Editorial
There’s a lot of buzz about the value of a 360-degree customer view, yet few companies have been able to achieve it. According to Gartner, fewer than 10 percent of businesses have this holistic view.
There’s a good reason for that: Bringing data together across disparate silos can be a difficult and time-consuming process. Even experienced IT professionals face hurdles.
Why You Should Bring Data Together
With all the work involved, you might ask, “Why am I doing this in the first place?” Let’s start there, because the value of bridging data silos shouldn’t be underestimated.
Here are some practical ways that consolidating data to enable a 360-degree customer view will help your business.
It orients the organization around solving problems: Data silos mean that different departments or groups, such as sales, marketing, customer support and finance, have their own data sets to analyze.
So, when they use their own data to answer organizational questions (such as “Why is this happening?” or “What do we need to do to reverse this trend?”), people from different groups waste time debating the quality of the data instead of creating solutions.
Dismantling data silos can be the foundation for a cultural shift. So that when the tough questions arise, there’s organizationwide trust in the data. With centralized data, company leaders can spend time creating solutions and solving the important problems.
It connects investments to outcomes: Companies invest to grow their businesses. But if the systems that track those investments aren’t, for example, linked to the systems that track sales and other business outcomes, how do you know what’s driving growth?
Marketing departments need to close the loop to understand which investments are generating leads that convert to sales. But that’s not the only use case. Organizations should also integrate data to connect the dots between business outcomes and investments in product management, account management and customer support to understand what key activities and milestones are leading to success.
You’ll improve the customer experience: Let’s face it: It’s all about the customer experience. And data silos create a fragmented customer experience. With an increasing number of customer touchpoints, the problem will surely get worse before it gets better.
A shared data resource is critical to ensuring that your people and teams are engaging with customers in a way that reflects all of their interactions with your business. Is sales aware of a particular customer’s support ticket? Does marketing know that the customer expressed interest in a certain product? Does the website chat system know who the customer is?
Unified customer data creates cohesiveness from the all-important customer perspective across all of those touchpoints.
It enables improved compliance: There’s an increasing number of rules and regulations that make centralizing customer data a sound strategy. For example, the EU’s General Data Protection Regulation (GDPR) is set to take effect in May, and companies that do not comply face fines equal to as much as 4 percent of their revenue.
Companies need to take steps to identify all systems that house customer data and ensure that there are processes and protocols in place to meet the requirements for managing that personal data. Connecting applications to a centralized data warehouse helps solidify and automate these processes.
Why Is Centralizing Customer Data So Hard?
So, it’s clear that centralizing customer data offers significant benefits. But how do we make the process faster and easier?
Here’s a look at six key challenges that you could face in your efforts to eliminate silos and centralize customer data, along with tips for overcoming them.
1. Duplicates Within a Single Data Silo
Sometimes data-silo-busting projects hit an immediate roadblock because the owner of a system says, “Our data is too messy to bring it in to a central system. We have too many duplicates.”
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Tip: De-duplicate within each data silo first, to ensure that you do not bring duplicates into the consolidated data set. To help the project build momentum, bring in any nonduplicate data first and flag duplicates for cleanup later.
2. No Common Identifier Across Data Silos
When consolidating multiple data sets, you need to take records across systems and match together the like records. This is often easier said than done.
Tip: When matching contacts, start with an email address, because it provides the highest probability for a unique match across systems. Then, to get more advanced, set up multi-level de-dupe keys that incorporate additional supporting data, such as name, company and address.
3. Conflicts Within the Data
The good news: You have matched records across multiple systems. The bad news: The data in those two systems is conflicting. What do you do?
Tip: Since a “by hand” review of data won’t be practical for most companies, I recommend one of two automated approaches, or a combination.
First, there’s the “system of record” approach, which takes a prioritized ranking of systems to determine — for a specific type of data — which system should win out in a conflict. For example, the CRM system would be the system of record for any data related to a sales opportunity.
Alternatively, there’s the “most recently updated” approach, wherein you use the most recently updated data across systems for a given field. So, if the customer phone number in the CRM is different from the one in the support system, whichever has been updated most recently would win out.
4. Inconsistent Data Formats
When combining data across systems, you should expect to encounter inconsistent data formats, such as different versions of date formats or differing picklist values for fields such as states or countries.
Tip: Take a two-pronged approach. First, ensure that you have a process in place to get data into a consistent format, and then take the time to go back to the originating systems and create consistency around how data is input and managed. Create consistency in picklists and field formatting across your systems to reduce the amount of normalization that will need to be done to the data on an ongoing basis.
5. Related Objects
Consolidating your data involves more than just bringing together a unified contact record. Remember that those contacts have relationships to other objects, and you need to maintain those object relationships because you will want access to this type of data for your reporting.
Tip: Start by ensuring that your centralized system has definitions and support for all of the key objects that you carry over. Typical objects include contact information, accounts, opportunities, tasks/activities, tickets, campaigns/programs, products, orders and users.
6. Data Is Changing ...
Data is forever changing. If a data centralization project takes too much time, the centralized consolidated data set could be out of sync with the data in the silos by the time you’re finished.
Tip: Work toward a process whereby there’s an ongoing automated way to extract, clean and combine data across silos, instead of relying on one-time cleanup projects. Otherwise you’ll never have up-to-date, integrated data.
No doubt, there’s a lot to this. But it doesn’t have to be tedious and time-consuming — and you don’t need to start from scratch. There are systems and tools available that can help automate the process.
And don’t forget that a data centralization project has payoffs — chief among them the ability to use data analytics in a meaningful way.
It is widely accepted that data analysis is the key to success in today’s business world, but companies need accurate and comprehensive data to fuel their analytics projects. Data is quickly becoming every company’s most valuable asset, and to get the most value out of that asset, you have to be able to share and integrate it across applications, groups and departments in your organization.
A unified source for customer information can serve as a building block for success. It not only provides a better customer experience and enables GDPR compliance — the growth of your company may rely on it.
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