Mastering Data Requires Attention to Detail

Originally published on the destination CRM blog.

13 minute read

Nowadays, every company wants to service its customers more effectively. And customers expect no less from the companies with which they do business. They see one company, and they expect all employees to respond to them in a consistent, cohesive manner.

But businesses are broken down into departments that provide different services during the customer journey: marketing, sales, product development, product delivery, and post-sales support. Corporations have hundreds, thousands, or even hundreds of thousands of employees performing various tasks.

Adding to the complexity, customer information is stored in so many different applications that wide gaps exist among data sources. Bridging those gaps so every employee in the organization has a consistent view of clients is possible. But the task requires large investments of money and manpower and sweeping process changes, steps that most organizations have not been willing to make thus far.

It’s not an easy task, but it is getting simpler, particularly as a wide and growing variety of applications emerge. Vendors are now building solutions to streamline workflows for employees inputting data or responding to various triggers, like customers calling in with a problem.


One difficulty that still remains is that these various applications collect customer information in different ways. “CRM solutions focus on process management and not on data management,” says Bill O’Kane, research vice president for data and analytics at Gartner.

Consequently, customer data is entered into numerous autonomous systems that were not designed to talk to one another. Client data is housed one way in a sales application, another way in an inventory system, and yet another way in contact center systems.

Other organizational factors further splinter the data, which can vary depending on the products in which a customer is interested, where the product resides, and who (the company or a partner) delivers it.

In addition, information is entered in various ways, including manually, either by the customer or an employee, or via voice recognition. And applications store the information in unique ways. One system might limit the field for customers’ last names to 16 characters while another could allow for 64 characters.


The challenge is further exacerbated by software design and vendors’ focus. CRM vendors concentrate on adding application features and do not spend as much time on data quality. “If they want to, customers can input their personal information 10 different ways,” O’Kane says. Most applications do not check for duplication when new customer information is entered.

And then human error creates additional problems. “No employees are compensated based on data hygiene,” says Allen Pogorzelski, vice president of marketing at data orchestration platform provider Openprise, who points out that these same employees are often quite busy, move frequently and quickly from one task to the next, and, consequently, sometimes do not follow best practices fully.

All of this leaves applications resembling a series of landscape paintings, where the basic customer outlines are similar but the hues and brushstrokes are unique in each system. “CRM data is very fractured,” says Michele Goetz, a principal analyst at Forrester Research. “While CRM is often considered the system of record for customer data, other capabilities—from marketing automation, [enterprise resource planning], commerce, databases, and master data hubs to identity resolution and permissioning systems—also operate as the source of truth for various customer activities.”

It also doesn’t help that companies have dozens and sometimes hundreds of systems that identify a person, all with their own set of identifiers, like name, address, email, telephone number, social media handle, and purchasing history. The data features a tremendous amount of duplication, inconsistencies, and inefficiencies.

The inconsistencies exist because fixing such problems is a monumental task, one that requires companies to tackle both technical and organizational issues. Master data management (MDM) solutions, which have been sold for decades, are designed to address the technical issues. They are built to clean up the various inconsistencies, a process dubbed data cleansing.

The work sounds straightforward, but it is time-consuming and excruciatingly complex. The business has to audit all of its applications and determine what is stored where and how it is formatted. In many cases, businesses work with terabytes and petabytes of information. Usually, they find many more sources than initially anticipated because cloud and other recent changes enable departments to set up their own data lakes.


Cleansing starts with mundane tasks, like identifying and fixing typos. The MDM solution might also identify where necessary information is missing.


To start the process, companies need to normalize fields and field values and develop standard naming conventions.

Such work is time-consuming, and with good reason: While the computer industry has developed standards in many areas, none exist for MDM. “I am skeptical of data formatting standards because each business uses information in unique ways,” says Eric Melcher, vice president of product management at Profisee, an MDM platform provider. He also points out that the sheer volume of data, the new ways that companies find to use it, and the complexity of business applications make moving targets out of any potential standards.

However, the data clean-up process can be streamlined in a few ways. If a company chooses only one vendor to supply all of its applications, the chances of data having a more consistent format increase. Typically, vendors use the same formats for all of their solutions. In some cases, they include add-on modules to help customers harmonize their data.

But that is not typically the case. Most companies purchase software from different suppliers, and data cleaning has largely been done in an ad hoc fashion, with companies harmonizing information application by application. Recognizing the need for better integration, suppliers sometimes include MDM links to popular systems, like Salesforce Sales Cloud, Microsoft Dynamics, and Marketo. The chances of finding links to other relevant applications, such as ERP, product life cycle management, and billing systems, are remote.

The work is also labor-intensive, and many MDM projects can take years to finish. “There is a heightened need to curate (classify, tag, audit, etc.) data at scale as our information ecosystems grow and federate,” says Forrester’s Goetz. “Manual efforts are inadequate.”

Artificial intelligence and machine learning are emerging to help companies grapple with such issues, but the work is still in the very early stages of development.

Still other challenges stem from internal company policies—or a lack thereof—and corporate politics. Businesses need to step back from their traditional departmental views of data and create an enterprise-wide architecture. They must understand data hierarchies and dependencies; develop a data governance policy; ensure that all departments understand and follow that policy; and assign data stewards to promote it.

But such organizational change often encounters problems right at the start. The simple question of who is responsible for cleaning the data often yields a convoluted answer.

With the advent of cloud technology several years ago, many business units gained autonomy over their departmental applications. The relationship between these groups and IT has sometimes been strained. The latter’s objectives to keep infrastructure costs low and to put central policies in place to create data consistency often conflict with the business unit’s drivers, which largely center on responding to customer demands ASAP. And while departments have taken more control over the data, they often lack the technical skills to manage it on their own.


Resistance to MDM has been common for a number of other reasons. For starters, departments often lack the budget and sometimes the interest to commit needed resources to MDM projects, according to Gartner’s O’Kane.

And because the technology and organizational barriers have been so high, many MDM projects have failed. “MDM has a negative connotation because it is viewed like a typical IT project—long and expensive,” Openprise’s Pogorzelski adds.

MDM solutions have also been expensive, costing hundreds of thousands if not millions of dollars. The cost involves not only the software but also a lot of custom integration work. Typically organizations spend $1 or $2 on services for every software licensing dollar.

With costs that high, expenses are often spread out over several budget cycles, leaving projects scheduled to run for years. The reality is that many of them never end. And unlike other applications that hum along once the software has been installed, MDM projects continue to require significant tuning because businesses develop new marketing and customer service plans, new data constantly enters the pipeline, and mergers and acquisitions create data overlap.

A number of vendors, including ActionIQ, IBM, Informatica, Openprise, Orchestra, Profisee, Reltio, SAP, SAS Institute, Syncsort, Teradata, and Tibco, sell MDM solutions. Even though these products have been available for decades, the market is relatively small and the growth has been tepid. Enterprises spent $1.4 billion on these systems in 2017, up from $1.3 billion the previous year, according to Gartner. Openprise avoids the term “MDM” and uses “data orchestration” instead because it views the former negatively.

All hope is not lost, though. A few changes, including new cloud-based MDM tools that ease the integration work and lower project costs, could boost MDM adoption.


Also, businesses are starting to learn from their past mistakes. “If a company wants to clean up all of its data and make it perfect, then they are setting themselves up for failure,” says Tasso Argyros, CEO and founder of ActionIQ. “They need to be pragmatic, focus on a small area, and try to make their data cleaner but not necessarily perfect.”

In such cases, the potential benefits are significant. Lead scoring becomes more efficient as firms are better able to correctly identify potential customers. They identify high-value clients more easily and better manage cross-selling and upselling. The customer service team has more information on hand when interacting with clients.

One success story is Medallia, which has been in business for 15 years. The company helps large organizations, like Paypal and Tory Burch, improve their customer experiences.

Raimondo Murari joined the company a year ago as marketing operations director, and his department focuses on using data analytics to extend its own sales reach. The Medallia marketing team collects and consolidates information from half a dozen marketing applications. Generating needed reports was challenging; the problem was data quality. “It was like having a nice sports car but poor roads to drive on,” Murari explains.

Throughout the year, the Medallia sales and marketing teams hold small informational dinners for prospects, but creating lists of potential attendees for certain cities was daunting.

So when Murari heard about Openprise, he decided to give its product a test run. “We wanted a tool that would be easy to use,” he says.

Medallia worked with Openprise to improve marketing data quality. As a result, the Medallia marketing team was able to identify the metropolitan areas of its prospects so that it could identify more prospects for its field events and eliminate much of the dirty data. The project took only a few months to complete and did not have much overhead. Because of the success, the marketing firm plans to extend its use of Openprise to other applications.

Most companies want their prospect data to be cleaner, but because of the way that systems are designed, most firms create dirty data. Like Medallia, they can take steps to improve their data quality, but such work is not simple. “MDM benefits are possible, but the process requires a long period of soul searching and cultural changes for customers as well as vendors,” Gartner’s O’Kane concludes.


Paul Korzeniowski is a freelance writer who specializes in technology issues. He has been covering CRM issues for more than two decades, is based in Sudbury, Mass., and can be reached at or on Twitter at #PaulKorzeniowski.