data hygiene - RP Revenue Pulse

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A database filled with entries that are inaccurate, outdated, miscategorized, or duplicated has a profoundly negative effect on the business.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.

TLDR: The need for MOPs to surface dollar values, improve processes, and validate ideas makes Data Science a natural ally. When MOPs and Data Science work together, revenue and lead generation become easier to predict, benchmark, and grow. But Data Scientists work their magic using emergent technologies that are expensive to deploy, and your CFO will only approve the budget for projects where the ROI makes sense. In any project proposal, focus on how the results can benefit the business and help Marketing boost ROI, and you’re likely to get the green light.

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. 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.

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.

We’ll guide you through that conversation in this Tough Talks Made Easy, with some help from Rachik Laouar. As the Head of Data Science at the Adecco Group, Rachik spent the last three years building a full-stack data team and making the business a predictable machine. Rachik contributes his personal views to this piece and does not represent the Adecco Group.

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 practice 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 judgement 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 behaviours. Data professionals connect and map the entirety of your business’ data to spot patterns and understand how to optimise processes. Where Marketing generates leads, Data Scientists automate changes to the lead journey to trigger positive engagement behaviours. 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 AI, which are expensive for companies to deploy. Collaborative time with Data Science and new tools, therefore, require a budget for development, which involves your CFO and CIO. Considering the costs, C-Suite’s looking to allocate Data Science resources only to teams that can justify the investment with impactful results.

There are a few points you can make to leadership in response. By modelling 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 organisation.

You might want to work with Data Science to better a product’s audience and attract more customer segments to it. Alternatively, you might want to streamline operations through automation and cut processes to achieve better outcomes. 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.

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 realise 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 modelling you want to do, limitations included, and what you’re trying to achieve with revenue when testing certain actions. 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 move the dial in your favour.

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, and you stand a good chance of getting the green light.

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

Tag: data hygiene

What Leadership Should Know About Data Hygiene