Data governance is a set of processes that allow for the formal management of the data assets within an enterprise, including data quality, ongoing data monitoring for emerging issues, accuracy accountability, data security, and a continual increase in the overall value of organizational data assets.
This is a discipline which has been practiced for quite some time within the data warehousing industry that only emerged after years and years of pain, suffering, and cost. But unfortunately, the practice of data governance is still very rare among CRM implementations today, especially within the SAAS CRM applications that have emerged over the past decade such as Salesforce.com. Yet the value of its practice is as critical and as valuable here as anywhere.
Data quality, a key pillar of data governance, is often cited as the #1 issue preventing success among CRM and marketing professionals. When CRM systems fail to deliver, poor data quality is quite often pointed to as the chief villain. But even marginal success or mixed results are a shame when applying a data governance strategy can reap tremendous benefits to the practicing organization. And anyone employing business intelligence analytics on top of their CRM system (and who wouldn't) is wasting their time and perhaps even hurting their organization if these analytics are not performed on a foundation of quality, accurate data.
For example, redundant prospect and opportunity records can lead to fractured, incomplete, and incoherent information that is difficult to follow and analyze, and even more difficult to report on. Missing data can significantly skew trend information. The USPS reports that billions of dollars are spent annually on undelivered mail, and this doesn't include the cost of mail delivered to the wrong place, or the same person getting multiple copies of the same thing. Unstandardized data can considerably throw off data-segmented reports. And customer profiling and targeted marketing campaigns are near impossible without comprehensive, current data about each customer and prospect.
Also, the data within a CRM system helps to drive a company's revenue, allows for critical business decisions such as territory alignment, and provides feedback and data points for future strategic business decisions in terms of product direction and marketing. That all sounds pretty important to me, and yet the overwhelming majority of organizations that utilize Salesforce.com have no formal data governance processes in place where goals, instrumentation, metrics, and accountability are part of a business improvement strategy.
Why?
One of the biggest challenges is that most organizations are unable to tell if they have a set of data issues such as poor data quality to contend with. They have no diagnostic capability to determine if they have a problem (and and I'd bet my Blackberry they do), and if so to what extent? It's kind of like the guy who is up walking around so he assumes he is healthy, but he hasn't been to the doctor in ten years, and ignores any kinds of pains or symptoms he might see from time to time. Sooner or later a serious condition can develop, and by then it is often unfortunately too late.
Also, data governance is not easy, nor is it sexy. Like opportunity, it often shows up in overalls and looks like work. It also requires an investment, so short-term thinking often eclipses long-term strategy, especially in tougher economic times, and so the activity often gets shelved, especially with no numbers available to point to the scope of the problem. The path of least resistance is ignoring it.
So what can the forward-thinking among us do?
Ideally, put together a long-term data governance plan including profiling existing data to determine the magnitude of the problem, put metrics and goals in place, determine what technology if any might be helpful, perform real ROI analysis based on proposed changes, and be sure to include the business users and their input to develop a comprehensive strategy appropriate for your organization.
However, some data governance within Salesforce.com (or other CRM systems) is better than just turning a complete blind eye, so if you don't have the runway, resources, or corporate support for a comprehensive approach currently, there are some steps to follow to get started more quickly and headed down the right path.
First of course, provide for some data governance accountability within your organization. Somebody has to be the "data steward" and take responsibility for organizational data assets. In smaller organizations with smaller numbers of users, this might be the same person as the Salesforce administrator.
Second, employ some diagnostic testing and uncover the list of systemic data quality issues that are present. For example, determine a percentage of records with incomplete information, and then begin to quantify in cost and lost opportunity a prioritization schedule of the basic issues that are uncovered. Some might even be o.k. to ignore if correcting them would be of minimal value. Unfortunately in the Salesforce.com world, there are not sophisticated data profiling or quality discovery tools available, so you might need to be a little more creative here during this discovery process by running a series of reports to uncover issues.
Third, start with at least a data quality strategy. One thing I want to point out is that implementation of technology is only part of any data quality strategy. For example, you can utilize the various Salesforce quality tools for data verification and quality improvement like the company I work for provides, but you can employ other non-technical techniques such as placing a percentage of a sales team member's variable pay directly tied to completeness of data entry into Salesforce.com. These incentive-driven approaches can go a long way in improving the level of quality and data completeness within your system.
Fourth, have some real, meaningful metrics in place, even if only a few initially, so improvement can be shown over time and demonstrated to company leadership for their continued or increased sponsorship.
Finally, recognize that data quality is not a one-time event, but rather a subset of a larger ongoing data governance strategy. After initial cleansing activities are performed, it is important to ensure that ongoing data quality control mechanisms are in place, both technically and incentive-based, to ensure that six months later you are not back to the beginning with the same set of data quality issues. For example, look at the points of data collection, such as Web-to-lead forms, and ensure data quality enforcement is in place there as well. Don't just focus on the back-end data. Data governance is a marathon, not a sprint.
Hopefully through time, data governance will no longer be an afterthought in the Salesforce.com community that only gets addressed when severe symptoms occur. With so much riding on your CRM system, a multi-faceted, comprehensive data governance plan can only be ignored at great peril.
This is a discipline which has been practiced for quite some time within the data warehousing industry that only emerged after years and years of pain, suffering, and cost. But unfortunately, the practice of data governance is still very rare among CRM implementations today, especially within the SAAS CRM applications that have emerged over the past decade such as Salesforce.com. Yet the value of its practice is as critical and as valuable here as anywhere.
Data quality, a key pillar of data governance, is often cited as the #1 issue preventing success among CRM and marketing professionals. When CRM systems fail to deliver, poor data quality is quite often pointed to as the chief villain. But even marginal success or mixed results are a shame when applying a data governance strategy can reap tremendous benefits to the practicing organization. And anyone employing business intelligence analytics on top of their CRM system (and who wouldn't) is wasting their time and perhaps even hurting their organization if these analytics are not performed on a foundation of quality, accurate data.
For example, redundant prospect and opportunity records can lead to fractured, incomplete, and incoherent information that is difficult to follow and analyze, and even more difficult to report on. Missing data can significantly skew trend information. The USPS reports that billions of dollars are spent annually on undelivered mail, and this doesn't include the cost of mail delivered to the wrong place, or the same person getting multiple copies of the same thing. Unstandardized data can considerably throw off data-segmented reports. And customer profiling and targeted marketing campaigns are near impossible without comprehensive, current data about each customer and prospect.
Also, the data within a CRM system helps to drive a company's revenue, allows for critical business decisions such as territory alignment, and provides feedback and data points for future strategic business decisions in terms of product direction and marketing. That all sounds pretty important to me, and yet the overwhelming majority of organizations that utilize Salesforce.com have no formal data governance processes in place where goals, instrumentation, metrics, and accountability are part of a business improvement strategy.
Why?
One of the biggest challenges is that most organizations are unable to tell if they have a set of data issues such as poor data quality to contend with. They have no diagnostic capability to determine if they have a problem (and and I'd bet my Blackberry they do), and if so to what extent? It's kind of like the guy who is up walking around so he assumes he is healthy, but he hasn't been to the doctor in ten years, and ignores any kinds of pains or symptoms he might see from time to time. Sooner or later a serious condition can develop, and by then it is often unfortunately too late.
Also, data governance is not easy, nor is it sexy. Like opportunity, it often shows up in overalls and looks like work. It also requires an investment, so short-term thinking often eclipses long-term strategy, especially in tougher economic times, and so the activity often gets shelved, especially with no numbers available to point to the scope of the problem. The path of least resistance is ignoring it.
So what can the forward-thinking among us do?
Ideally, put together a long-term data governance plan including profiling existing data to determine the magnitude of the problem, put metrics and goals in place, determine what technology if any might be helpful, perform real ROI analysis based on proposed changes, and be sure to include the business users and their input to develop a comprehensive strategy appropriate for your organization.
However, some data governance within Salesforce.com (or other CRM systems) is better than just turning a complete blind eye, so if you don't have the runway, resources, or corporate support for a comprehensive approach currently, there are some steps to follow to get started more quickly and headed down the right path.
First of course, provide for some data governance accountability within your organization. Somebody has to be the "data steward" and take responsibility for organizational data assets. In smaller organizations with smaller numbers of users, this might be the same person as the Salesforce administrator.
Second, employ some diagnostic testing and uncover the list of systemic data quality issues that are present. For example, determine a percentage of records with incomplete information, and then begin to quantify in cost and lost opportunity a prioritization schedule of the basic issues that are uncovered. Some might even be o.k. to ignore if correcting them would be of minimal value. Unfortunately in the Salesforce.com world, there are not sophisticated data profiling or quality discovery tools available, so you might need to be a little more creative here during this discovery process by running a series of reports to uncover issues.
Third, start with at least a data quality strategy. One thing I want to point out is that implementation of technology is only part of any data quality strategy. For example, you can utilize the various Salesforce quality tools for data verification and quality improvement like the company I work for provides, but you can employ other non-technical techniques such as placing a percentage of a sales team member's variable pay directly tied to completeness of data entry into Salesforce.com. These incentive-driven approaches can go a long way in improving the level of quality and data completeness within your system.
Fourth, have some real, meaningful metrics in place, even if only a few initially, so improvement can be shown over time and demonstrated to company leadership for their continued or increased sponsorship.
Finally, recognize that data quality is not a one-time event, but rather a subset of a larger ongoing data governance strategy. After initial cleansing activities are performed, it is important to ensure that ongoing data quality control mechanisms are in place, both technically and incentive-based, to ensure that six months later you are not back to the beginning with the same set of data quality issues. For example, look at the points of data collection, such as Web-to-lead forms, and ensure data quality enforcement is in place there as well. Don't just focus on the back-end data. Data governance is a marathon, not a sprint.
Hopefully through time, data governance will no longer be an afterthought in the Salesforce.com community that only gets addressed when severe symptoms occur. With so much riding on your CRM system, a multi-faceted, comprehensive data governance plan can only be ignored at great peril.
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