Data Trust Suffers When Your Comms Stack and Your Rev Stack Don’t Talk to Each Other

Shopping for a new (or improved) enterprise revenue data integration solution is no small task. Because the choice drives the connection between—and access to—the data lake housing enterprise communications and revenue data, the decision will profoundly impact data governance, completeness, and quality that inform data trust—for better or worse. That’s why it’s worthwhile to think through the challenges your enterprise will face when key platforms are disconnected (or ill-connected), and stakeholders can’t trust the data they use to build customer relationships and close sales. 
Poor data quality costs the average US enterprise $15 million

In this first installment of a two-part post, we’ve compiled a list of five primary data trust challenges to keep in mind when shopping for the right revenue data solution: 

  1. Your platforms don’t talk to each other and you are relying on end-users for data entry
    CRM, email, chat, and other communications platforms do what they do very well—and they continue to improve. But because end-users are consistently interacting with customers and prospects in calendars, emails, and elsewhere outside of CRM, you have a critical gap to address. Why? Because the data in these platform-specific silos don’t automatically connect. As a result, users who live in CRM have access to revenue data gathered via CRM—and users who live in Outlook/Exchange and Google Workspace have access to data collected via those platforms. While some enterprises mandate manual data migration from one platform to the other, that approach is a risky half-step prone to errors, omissions, and duplication—resulting in low-quality data users can’t trust. We’ll go deeper into those issues below.
  2. Your end-users are not particularly good at data entry—and it takes them away from customers
    In addition to the data quality and completeness issues associated with manual transcription of customer data from CRM to other communications platforms—and vice versa—the practice is woefully inefficient. In an enterprise environment, even the most minor inefficiencies are compounded by the volume of each user’s customer interactions and the sheer number of users routinely performing manual entry tasks. When 500 users interact with 20 customers daily, as few as 12 minutes of redundant data entry adds up to more than 500 wasted hours each week—time better spent in conversation with customers.
  3. Your users’ data entry mistakes are compounded when other users rely on data that are omitted, incomplete, and/or inaccurately transcribed
    When one end-user omits critical data gleaned during a customer conversation—or makes errors in their efforts to transcribe that data from one platform to another, subsequent engagements with (or regarding) the same customer are compromised and compounded over time. Because that customer’s history is then incomplete—and possibly erroneous—later end-users who rely on that data can’t make fully-informed recommendations or pick up customer interactions where they left off. Often, this means customers must provide the same information again or correct ill-informed assumptions. Today’s customers bring high expectations to interactions with vendors—and even minor errors or inconveniences can diminish their trust in a vendor’s knowledge, commitment, and ability to deliver. Over the course of multiple interactions, the risk of additional errors increases, potentially compounding customer history inaccuracy—and confirming their sense that your enterprise lacks the sophistication necessary to answer their needs effectively.Woman frustrated, pulling at hair
  4. End-users may gain access to customer data they’re not authorized to see—putting the data at risk of misuse or breach and elevating the threat of regulatory compliance violations
    For enterprises in regulated industries, customer data security and compliance are essential. Once gathered, these enterprises bear full responsibility to secure sensitive revenue data and observe strict regulations governing how it’s used. Data must flow to the right screen for the right user to prevent breach or misuse and must be consistent in all the applications they use. Accordingly, it’s essential to maintain strict protocols that limit access to authorized users and only for authorized uses. This complex challenge is further complicated when customer data is housed in the individual data silos of many communications platforms, requiring duplicative security and compliance protocols that complicate monitoring and enforcement—and, in some cases, may expose data to increased risk of breach.
  5. Disconnected platforms prevent the level of administrative visibility necessary for meaningful user auditing and don’t allow for the configuration of processes and data flows
    Because data trust improvement and assurance is an ongoing enterprise obligation, the ability to audit end-user behaviors and configure (and reconfigure) processes and data flows is essential. Absent the ability to see how users are interacting with customer data, it’s difficult to identify and address substandard practices early and to halt malicious behavior before it becomes unmanageable. As with the challenges discussed above, these processes are complicated when revenue data is siloed in multiple communications platforms, resulting in decreased oversight, increased risk of compromised data quality—and undermined data trust.

40% of business initiatives fail to achieve targetKnowing the challenges, you’re likely to face as you begin to create your requirements is always better.  And while your enterprise may not suffer from every data trust issue we’ve highlighted here, the list provides a helpful starting point to assess your firm’s unique situation. Once you have a clear sense of the data trust challenges you may be struggling with—from governance to completeness to quality—you’ll be in a much better position to seek the best, most impactful revenue data ops solution. 

To make that task more manageable, we encourage you to read the second installment of this post, 5 Data Trust Trust Solutions to Reduce Risk of Bad Revenue Part II, where we’ll detail the solutions we recommend.


Interested in learning more about what is possible?

At Riva, we understand the journey that you are on as you create requirements to select the right revenue data operations platform. That’s why we exist. We’ve helped hundreds of organizations with complex data requirements understand what is possible to achieve.  Talk to one of our Riva specialists today, and we’ll help craft a custom evaluation guide to frame up your decisions.