How Data Silos Are Killing Your Digital Marketing Analytics Success

Digital Marketing AnalyticsIn theory, every manager uses digital marketing analytics for data-based decision making. If you can move from driving business decisions based on hunches to tangible information, your chances of growing your business while improving your digital marketing efforts.

Unfortunately, that theoretical benefit doesn’t tend to play out nearly as seamlessly in reality. Even when decisions are backed by data, that data may not actually be complete or accurate enough to result in accurate choices and business direction. Too often, the data is available, but not built in a way to actually make good decisions.

The reason: data silos. Every business has them, and every business sooner or later comes to the same conclusions: data silos are killing your marketing analytics success. How does that happen, and what can you do?

Understanding Data Silos And Their Effect On Digital Marketing Analytics

You probably already know the core definition of data silos, which describes any tangible information not available to the entirety of the organization. Through a separate data base or application, this data is collected in isolation, unavailable for anyone but its immediate users.

Every organization has to contend with data silos. On average, even a small business deploys more than 14 applications, all collecting their own data. And the resulting lack of effectiveness is a common problem: on average, more than 80 percent of marketersΒ say that silos within their efforts obscure a perfect view of their promotional success.

The ideal scenario for any organization is perfectly integrated data.Β  Marketing and sales are able to accurately track a customer from first touch to the end of their customer lifetime. The result is more informed decision making, optimizing your budget while maximizing message effectiveness.

Data silos, however, make that ideal scenario difficult to impossible. That’s why it’s so important to not just understand how they occur, but also how you can eliminate them to increase your analytics success.

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How Data Silos Tend to Occur

No less than 80 percent of the work involved in data science includes acquiring and preparing data. Stated differently, data-based decision making is actually the end point of a long process that ensures accuracy of the information used in the process. Because data silos insert themselves into the process early on, they can be devastating in actually using analytics to drive your business forward. In the Harvard Business Review, data scientist Edd Wilder James outlines four major reasons for the existence of silos in most organizations:

1. Structural.

Applications tend to be optimized for their major function. Resources are limited, so the development doesn’t tend to include data sharing or integration capability as a core driver. As a result, data gets isolated within the application, with little ability to get out.

2. Political.

Every organization consists of various subcultures that, even as they aspire to drive the company forward, have their own goals in mind. Naturally, these groups tend to be suspicious of external use of their data. This sense of proprietorship and fear of misuse may be understandable, but is a core block in trying to accomplish data integration.

3. Growth.

We tend to look at business growth as positive, and rightfully so. But it can also help build accidental silos that can be difficult to break down. Different leadership, employees, and acquisitions will result in a multitude of applications and management philosophies, all with different approaches to gathering and distributing data.

4. Vendor lock-in.

As soon as you begin working with external vendors, you might have a silo problem on your hands. Vendors have a reason to keep the data to themselves, holding their clients to their own platform whenever possible. Once you begin working with multiple vendors or look to establish your own supplemental capabilities, this ‘data hoarding’ can easily turn into a silo.

All of these scenarios occur in the vast majority of organizations, and sometimes more than once. The results, if not addressed, could be devastating to your analytics efforts.

A perfect case study for the dangers of data silos is the ever-growing marketing technology environment. Here, more than 5,000 applications (from social media monitoring to marketing automation) fight for attention with specialized solutions that range from building a content calendar to sending marketing emails. Because they tend to be highly specialized, the average business now deploys 16 MarTech solutions in improving its digital marketing and sales efforts.

Digital Marketing Analytics

Each of these solutions collects data. But none of them communicate with each other. On Forbes.com, Larry Myler sums up the problem with this approach:

“Each platform was built with the idea that their insights and data are the most important. Therefore, you end up wasting a lot of time bouncing from report to report in each platform in order to try and piece together the whole story. The odds are high that you’re missing critical pieces of the story.”

It’s no surprise, then, that the research firm Gartner estimates a lack of data transparency costs your business 10 percent in annual revenue. In reality, that estimate is probably conservative. The more isolated your data, the more drastic the financial opportunity cost for your business will be.

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How You Can Fight Against Silos for Better Analytics Success

The common occurrence of data silos paints a bleak picture for organizations across industries. Even if you recognize the importance of using data to make strategic business decisions, how can you succeed if you know that the information you’re using to make these decisions is not accurate or incomplete?

Fortunately, you can take several steps to reduce and minimize the dangers of data silos. To get started, consider taking the below three steps to improve your analytics success.

1. Change the Data Culture

Your first step has to be strategic in nature. Before you can implement technical steps to accomplish better data sharing, you have to make sure that the entities holding your data will actually embrace these efforts.

Depending on your business operations, these entities may be external (such as vendors) or internal. In either case, it makes sense to highlight exactly what each unit has to benefit from a better data-sharing culture. Encourage cross-departmental collaboration and frequent status updates as a first step in making that culture change.

2. Build a Better Infrastructure

Next, it makes sense to become more strategic about the data infrastructure in place at your organization today. Partner only with vendors who are willing to work on your platform, or share their data with it. Build applications with data sharing in mind. Consolidate your databases into a singular, centralized platform.

3. Embrace Integrated Dashboarding

With an improved data infrastructure in place, your final step should be working with a customized dashboarding system. This system can pull together information from all potential sources and visualizes them for each entity in your business.

In many ways, this step is your key to ending data silos. It allows you to not just centralize your reporting output, but also get the various units on board with sharing the data to begin with. If they can visualize their data as part of a greater whole, their motivation to end their own silos will increase drastically.

Left unattended, data silos can be a devastating road block to analytics and business success. Recognizing them allows you to take the appropriate measures to integrate your data, and drive your business forward.

Done the right way, analytics are and should be a crucial part of your digital marketing output. But too often, data silos prevent that from happening. How can you more effectively gather, integrate, and analyze your data to improve your marketing output? Contact us to learn more. We specialize in optimizing your digital marketing analytics capabilities, so you can see a return in your organisation.

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