In a recent Dun & Bradstreet/Forrester Consulting report, 72% of firms said managing multiple CRM systems across geographies and technology silos is moderately to extremely challenging, and that “… the greatest challenge organizations face in meeting their marketing and sales objectives is managing data and sharing insights that drive actions across organizational silos.”
So, why are data silos such a problem?
What is a data silo?
A data silo (or information silo) is a repository of information in a department or an application that is not easily or fully accessible by other departments or applications. Marketing, sales, HR, and other departments rely on specific information to function, and those collections of often overlapping-but-inconsistent data are in separate silos. This can result in duplicate and inconsistent records, and in a major drag on efficiency.
Imagine, for instance, that a marketing department runs its automated marketing campaigns through Marketo, while the sales department maintains its customer data in Salesforce. Without proper integration, the marketing outreach data is isolated within the marketing department, the sales information is also siloed, and it’s difficult or impossible to attribute sales to a particular marketing campaign. When data is siloed, you don’t have a 360-degree view of your customers and prospects.
What causes data silos?
Data silos arise naturally as organizations grow and factors such as technology, company culture, and organizational processes limit or discourage the sharing of information. Furthermore, management may — intentionally or not — foster a competitive environment among departments, which can result in duplicative efforts and insulation of data and information flow.
These are three common causes of data and information silos within an organization:
1. Multiple apps and sources of data
Many organizations rely on SaaS applications to manage core processes, and many of those applications don’t directly integrate with one another.
2. Company growth
When a company grows quickly, infrastructure and processes often fail to scale, and individual departments may implement processes and applications in an ad hoc fashion. This produces data assets that are usable only by the teams that produced them, as well as a backlog of cleanup and integration work for data managers and IT.
3. Organizational structures
Bureaucracy and restrictive access control systems and permissioning schemes can add to the burden of sharing data, and often no one is in charge of making data shareable company-wide. A person may be in charge of each individual application, but when there’s no mandate to make people work together for the greater benefit of the organization or create system-wide collaborative processes, it just doesn’t happen.
The costs of data silos
The direct costs of data silos within an organization may not be clear; however, there are numerous ways that data silos waste company resources and reduce employee efficiency.
InformationWeek lists five ways in which silos hurt organizations:
- Slow data-driven decision-making, which can affect a company’s ability to efficiently compete
- Poor trust and collaboration across teams within a company
- Higher costs due to redundant IT and application infrastructure
- Reduced data quality, which hinders the ability to leverage data analytics
- A poor experience across the end-to-end customer journey
Data silos often maintain duplicate data when multiple departments track the same information. This presents the risk of someone using data that’s not sourced properly or data that no one knows the origin of. Also, if someone is unable to reconcile multiple versions of data, duplications can cause logical errors during transformation or analysis.
Data silos also produce incomplete views of essential business information. For instance, a customer profile could be segmented across multiple data silos; think of a shopper whose personal and purchase data resides in a point-of-sale system, a mobile app, and a SaaS marketing platform. If data is siloed in each system, that isolation keep the organization from seeing a holistic view of that shopper and his preferences. Integration of the data in all of the systems would present an informed picture of the shopper and would help the organization to make better sales and marketing decisions.
Finally, the problems caused by data silos are compounded as people either begin to accept the isolation of work or try to find workarounds that become complex, difficult to maintain, and likely to yield incorrect information. This produces a negative feedback loop in which processes worsen over time.
The benefits of busting data silos
When data is shared across an organization, departmental decision-makers can use business intelligence (BI) tools for insight into improving operations, improving products, saving money, and discovering hidden opportunities. By assembling and analyzing data from multiple sources through data integration, business leaders can discover trends and patterns that might have been impossible to find before.
How to break down data silos
Data silos don’t resolve themselves without top-down change. Management should encourage cross-team collaboration and reduce unnecessary interdepartmental competition. Employee incentives should align with the overall company vision rather than on an individual or departmental basis. However, team management changes alone won’t solve the problem.
You may choose to deal with data silos through application integration, which is the process of enabling independently designed applications to work together. One way to do this is to manually program interactions between two or more applications. When there are dozens of applications, a better approach is the use of middleware — software that sits between the front-end request and the back-end resource.
Another way to break down data silos is with data integration, which is the process of replicating data from databases, SaaS platforms, and other sources into a single centralized repository, such as a data warehouse, where it can be shared and analyzed across the company. If you need to make all of your data available for analytics and BI systems, data integration is the best way to deal with data silos.
Getting started with data integration
You’ll need an ETL solution to replicate data from your sources into a data warehouse. ETL refers to the process of extracting, transforming, and loading data to an analytics platform, such as a data warehouse. It’s a necessary process if you want to optimize your data for analytics. You can build your own or use a third-party tool to do the job.
Building your own data integration solution may seem tempting if you’re facing a small project with few data sources. However, writing ETL code requires no small amount of software development expertise, and adds another in-house application that needs ongoing maintenance.
It doesn’t have to be this way. Stitch is a cloud ETL service that provides connectors from more than 100 data sources to the most popular data warehouse destinations. We make it easy to extract data and load it to a data warehouse. Our approach is simple, straightforward, and ready to go right out of the box. Give Stitch a try, on us — set up a free trial and start busting your data silos today.