MOPs and Data Science: How to Get the Green Light on Collaboration

TLDR: The need for marketing ops to surface dollar values, improve processes, and validate ideas makes data science a natural ally. When MOPs and data science teams work together, revenue and lead generation become easier to predict, benchmark, and grow.

The importance of data literacy: Marketing is now a data-driven discipline, where a top priority is to understand what generates revenue and drives growth. Data literacy is crucial for MOPs to handle data, structure their systems, and answer decisive business questions.

The problem for most MOPs teams: Unfortunately, teams often lack the skills and resources to manipulate and turn data into an asset that generates value. Data scientists are the ideal collaborators for MOPs to validate ideas and improve processes, but they’re hot commodities in every workplace.

How to get the collaboration green light: When budgets are tight, it’s easy for C-Suite to overlook marketing when approving spending for data science projects. So, if you want the green light on a collaborative project with data science, show your CFO and CIO that the ROI makes sense.

What’s in this article for you? In this Tough Talks Made Easy, we’ll guide you through the conversation you’ll need to have with your CFO. You’ll learn how to:

➡️ Make data-driven decisions.

➡️ Collaborate effectively with data science teams.

➡️ Justify data-driven investments to leadership.


How data science enriches marketing

One of the most persistent challenges marketing teams face is proving their success.

If you’re in a low-margin business in particular or otherwise facing cost strains, there’s extra pressure from your CFO to show your contributions to the bottom line.

Marketing as a space also has many ‘common sense’ generalizations of best practices floating around, but the likes of “never send an email on a Friday” don’t hold water for your business without evidence.

Without the data or the know-how to interpret it correctly, your team is validating decisions and measuring success in the dark.

The need for MOPs to surface dollar values and make good judgment calls makes Data Science a natural ally.

MOPs collects lots of data from campaigns, which Data Science can turn into detailed customer profiles and identify purchasing behaviors.


“Data science connects and maps the entirety of your business’ data to spot patterns and understand how to optimize processes.”


Data science connects and maps the entirety of your business’ data to spot patterns and understand how to optimize processes. Where Marketing generates leads, Data Scientists automate changes to the lead journey to trigger positive engagement behaviors.

When MOPs and Data Science work together, revenue and lead generation become easier to predict, benchmark, and grow.


Collaborating with purpose

Data science teams work their magic using emergent technologies, like machine learning and artificial intelligence, which are expensive for companies to deploy.

This means collaborative time with Data Science and new tools requires a budget for development, which involves your CFO and CIO.

Considering the costs, your C-Suite is looking to allocate data science resources only to teams that can justify the investment with impactful results.

You can make a few points to leadership in response.

👉 By modeling the business end to end, Data Science can see what brings in the most revenue, and as one of the most commercially-minded teams, Marketing should be at the top of the list. The more you invest in making campaigns compelling in response to audience data, the more likely the business will win deals.

In other words, investing in Marketing boosts the whole organization.

👉 The insights that Data Science provides can create a strong audience for a product/service and attract more customer segments to it.

👉 Collaborating with data science can help find solutions to streamline operations through automation and cutting processes to achieve better outcomes.

Bottom line: Whatever your proposal, frame it to C-Suite with intended results and impact in mind. 

After all, you’re not running experiments for their own sake — you’re working with Data Science to help Marketing make or save more money than you spend, investing less per lead generated than you bring in. That’s the language your CFO and CIO speak.


Set appropriate expectations

Leadership can be adverse to risk or expect quick results, which means your CFO or CIO might be hesitant to play the long game.

A dose of reality: if you’re trying something new, you need time to ride it out, make sense of findings, and realize the benefits.

Suggest running campaign experiments with small subsets of your audience first, as a proof of concept, to make the idea more palatable to a hesitant CFO. 

On the whole, explain the analysis and modeling you want to do, the included limitations, and what you’re trying to achieve with revenue when testing certain actions.


“If you can estimate the ROI at 1.5-2x what you spend, you’re likely to get the green light.”


Focus on how the results can benefit the business, and if you can estimate the ROI at roughly 1.5-2x what you spend, you’re likely to get the green light.


Investing in success

MOPs and Data Science together can be a force of nature, making the wealth of data that Marketing collects actionable and steering better strategic decisions.

Come to any conversation with leadership with a clear plan of action and a confident sense of how collaboration with Data Science can boost the bottom line.

Need some help with data? Drop us a line to chat

Data Privacy and Reputation: Protecting Your Business

TLDR: Data privacy regulations are evolving fast, and businesses in breach face harsh financial penalties and reputational damage. Bring RevOps, legal, sales, and marketing together every quarter to set the agenda for your data privacy strategy, review your processes, and plan around new compliance requirements. Hiring a data privacy officer and investing in cybersecurity are strong measures to properly process and protect customer data.

The data privacy landscape moves fast. As regulations emerge worldwide, businesses that collect, store, and use customer data face a complex web of compliance responsibilities.

Businesses that breach data privacy regulations, even unintentionally, face steep consequences. Regulators can place data handling restrictions on companies and issue sharp fines. To date, EU regulators have enforced over €1.5 billion in penalties to organizations in breach, with an average of €1.4 million per fine.

In a time when people are more conscious than ever about how businesses handle their data, falling foul of regulations is an easy way to shatter customer trust.

Now is the time to act. To stay compliant, your RevOps team needs to know how the interlocking data privacy regulations apply to the territories where you handle customer and prospect data. In this Tough Talks Made Easy, you’ll learn to identify where the challenges and blind spots lie within your company, and the processes you should implement to keep on top of your responsibilities.


Challenges with data privacy

Companies tend not to proactively review their data privacy policy, which causes them to fall behind the times and incur fines. Many major markets (EU, Japan, India, Australia, Brazil, and some US states) have regulations that place responsibility on the organizations operating in these territories or collecting data on their residents.

While data privacy is more complex for organizations operating internationally, multiple regulations can apply even when doing business in one local market.

As the regulatory landscape evolves, it’s important to stay in the loop with how these frameworks shape your legal obligations and data practices. It’s particularly crucial if your business is considering expanding into international markets.

Organizations typically focus on online practices when designing a data privacy strategy, sometimes, to the detriment of offline behavior. The age-old challenge of sales and marketing alignment becomes relevant to compliance here.

Important: As Sales Ops and MOPs send customer and prospect data between platforms, both teams should know how they’re allowed to use and store this data to avoid taking actions that violate the privacy rights of people in the dataset.


Measures to take

To set the agenda for data privacy strategy, RevOps should get together with legal, sales, and marketing every three to six months. Across teams, you want everyone to have a good grasp of their responsibilities and have an eye on the regulatory movements that could impact their work.  


First, answer these questions during an initial meeting:

  • How are privacy and cookie policies evolving?
  • What are our regulatory requirements for each market we do business in?
  • How might our usage of tools and the web need to shift to meet new requirements?
  • What gaps do we have in implementing compliance policies?


Next, review your data processes:

From there, review your data capture, storage, and deletion processes. When capturing data, timestamp the date and time people submit contact forms, why they’re contacting your business, and whether they’ve opted in to receive marketing communications. For logging and auditing purposes, this creates evidence that you’ve lawfully obtained the authorized data.

For sales ops and marketing ops, set up filters to segment the people in your dataset based on the communications they’ve opted in or out of receiving. Read our piece on data hygiene to learn more.

For prospects who’ve unsubscribed from your communications, check in with legal to decide when to delete their data entirely. And it helps to test regularly that your measures are working as planned. Are your filters and timestamps working correctly? Are you deleting data when required? Are you storing it in secure places that don’t violate compliance policies?


Finally, hire a data privacy officer:

Hiring a data privacy officer is a smart move. DPOs are experts in:

  • keeping up with regulatory evolution
  • guiding policies and processes, and
  • educating people on the risks of non-compliance is a smart move to advocate.

If the budget to hire for such a role is a concern, it’s worth mentioning the penalties that regulators can apply. E.U. authorities, for instance, can enforce the GDPR with fines of up to €20 million, or up to 4% of a company’s global annual turnover.

For similar reasons, cybersecurity training and tools are worth pushing for. Data breaches decrease customer confidence and brand strength while making fines and legal action all the more likely—so by investing in data protection, you invest in protecting your customers and your reputation.


Create trust

People want to do business with organizations they trust.

By making a cultural and financial investment in data privacy, you get to:

  • keep your business from appearing under the limelight for the wrong reasons
  • avoid fines and restrictions on how your RevOps team uses data, and
  • better understand the processes to implement if you’re expanding into new markets.

Want to learn more about the actions you can take to remain GDPR compliant? Get in touch with us.

Will the EU’s Ban on Google Analytics Affect Your Company?

TLDR: To date, France, Italy, Denmark and Austria have banned Google Analytics—a trend that could continue throughout the EU. If your business depends significantly on Google Analytics or EU markets, your analytics practices and revenue could be at stake. Wherever the ROI makes sense, focus on using owned data for the countries affected by the ban, explore alternative tools that are GDPR-compliant, and invest in the education of a Data Privacy Officer to adapt to new and emerging regulatory developments.

Several key EU markets recently moved to ban Google Analytics. Data protection authorities in Italy, France, and Austria have deemed the practice of transferring user web activity and IP data to the US a violation of data protection laws. The bodies found the US lacks adequate safeguards to preserve personal data anonymity.

Businesses whose products, services, operations, and infrastructure rely significantly on Google Analytics would do well to explore alternative strategies and software, planning around the likely consequences of the ban and potential developments in regulation. This also applies if your business takes significant revenue from EU countries.

A potential move away from Google Analytics could make your data less accurate and accessible. It would also require setting up alternative web optimization and tracking mechanisms. Therefore, your CMO and CTO are chief among the people who should know the score.

In this Tough Talks Made Easy, we’ll help you talk them through the impact and outlook of the ban, along with some solutions to consider. The better educated your leaders are, the better prepared you’ll be to weather any disruption.


The impact of the ban

The gravity of the situation depends on how much Google Analytics drives your business. If your MOPs and RevOps teams use it greatly, your data collection, reporting, and forecasting powers are at stake.

No longer able to track web activity and IP data created from top-of-funnel initiatives, MOPs and RevOps will need to refocus analytical practices exclusively onto data they already own (e.g., captured leads living inside their system with consent expressed in compliance with the GDPR).

For now, the Google Analytics ban applies only to France, Italy, Denmark and Austria. To keep doing business in these countries, you’ll need to adapt your website and introduce new processes and tools as necessary to comply with both the GDPR and any local requirements. If your business is based outside of these countries, the ban equally affects your ability to use Google Analytics to process data from users in France, Italy, Denmark and Austria.

The key thing to remember: to stay compliant with the GDPR, you cannot transfer web and IP data from these citizens and countries to the US.

Actionable takeaways

Your CMO and IT will need to investigate the changes required to your website, subdomains, and data analytics processes to stop the tracking and transference of website data for these countries and their citizens.

Your CTO should consider the ROI of tools that offer similar capabilities to Google Analytics. Examples include:

Any new tool you consider should allow you to process data from France, Italy, Denmark and Austria in compliance with the GDPR and any country-specific regulations. Your Data Protection Officer (or a consultant with GDPR expertise) is also a good source of counsel on potential changes to your tech stack and infrastructure.

Of course, these changes take time, effort, and resources. If your CMO and CTO need a hand assessing the ROI of making adjustments and implementing more advanced processes, look at how much revenue your business sees from the countries impacted. If less than 5% of gross revenue comes from France, Italy, Denmark and Austria (and their citizens in other countries), it might make sense to rely solely on the data you own.


Future EU bans?

While the ban currently applies to just three countries, it’s sensible for leadership to think about how the regulatory landscape might evolve.

EU countries could increasingly move to ban Google Analytics and restrict the transfer of user data from the EU to the US, potentially leading to an EU-wide ban to streamline regulations in the bloc.

A sweeping EU-wide ban would take considerable time to enforce, though it would be a massive blow to companies whose data storage infrastructure is based in the US.

As a means of ensuring GDPR compliance, US companies wouldn’t see much success from storing their user data in the EU.

Companies exempting themselves from transferring data back to the US would ultimately violate the CLOUD Act, which asserts that US businesses must, at request, provide authorities in the US with data stored in their servers, regardless of where those servers are stored.

One emerging piece of legislation to watch is the American Innovation and Choice Online Act. If codified, the bill would ban large tech companies such as Google from using non-public data generated by business users to benefit the covered platform’s own products. The enforcement of new antitrust practices in the US could result in data transfers to the US being deemed acceptable in accordance with the GDPR.

Amidst it all, businesses that prioritize EU markets or have a significant EU presence may increasingly turn away from Google Analytics and adopt tools that guarantee GDPR compliance. A resulting rise in demand and availability of solutions that ensure GDPR compliance can help your CTO identify an alternative that allows you to keep doing business in the EU in the most optimal way.


The bottom line

The Google Analytics ban in various European countries is likely not an existential threat to your business—but if your services, operations, and infrastructure relies on the software or you get a significant portion of your revenue from the EU, it’s a situation that demands building resilience.

Wherever the ROI makes sense, turn your focus towards owned data for the countries affected by the ban, explore GDPR-compliant alternatives to Google Analytics, and invest in the education of a Data Privacy Officer to adapt appropriately to new and emerging challenges with data regulation.

Get in touch for more guidance on navigating your RevOps team through the data privacy landscape.

How Can I Avoid Having Dirty Data?

Hi Jo,

I keep hearing about the cost and risk of having dirty data in your MOPs systems, but I don’t know how to check if my company’s data is up to snuff.

Do you have any advice on rooting out dirty data and preventing it from happening?


Data-driven Dave

pink seperator line

This is a great question, Dave!

It’s one we should all be talking about.

Let me start by making sure we’re on the same page about dirty data (sometimes called rogue data).

The short version is that dirty data is any data with erroneous information.

The slightly longer version is that there are different types of dirty data, including:

  • duplicates
  • errors and typos
  • outdated information
  • prospects that don’t align with your target persona, and
  • incomplete entries (e.g., without an email address).

Examples of dirty data:

For example, you could have an entry from someone who no longer works at a company — so any email sent to them will bounce back.

Or you could have an entry with a typo, like an email address ending in “.con” instead of “.com.”

Duplicate (or triplicate) entries are another common data problem. I’ve worked with companies with thousands of duplicates in their database, which is not sustainable or practical.


Dirty data is a mess

Dirty data can indeed be costly.

Email reputation

Bad email reputation is a huge issue.

For instance, if you’re sending marketing emails to people who shouldn’t be in your database and they mark your email as spam, that counts as a ding against your email sender reputation.

Your sender reputation is a measure that internet service providers take to determine whether they will deliver your emails to the inboxes of the people on their network.

The lower your score, the lower the chance your email reaches your audience. It can be really hard to recover from a low score.

Database costs

Some martech databases will charge per the number of entries in your database.

For companies with thousands of duplicates, that can mean they’re spending way more than they should—which isn’t great.

The costs also add up when you have to spend on tools that clean that data.

Having that many duplicates also gives you a false understanding of how many people are actually in your audience. It can lead you to make decisions that don’t necessarily make sense for your business.


Steps to avoid dirty data

So, how do you stay ahead of dirty data?

You can do it in-house, but it does require some heavy lifting (which we can help with).

Here are my suggestions.

Create a data hygiene plan

Bloated, inaccurate databases cause all kinds of problems.

Data hygiene is a company-wide project that gets your entire team on the same page.

It will standardize how people collect and handle data across systems and conduct periodic audits to check the quality of your data and sources.

We wrote a Tough Talks Made Easy article outlining the steps you’ll want to take.

Build habits into your processes

Every six to 12 months, you should perform checks on your database to identify and remove any dirty data.

This process goes beyond just looking for duplicates and errors.

It requires a concerted effort to identify the people who:

  • no longer fit within your target persona, or
  • haven’t engaged with your content for a particular period.

Take proactive steps

Dealing with dirty data shouldn’t just be a corrective action.

There are also things you can do to avoid creating those errors in the first place.

For example, duplicate entries tend to happen when teams import lists from multiple sources (e.g., Marketo and Salesforce) without checking for repeat entries.

If you’re importing data, ensure there’s a check in place to flag duplicates.

You should also clean up any list (e.g., check if there’s a missing email address) before it gets migrated.

Lastly, building a process to identify and delete common bogus email addresses (like or can help keep your data clean.

Normalize your data

You’ve probably seen that some companies and teams use full country names, like Canada, while others use country codes, like CA.

The best way to keep your data clean is to normalize your entries so there aren’t discrepancies in your data set.

These might sound like small changes, but they’re important ones. Trust me, once you start doing these things, you’ll be able to have a lot more trust in your data.

You’ve got this, and if you need more advice, let us know.

Jo Pulse

P.S. Never miss an update! Follow us on LinkedIn

How to Create a Data Hygiene Plan That Works

TLDR: Bloated, inaccurate databases cause all kinds of problems; lost revenue and productivity, heightened risks of data privacy violations, and unreliable decision-making. Data hygiene is a company-wide project—everyone from the CEO to your SDRs should think critically about how they handle data and contribute to policies. Standardize how people collect and handle data across systems and conduct periodic audits to check the quality of your data and sources. This will drive the business forward and allow teams to support each other and reach customers and prospects as effectively as possible.


Data runs the world, and it also runs your business. Many leaders know this and have strategies for data hygiene and protection, but too often, the enforcement of policies and best practices is inconsistent. This creates a culture where teams don’t know how to collect, handle, and categorize data, and lack insight into why data hygiene is so important.

The results? Inaccurate data and bloated databases—sources of pain for people in many different roles and threats to the revenue and reputation of your business.

If you’re feeling the strain of dirty data, this Tough Talks Made Easy is for you. You’ll learn to explain to leadership the deep impact of bad data and influence a shift to a data-centric culture, suggesting policies, practices, and perspectives on data that help everyone in the team do their jobs more effectively.


The damage of dirty data

A database filled with entries that are inaccurate, outdated, miscategorized, or duplicated has a profoundly negative effect on the business. People doing tactical work burn hours to correct and clean data. Concerned with the quality of their information, SDRs get distracted from reaching out to people, which slows down the sales cycle. CX-wise, missing pieces of information compromise your interactions with customers, who’ll know when your outreach is less than seamless.

Between marketing operations and sales operations, bad data and unclear accountabilities cause infighting, as teams blame each other for disarray. Everyone who needs reporting, from C-Suite downwards, simply cannot surface or grasp the performance and impact of their work. With a messy database, extracting the insights to fuel strategic decisions becomes a near-impossible effort.

If you can’t trust your data, you can’t trust your decisions.

Bad news for the productivity, revenue intake, strategic potential, and inter-team collaboration of your business.

Then factor in some more explicit financial costs. You’re getting charged for your database per row—bloat = dollars spent.

Excess data also heightens the risk of breaching data privacy compliance requirements. EU regulators have issued an average of €1.4 million per fine to companies in breach of the GDPR, so if your database includes old and duplicate records of people who’ve opted out of communications or asked to have their data deleted, you need to clean up ASAP.

The larger your tech stack, the greater the likelihood (and consequences) of disordered data. Coming up with and implementing a system for data hygiene may seem like an effort that’ll slow you down in the short term, but it’s the smart choice every time over tolerating a messy database and all its chaos and revenue loss.


Develop a hygiene plan

Data hygiene is a company-wide project—everyone from the CEO to your SDRs is responsible within their remit for thinking critically about how they handle data and contributing to policies.

How does data enter your system?

The first thing to think about is how data enters your system. Your sources are often things like:

  • enrichment tools
  • CRM data
  • web forms, and
  • purchase lists.

But could also include people like SDRs manually inputting information.

Verify the source

With each new entry, verify that the source is reputable and the data is both factually correct and accurate (e.g. checking for spelling errors and duplicates). And take extra care with data obtained from gated content—in the earlier stages of the cycle, people are more likely to offer untrue or incomplete information to easily access your content.

Standardize your data types

Lots of different people touch data when it’s in the system, and without a strict policy on how to handle and categorize it, things can quickly get out of hand. Standardizing the data types you collect and fields used across your systems can help to ensure you’re handling only the most relevant information and organizing data consistently.

Form a data compliance team

To further help keep things clean, advocate for a dedicated data compliance team. This team comprises a board of people who assess the impact of introducing any new field, data type, or source into your database.

Review your approach to data collection

It’s also worth interrogating your approach to data collection. More data doesn’t necessarily make you better informed, and it’s certainly not worth the excess storage costs and risks of violating data privacy requirements.

Additionally, not all data created or data sources are equal. You may have one or several usual suspects for creating bad data. Get ahead of it by shutting those sources down so you’re not creating bad data to start with.

Ask of each piece of data you solicit:

  • What’s the purpose, use, and relevance to your goals?
  • What categories of information are relevant to your customers and prospects?
  • What information do Sales need to move through the cycle?

Auditing your systems and data on a regular basis (e.g. monthly, quarterly) is crucial to determine what your baseline for hygiene should be. This is your opportunity to detect and remedy any flaws in your database.

Steps you can take to improve data hygiene:

  • delete old and unused records
  • remove white spaces
  • merge duplicates together
  • check your integrations are tight, and
  • ensure your records are enriched with the correct information from quality sources.


Dealing with data

The way you handle data can make or break your business. Dirty data results in losses of revenue, productivity, and decision-making power—to avoid the fallout, C-Suite should treat data hygiene as a priority initiative for everyone in the organization to partake in.

Clear, enforced policies that standardize how people collect and handle data across systems, and periodic audits to check the quality of your data and sources, will drive the business forward and allow teams to support each other and reach customers and prospects as effectively as possible.

Struggling with systems and data in disorder? Drop us a line. We’re here to help.

How Do I Enhance Security in MOps?

Hi Joe,

I’m worried that we’re not doing enough when it comes to security in MOPs. There are some pretty big gaps and I’m not quite sure what to do about it. How do I go about asking for help? Should I create a plan beforehand? How transparent should I be with leadership?

Thank you,
Concerned Casey

Casey, I can’t thank you enough for that question. I don’t think we talk about security enough in MOPs—but we should. Our marketing automation software holds a ton of sensitive information, whether it’s user account details or some level of personal identifiable information (PII), and our customers trust us to keep it safe. Particularly now, where marketing relies so much on personalization and connecting the dots between what our business offers and our customers’ needs. 

The risks of mismanaging this data are huge. For one, if a hacker or bad actor gets access to a pool of customer information, you better believe they’ll use it for nefarious purposes. Whether it’s selling that information to other cyber criminals or your competitors, using it to access your customers’ accounts on other high-value platforms, or blackmailing your company; there’s no shortage of ways your data can be used.

Beyond compromising your customers, a data breach is also bad for business. After they’ve been compromised, companies can spend millions of dollars addressing their security vulnerabilities and the loss of reputation that comes with a cyber attack.

The MOPs teams and businesses that are doing security right are focusing on the following areas: 

  • Data integrity: What data you collect, how you collect it, where you store it, and how you maintain it can all influence how secure that information is. For instance, there’s no need for you to have your customers’ social security numbers—so don’t ask for them. And if you do have passwords or PII on your marketing systems, you should look into encrypting or hashing them so that if a hacker gets their hands on them, they can’t read anything. You can also evaluate whether there’s even a business need for this sensitive information on your marketing system.
  • Controlled access to your systems: Security savvy teams ensure that only the right people have access to the right data—at the right time. It can be dangerous to have too many user accounts with permissions to access and manipulate the information on your systems. Instead, you should take a look at all your roles and permissions, and limit access to the people who need the data on a daily basis. Not everyone should be an admin. In addition, conducting regular scrubs on your systems to remove any old user accounts will also ensure you’re not at risk of a disgruntled employee compromising your data or your systems. 
  • Robust security policies: Good security should mean that you don’t have to think about security. With solid policies in place that let the right people in and keep the bad actors out, you and your team can focus on what you do best: marketing ops. 

If you’re seeing gaps in any of these areas, you should absolutely have a conversation with your security team (if you have one) and your executives. Be fully transparent about what you think is lacking, what the impact of those gaps are, and what the business should be doing instead. If they ask you whether this is an immediate need, the answer is yes. At the end of the day, securing your data is all about being proactive. You need to stay one step ahead of the bad guys—and avoid being the next big data breach in the news. 


You’ve got this, 

Joe Pulse