CRM marketer brought in through an acquisition who’s learned to make do with duplicate CRMs via connector tools and endless downloading, adjusting, and uploading. Given the chance, many will happily join you on your quest.

Step 3: Collect data and anecdote that get at the pain

What are the impacts of the bad data on you and your compatriots? Gather them in a list with three columns—the role, the impact on them, and one anecdote that illustrates the problem.

Step 4: Build your business case

Writing out your business case helps you get your thinking straight. Are the damages enough to warrant your time and attention? Is the source of the problem within or near to your control?

If you are presenting your case to someone, tailor it to what they care about. How can you frame the problem in terms they’re measured on?

Your business case can be as simple as a Word document with the following sections:

  • Executive summary: Start with the costs and the benefits of fixing the problem. Explain the problem clearly in terms your audience will understand.
  • Problem: 
    • What is the source of the data issue?
    • How do you manage it today?
    • What are the direct costs? (i.e. lost revenue, calculable productivity) 
    • What are the indirect costs? (i.e. negative customer experiences)
    • What are the potential downstream consequences?
    • Who else relies on this data?
  • Cause: Why is this problem occurring? This could be either carefully explored or not yet understood. Regardless, it should be laid out as clearly as possible.
    • Well-explored causes: Faulty connectivity across various data sources or lack of data standards and data governance
    • Unexplored/not yet understood causes: Suspicion of inefficient use of your data warehouse

Step 5: Prioritize Data Automation

Data automation is not the answer to every data problem, but it is the answer to many. Tease out which scenarios it’s the best fit for. Data automation is the process of improving your data quality through automation rather than manual effort. Anywhere you see an issue where someone is transferring data between two systems that should talk, it’s a likely use case. Or, anywhere you see data unresolved across multiple systems and the need for them to talk together, data automation may hold the key. The idea is to get people back to focusing on their job, not on moving data.

A few examples where data automation shines:

  • Sync data back and forth between two or more systems (multi-directional sync)
  • Manage data across many systems, but do it from one central place (centralized data management)
  • Errors “self heal” without human intervention (data resiliency)

How do you know if data automation is the answer?

On your journey toward your fix, it can help to begin looking at (or even trial) data automation platforms like Syncari. It’s likely there are others who’ve used it to fix problems similar to yours, and you can use their stories as building blocks for your business case, or to better understand your own problem. If nothing else, talking to subject matter experts can help stanch some of the well-understood issues while you investigate—like controlling for data quality across faulty integrations.

You’re ready to move forward 

With your business plan assembled, act on it. If you don’t need anyone’s permission, ask yourself, does the benefit outweigh the time I’ll spend investigating this? What do we stand to gain? If you need someone’s permission, share the business case and schedule time to discuss. Walk them through. Are they willing to grant you the time and resources to investigate? If so, you’re ready for the next phase in your journey—to deeply understand the root cause. 


Download The Definitive Guide to Data Automation to discover clear frameworks and real examples to restore your confidence in your data and get on the path to predictable revenue growth.

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