What are data silos, and how can you eliminate them?
Imagine you’re presenting the latest quarterly report to your entire department. You and your team would like to propose a new marketing campaign and have spent the last week researching data points that support the need for this new initiative. As you’re going through each data point and building your case, someone from a different team says, “Excuse me, but according to our analytics, your data is incorrect.” Suddenly, what you hoped would be a clean proposal is turning into a debate as each team tries to find the true data points. And now your manager no longer feels secure moving forward with this campaign.
If your team shares data that regularly conflicts with that of other teams, there’s a strong chance that data silos exist in your organization. Other signs could include your team members not knowing what metrics you need to track or finding it next to impossible to find the data you need to make important business decisions. In all of these cases, data silos are a point of friction that can prevent making informed decisions in many businesses.
What are data silos?
A data silo is a repository of data that’s controlled by one department or business unit and is isolated from the rest of an organization. Siloed data is typically stored in a standalone system and often is incompatible with other data sets.
In some cases, data silos may come from technical issues like a tech stack that doesn’t communicate properly. Applications might not be used or designed to cross-reference or add to each other. One department may not have access to an application from another department. If data isn’t easy to find and use or can’t be trusted, it isn’t adding value to an organization. To become truly data-driven, organizations need to break down these barriers and provide decision-makers with a 360-degree view of data that’s relevant.
The reasons data silos exist
Let’s go back to our hypothetical company meeting. Another department has called into question your data by showing contradictory reporting from their analytics. You find this especially frustrating because you didn’t have access to their analytics while you were putting together your report. After pulling up your source, you realize that you’re both somehow measuring the same metric, but your tools are giving you different numbers. How does this happen?
As a company grows, the likelihood of data silos grows with it. New departments or teams with specialized employees create spaces where these silos exist. Although growth is the condition that creates data silos, there are usually two reasons these silos appear.
Sometimes data silos are human creations. A silo mentality is exemplified by departments that are reluctant to share information with other departments at a company. This can occur because company divisions are narrowly focused on their own goals and don’t see the need to collaborate between departments or work toward a company-wide vision.
Because silo mentality is a personnel issue, it needs to be broken down by people. We’ll discuss how technology can both create and solve data silos, but in every case it’s important for all teams involved to work together. We see customers are most successful when they incorporate a center of excellence into their data management strategy.
Technology that isn’t integrated
New technologies are adopted for different teams as a company grows. Each system stores customer data for its own purposes. In many cases, duplicate data is stored in multiple systems, but silos arise because these systems don’t talk to each other. These systems will start to develop different audience definitions (what counts as a lead versus what counts as a contact, and so on), which creates poor user experiences and technological redundancies.
- An email team uses an email service provider to build customer audiences, create campaigns, and trigger retargeting based on site views. Email engagement is tracked and used to inform future campaign qualification.
- A parallel media team uses a demand-side platform and other buying platforms to build customer audiences, create campaigns, and do retargeting based on site views. Paid media engagement is tracked on each platform and used to inform future experiences within those platforms.
- In many cases, there are efficiencies to be gained if these teams worked from the same audience definitions, which would also result in better customer experiences due to consistent messaging across channels.
Technical data silos propagate over time as new technologies are acquired to address new use cases.
The problems caused by data silos
It’s obvious that not having full access to data is problematic for decision-makers, but what specific issues do data silos cause? Since breaking up data silos requires the collaboration of multiple departments and managers, it’s important to have specific examples and arguments in your pocket when this discussion comes up.
Wasted resources and poor productivity
Because each team has its own “source of truth” that it’s comfortable with, data silos can make it difficult for different teams to collaborate. In our meeting example, a quarterly touch-base meeting got derailed because two departments had different datasets, wasting the time of everyone present. Siloed teams will continue to create and optimize initiatives based on the subset of data available in the system they’re working with. If someone wants to put together a complete view of company data, they must chase down that data from each silo and then work to assemble the data from fragmented cases by eliminating duplicates and fixing contradictory information.
Lessen the accuracy and integrity of your data
An isolated data silo will age and become more inaccurate over time. In other cases, data silos can have an incomplete view of a dataset. Different systems need to talk to each other so that data, such as contact information or campaign history, can stay accurate and consistent.
Limited access to data
With limited access to data, team members can become confused about the company’s mission and goals. Management may give them tasks that don’t make sense with the incomplete information they have. Or worse, teams can be given poor directives based on the limited information available to their managers.
How to break down data silos
1. Put the right tools in place
In order to have a single source of truth, you’ll need to ingest data from across the organization and build a unified view of individual customers. There are a variety of systems that help with this effort:
- Data lake. A data lake stores all your data in its raw form. These can be as simple as a CSV file, but they don’t give you a lot of flexibility with your data.
- Customer relationship management (CRM) systems. CRMs are generally cloud-based software suites designed to administer all your customer data. However, a constant challenge with CRMs is ensuring all the data is accurate and translating that data into an action plan.
- Master data management (MDM). MDMs unify all data into an official master data asset. Though this can solve the data silo problem, it usually has a latency of 24 hours or more before your data becomes actionable.
- Customer data platform (CDP). A CDP collects customer data from various sources in your tech stack to build unified profiles for insights and personalized customer experiences. A key difference between a CDP and the other systems listed here is the ability to connect to downstream systems of activation (for example, onsite personalization, paid media, email, call center, and so on), and to serve the needs of marketing use cases as well as other teams within the organization.
Having a single source of truth for the company is essential for unified data. A CDP runs complementary to data lakes, CRMs, and MDM systems by taking data from those systems and combining it without other sources. CDPs can then make that data actionable for all other systems so that they can deliver personalized messages.
2. Integrate and unify your data
Once your CDP is installed, you must ensure that all your applications are talking to each other by having your data integrated and unified. You will want to create a schema that makes sure data is being mapped correctly as it’s imported so that you have minimal clean-up work in the future.
Many of the systems listed above will have trouble integrating with a full tech stack, and full integration will likely require quite a bit of work. The big advantage of a CDP is that it breaks down silos by bringing data together, but you will want to make sure your CDP can cleanly integrate with your tech stack.
3. Enhance your data governance
Now that there’s a lot of data running in and out of your CDP from various sources, you’ll want to set up some governance on how all that data is going to be used. By law, some data can’t be used on certain channels or in specific locations, while other data usage, though legal, might cross the line from effective to inappropriate marketing.
Organizations should look for productized data governance capabilities that allow them to market responsibly. These capabilities should clearly label what data sources are barred from being used in specific cases and include a system that prevents improper data usage.
Another aspect might also come down to which teams have access to certain types of data, as some customer data may contain sensitive, personally identifiable information. This restriction can be done based on teams, roles, or regions.
Remember, building trust and loyalty with your customers begins with responsible marketing.
4. Choose applications wisely
When selecting a CDP, it’s important that it has integration capabilities so that it can connect to your other applications — both inputs and outputs. If the integration you need is not built in, check and see if it’s possible to request a new integration or create a custom one. Having everything properly integrated is essential to truly break down data silos.
As you add to your tech stack in the future, you’ll want to make sure that you can integrate new applications into your CDP. Otherwise, you’ll just be creating new data silos.
When choosing a CDP, you’ll want to make sure it fits all of your needs and can properly future-proof your data. We recommend going through this checklist to help you invest in a CDP you won’t outgrow.
How Adobe Real-Time Customer Data Platform can help
With Adobe Real-Time Customer Data Platform, you can feel secure knowing that your technology can bring your disconnected data to life, setting you and your company up for limitless evolution. Forge lasting bonds with customers through respectful, real-time, and personalized experiences, thanks to customer and account profiles intelligently powered by Adobe Experience Platform.
And because Real-Time CDP is built on Experience Platform, it effortlessly integrates with your existing technology, as well as other Adobe solutions, to create the most comprehensive tech stack available.
The real-time nature of this platform allows for data to be processed and activated the moment it’s entered. A potential customer could abandon their cart and hop over to a social network and immediately have relevant banner ads served to them. Or a contact could purchase something at one of your brick-and-mortar stores, which would trigger Real-Time CDP to tell your marketing automation software to pause remarketing ads for this contact and move them to a recent customer segmentation.
Additionally, Real-Time CDP is the only CDP designed to support both B2C and B2B businesses simultaneously. Real-Time CDP supports personal, account, or hybrid customer profiles to give you unified, actionable data across your entire business.
If you would like to learn more about why CDPs are essential to breaking down data silos and building great customer experiences, watch Creating a Roadmap for CDP Selection and Success, a session from Adobe Summit 2022.
Removing data silos requires a mix of technology and tenacity. With this framework and the help of Adobe Real-Time CDP, you can feel secure in the accuracy of all your data and create lasting bonds with your customers with immediate and personalized interactions.