Why Marketing Data Management Remains a Huge Headache for Marketers

These issues suggest marketing teams need to reassess the way they manage their data. But what are the most common data management challenges facing marketers, and how can they overcome them?

1. Integrating legacy systems and modern marketing platforms

Organizations typically have a mix of information systems built by layering and interconnecting newer platforms with older ones. Storing, processing and pulling data generated by multiple platforms can be a challenge, and while not unique to marketing analytics, marketers know this problem all too well.

For instance, data produced from online campaigns, likely collected by newer marketing platforms, may not align with legacy systems that track offline campaigns. Even interoperating modern analytics platforms with each other can also be a headache, particularly when the services are competitors or when they specialize in different types of marketing channels.

As a result, companies struggle to build and track a single and cohesive view of their customers. Marketing professionals toggle back and forth between a number of platforms, which may lead to inaccurate customer profiles and subsequently ineffective campaigns. This impedes efficiency in their day-to-day work and poses risks in data security and compliance. Migration or integration between different systems can also elevate the risk of inaccuracy or threaten integrity of the resulting data sets.

Nevertheless, it is possible to integrate disjointed data sources. Practices and tools such as unified marketing measurement can help analyze datasets across various channels to help form a holistic customer view and identify actionable insights.

2. Improving data quality

Salesforce found that only 42% of B2B marketers are satisfied with the quality of their customer data. As much as high-quality data is an imperative in understanding and engaging customers, poor data quality persists as an issue for marketing professionals.

For example, the typical customer database is often plagued by low-quality records. Names, contact details and demographic information could be formatted incorrectly. It’s also not unheard of that importing or exporting data could duplicate records or make a mess of databases.

As marketing continues to grow in its reliance towards more granular and personalized data, improving and maintaining data quality will persist and likely even become more complex. While marketing data management tools may keep pace in sophistication, organizations should not only invest in the technology, but also in the talent that manage and make sense of customer and marketing data.

Learn more: 30% of Your Company Data is Outdated. Here's How to Clean Up Your CRM

3. Defining high-quality data

While it’s easy to accept that having high-quality data is an imperative, defining what makes data as high-quality is a challenge worth considering. Different frameworks offer a variety of metrics that organizations can consider when improving the quality of their data. For marketing teams, these data quality dimensions should be the priority:

• Timeliness: The time gap between data inputs and expected outputs (e.g. marketing campaigns) still meets business needs
• Relevance: The data can be a useful input in understanding or engaging customers
• Transparency: The source and how the data was gathered is known to marketers
• Representativeness: The data can provide accurate conclusions about a population from a sample, particularly when building customer profiles and segments
• Completeness: All the necessary attributes, elements and data points are present in a specific dataset
• Accuracy: The data matches the actual or real-world value (e.g. customer behavior)
• Consistency or standardization: There is synchronicity or an absence of contradictions across datasets

4. Translating data into actionable insights

It’s easy to get lost in the imperative of gathering and processing data about your customers without turning them into business intelligence. With all the powerful and relatively affordable data analytics tools easily accessible to businesses of any size, playing around with data can either become an end in itself or a paralyzing challenge that deters marketers and executives from translating data into meaningful decisions and actions.

Here are a few tips to keep you and your marketing teams from being distracted:

• Focus on business results: As early as possible, identify how your data can meet the KPIs of your marketing department and the entire business.
• Establish data analysis into regular workflows: Taking time to sit down and analyze data can sometimes be treated as special and irregular tasks. Find a way to institute this into the regular workflow of specific people.
• Leverage data visualization and storytelling: Decision-makers, whether inside or outside of marketing teams, can lean towards your desired action when data is presented as visual and compelling insights rather than as dry data points.
• Articulate clear problem statements and hypotheses: When looking at any given dataset, approach it with specific business problems in mind. This will focus your attention towards concrete issues that you need to solve.

Marketing data management challenges are here to stay

With all the capabilities readily available today, there’s no more excuse in not being able to know who your customers are and how to reach them. On the other hand, the explosion of data that marketers source, generate and manage can stop them from making sense of all the noise. Unfortunately, these opportunities and challenges in data management are here to stay.

To learn more about the importance of analytics in your marketing strategy, listen to our interview with Dara Fitzgerald on The Strategic Marketing Show:

Listen to the episode via your preferred pocast platform:

• Spotify
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Big data in marketing can be intimidating, but when executed with the right tools and practices, it will give you an edge over your competitors in understanding your customers and making your campaigns more effective.

Further reading:

• 5 Insights Shaping the Future of MarTech
• 5 Tactics to Ensure CRM Data Quality Isn't Compromised
• These 4 Critical Mistakes Are Ruining the Accuracy of Your Marketing Data