Any effective digital marketing strategy is built around one thing—data. Anyone who has suddenly been served ads for a product after being to a specific website knows their data is being collected.

This strategy is certainly not new. Marketers have collected data for a long time. Back in the ’60s, media mix modeling was already being used by marketers to see how impactful advertisements were on sales.

While it’s been around for some time, collecting data has changed drastically. Today, data collection methodologies are significantly more sophisticated and accurate.

Marketers can track each individual’s journey from the first click to the final purchase. They can see what pages were visited, how long each page was viewed, what products were added to the cart, and finally, whether a purchase was made.

The sheer volume of data now available to marketers is both a blessing and a curse. On the one hand, marketers have more information at their disposal than ever before. They can segment their audience into smaller and more targeted groups, resulting in more effective campaigns. On the other hand, this vast amount of data can be overwhelming and difficult to manage.

We’ll discuss the various issues related to managing marketing data and how a centralized data warehouse differs from a decentralized one.

Want to skip ahead?

First, let’s look at how data management can help your organization.

How data management benefits an organization

For many organizations, marketing data is very much an afterthought. Some find it too time-consuming or simply not relevant to their strategy.

There are many benefits to setting up an efficient data management system for businesses.

  • Identifying and targeting new markets to ensure growth. Data can help you identify new markets that you can target. This is especially useful if you want to enter a new market or grow your business.
  • Gaining insights into your audience to update your ideal customer profile—ICP. If you’re not reviewing your data regularly, you might be missing out on insights into your target audience. This information can be used to update your ICP, which in turn will help you create more targeted campaigns.
  • Creating a 360-degree view of your customer base. By reviewing your data on your customers, you can get a 360-degree view of them. This will help you create more personalized experiences for them and improve customer retention rates.
  • Targeting specific segments based on highly specific data. The more data you have, the more granular your segments can be. This allows you to target specific groups of people with focused campaigns that are more likely to convert.
  • Making sure compliance with stringent data privacy regulations. With data privacy regulations becoming more stringent, it’s important to have a data management system in place that ensures compliance.

Let’s now take a look at some of the problems with marketing data management.

What’s the problem with marketing data management?

The main problem with marketing data management is its complexity.

Marketers now have huge amounts of data from consumers to deal with. The problem they have is how do they make sense of it all? Two key areas make this a problem for marketers—data integration and limited data analysis resources.

Data integration

The first challenge is data integration. To make sense of the data, marketers need to be able to bring it all into one central location. This can be difficult as the data is often scattered across different departments and systems. It’s not just a case of putting all the data into one place but ensuring that it’s accurate and up-to-date.

It can be difficult to turn consumer data into actionable information. One common stumbling block is that marketers collect various metrics that may not be comparable immediately. It’s vital to normalize activities across campaigns from different sources so that marketers get a balanced view of their target audience.

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Limited data analysis resources

The second challenge is limited data analysis resources. Even if marketers can overcome the hurdle of integrating their data, they need to analyze it effectively. This can be a problem as most marketers don’t have the necessary resources or expertise to do this effectively.

This lack of resources can lead to inaccuracy which in turn can cause poor decision-making and missed opportunities.

Often data analysts spend far too long reviewing data. When you finally understand what the data tells you, it’s usually too late to use that information for the campaign you’re working on.

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Why is siloed data the biggest data management problem

So what’s siloed data.

Siloed data is data that’s isolated and not integrated with other data sets. This can happen for many reasons, such as different sources of data, departmental boundaries, different software applications, or even just incompatible formats.

Here are a few reasons why this is the most significant data management issue.

They give incomplete data sets

One of the main problems with siloed data is that, individually, they give incomplete data sets. This is because they only contain a subset of the total data that’s available. For example, if you wanted to compare the performance of your Facebook ad campaigns with your Google Ads, it’s a very manual effort if they’re not in the same location. Any manual effort is likely to lead to inaccurate results over time.

Data tends to be locked away in silos, preventing certain users from accessing it, some who could benefit from it might not even know about it. This prevents organizations from making decisions and strategies based on all the available data. This can lead to flawed decision-making.

Inconsistent data

Another issue with siloed data is that it can often be inconsistent. Each silo will have its own way of storing and organizing data. For example, one department may use different field names or codes than another department. This can make it difficult to join data from different silos together.

It’s also common for departments to have different definitions for the same terms. For example, one department may consider a customer to be someone who has made a purchase. And another department may consider a customer to be anyone who has interacted with the company, regardless of whether they have made a purchase. This inconsistency can lead to confusion and errors.

Duplicate data platforms and processes

Siloed data often leads to duplicate data platforms and processes. Each department or business unit will have its own data platform and process for managing its data.

This can lead to inefficiencies as there are now multiple platforms and processes that need to be maintained any time a change needs to be made. And every time there’s a change, you increase the likelihood of the two platforms ending up with inconsistent data.

Less collaboration between end-users

Siloed data can also lead to less collaboration between end-users. If each department in an organization has its own way of managing data, collaboration will be challenging. Siloed data can make it difficult for end-users in different departments to access and use each other’s data.

It’s far more difficult for department or team members to work effectively when there’s no shared access to the same data.

A silo mentality in departments

When data is siloed, it can lead to a silo mentality in departments. This is because each department will view its data as the most important. This can lead to a ‘us vs. them’ mentality, where departments are more concerned with defending their data than sharing it.

This silo mentality can be harmful to an organization, leading to a lack of trust and collaboration between teams. It can also make it difficult for an organization to make decisions that are in the company’s best interest as a whole.

Data security and regulatory compliance issues

Another issue with siloed data can often lead to data security and regulatory compliance issues. This is because each silo may have its own security procedures and policies.

If an organization has sensitive data, such as customer credit card information, this data must be properly secured. When data is siloed, it can be more difficult to secure as there are now multiple entry points. This can increase the risk of a data breach.

For the reasons above, siloed data can also make it more difficult for an organization to comply with regulations, such as the European Union’s General Data Protection Regulation—GDPR. 

There can be hefty fines for an organization that violates such regulatory bodies.

Reporting reliable marketing data effectively

One of the most frequently seen issues with siloed data is that it can make reporting marketing data effectively more difficult. This is because each silo will have its own way of tracking and reporting data.

Let’s take a look at four consequences of siloed data.

Standardized mapping and categorization are difficult

Different departments often have different category names for the same thing. This makes it difficult to map data from one silo to another. As a result, it can be difficult to make accurate reports that include data from multiple silos.

Let’s say you’re trying to report on a marketing campaign performance. But the data from the campaign is stored in two different places, each with its own categorization system. This makes it difficult to make an accurate report.

It’s true that you’ll need to do this mapping to centralize your data properly, but far better to do it once and do it well than to leave it up to individuals to map the data each time they need it.

Attribution is difficult

In marketing, attribution is a reporting strategy that allows sales teams and markets to consider the impact on a specific goal, for example, a customer purchase.

With siloed data, attribution is challenging because to attribute with accuracy, you need to see the data from all of the marketing channels that a customer interacts with. This is very difficult when the data isn’t centralized.

For example, a customer saw an ad on Facebook, clicked on it, and then made a purchase on your website. If Facebook and your website data are stored in different silos, it can be difficult to attribute the purchase to the Facebook ad.

Automated cross-channel reporting isn’t really possible

Organizations often have data stored in different silos for each channel. This makes it difficult to make reports that include data from multiple channels.

For example, let’s say you want to report on the performance of your marketing campaigns across all channels. If the data for each channel is stored in a different silo, you need to manually pull data from each silo and combine it into one report. This is time-consuming and error-prone.

Create trust with data governance

It’s critical for any organization to ensure that key data assets are managed formally. If critical business decisions are being made based on given data, there needs to be significant trust in that data. That’s where data governance comes in.

When it comes to creating trust with data governance, siloed data makes this challenging. Here’s why.

There’s no single source of truth

If there is no single source of truth, there may be different definitions for similar concepts. This makes it challenging to get accurate and reliable results.

Duplication of data and workload

If data is duplicated across different storage areas, your costs are multiplied. The cost of storing the data, the cost of any processing of that data, and above all, the maintenance effort costs when there are any changes.

Trust from users is lowered

When data is siloed, there can be different calculations used for similar definitions. This creates distrust from users who lose confidence in their understanding of the data.

Lack of control over personally identifiable information—PII

If data is siloed, it can be difficult to control and manage PII. This could have serious consequences if the data were to fall into the wrong hands.

A centralized data warehouse addresses these challenges by providing organizations with a single source of truth with all security measures in place.

No clear data lineage

With siloed data, it can be difficult to determine where the data came from and how it has been transformed.  This means when changes are made, it’s very hard to ensure you’re not impacting other data undesirably. It can also have legal implications if you can’t show how the data used in your business decisions was calculated.

The benefits of a centralized data warehouse

Hopefully, I’ve now convinced you there are plenty of challenges with siloed data. For these reasons, more organizations are moving from decentralized and siloed data systems to ones that are centralized—most commonly a data warehouse.

Let’s look at some of the benefits of a centralized data warehouse.

Centralized data access model visualization

Easy, fast access to your data

A centralized data warehouse gives you easy and fast access to your data whenever you need it. Storing all your data in one central location saves time and resources by avoiding accessing multiple siloes. 

What’s more, the performance of your reporting and data visualizations will also greatly improve when a data warehouse feeds them.

Historical access to your data

Another benefit of a centralized data warehouse is having historical access to your data. This means that you’ll be able to track changes and trends over time and make better business decisions.

If you’re just using the data directly in reports and not storing it in a centralized data warehouse, data won’t be available for other purposes in the future.

Improved data integrity and security

With a centralized data warehouse, you can focus your security on this single asset. You can more easily monitor who has access to the data and keep track of activity.

In addition, with a centralized data warehouse, data integrity is improved. As all of your data is in one place, you can more easily ensure data is accurate and up-to-date.

Reduced cost

A centralized data warehouse can also help reduce costs. Organizations often need to invest in different hardware and software to support each platform when data is siloed. This can quickly add up and become very expensive.

A centralized data warehouse will save you money in the long run by reducing the need for multiple platforms.

Improved decision-making

A centralized data warehouse gives organizations improved decision-making capabilities. As all the data is stored in one place, it’s easy to make reports and conduct analyses. This means you’ll be able to make better-informed decisions for your business.

Data centralization to solve your siloed data problem

We’ve already covered quite a bit about the various challenges of siloed data and the benefits of a centralized data warehouse. By now, it should be clear that data centralization solves the various problems and challenges associated with siloed data.

Data is one of the most fundamental assets that any business can have. So, it needs to be managed efficiently and effectively to ensure that all data-driven decision-making is in the organization’s best interests. 

That’s why so many businesses invest in a centralized data warehouse.

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About the author

Lee has over twenty years’ experience immersed in data. Starting as a developer-consultant focussed on ETL and specializing in data warehouses, he transitioned through roles in data architecture, solution design, and people leadership that fueled a passion for mentoring in data-centric thinking. Lee is from Australia, has worked in New Zealand and the UK, and now lives in Finland. He’s a Senior Sales Engineer at Supermetrics, where he helps customers access their marketing data quickly and easily.

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