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Understanding the importance of data governance

Understanding the Importance of Data Governance


Accurate enterprise data is the backbone of every successful strategy. It helps leaders understand and improve processes, identifies the potential to reduce waste and drives business decisions with deeper customer insights.

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But What Happens When Data Decays

Whether it’s duplicate, messy or invalid, bad information is costing your organization significant time and money. Without a data governance policy in place, this phenomenon is only likely to worsen as your company grows and your digital environment becomes increasingly complex. 

By establishing a stronger data governance program, leaders can achieve better business outcomes and reduce operational inefficiencies.

The Importance of Data Standards

The support of third-party cookies is rapidly coming to an end. Without those small files providing invaluable user information, companies will become more reliant than ever before on first-party data to activate and measure outreach initiatives. However, without data standards in place, organizations will not possess the information they need to efficiently execute their strategies. 

Data governance, a set of principles and practices surrounding first-party data, can help to prevent inaccurate information from wrongfully influencing business decisions. Without such a framework in place, teams are left using inaccurate or invalid data, weakening organizations’ competitive standing and undermining critical business objectives. 

Gartner research shows that, on average, poor-quality data results in companies losing $15 million on an annual basis.

This loss only grows as an organization continues to create and store an exponential amount of data that tends to be duplicate or inaccurate.

While some companies have worked to address this challenge by employing new positions, such as a data steward or a chief data officer, oftentimes the effort isn’t enough. Information still becomes siloed as the IT department may not understand all of the sources creating new data or the impact across the individual lines of business (LOBs). To be truly effective, a data governance framework should reflect fully organizational priorities and the strategic challenges the company faces.

Contact and account intelligence requires more than data quality that’s on-par with that of the competition. Teams need persistent, unified and up-to-date information on prospects and customers that’s accessible across all systems and platforms. Only then will companies be positioned to create compelling business cases that connect data quality improvement with key priorities.

Maintaining Data Integrity

Leveraging quality data is key for business-to-business companies in today’s market landscape. Research and Markets projected that the B2B eCommerce market would reach a value of $20.9 trillion by 2027 at a compound annual growth rate of 17%. However, with this growth comes an increasingly competitive landscape, making it critical that data integrity is maintained through a strong governance framework to drive business value.

Data integrity refers to the accuracy and validity of data over its lifecycle within a digital environment. Inaccurate or out-of-date information is of no use to a company, and can also open up liabilities related to the theft of sensitive data and the cost of unnecessarily storing and managing duplicates. Maintaining data integrity and reducing the presence of bad data within enterprise systems should be the core focus of a strong governance framework.

What Does Bad Data Look Like

Organizational change is inevitable. Every 30 minutes, 120 business addresses and 75 phone numbers change, making it that much more difficult for sales and marketing teams to maintain a quality set of customer profiles. This unavoidable phenomenon results in data decay, the gradual loss of quality within a system, including key company information, personal details and accurate contact addresses. 

As decay occurs, your customer relationship management (CRM) tools and other platforms begin to rely on inaccurate and out-of-data information, known as bad data. This results in data that is:

  • Inconsistent

    When a partial record or the entire entry is stored separately by each data owner, information often becomes inconsistent across teams.

  • Inaccurate

    While the data is mostly correct, some pieces may be inaccurate, such as a customer’s email featuring a typo or the wrong digit in a phone number. 

  • Duplicate

    Many companies store repeat records which can be costly as it affects the accuracy of the count of customers in their database, requires additional storage and can inaccurately impact business decisions.

  • Incomplete

    If any record is not complete, it can’t serve its purpose. This can negatively impact productivity, especially for organizations and teams that use data as a normal part of their day-to-day operations.

In addition to these issues with data quality, many traditional governance frameworks use outdated technologies that cannot meet modern security and data privacy standards — a serious problem for any company that handles customer data that must comply with current regulations, such as GDPR and CCPA.

By executing a well-maintained data governance strategy, organizations can protect themselves from potential security risks and stop incurring losses as a result of decaying data.

Building a Data Governance Program

Data is virtually useless if its integrity is not maintained across departments. However, as Chief Martec explains, the average enterprise uses 1,295 cloud services, with customer data scattered across many of them. It can quickly become overwhelming — if not impossible — to manually monitor every single piece of data within your organization’s systems.

When embarking on your governance effort, your company must establish a set of modern principles and practices that ensure data remains high-quality and in compliance throughout its entire lifecycle – both pre- and post-sale. At Zylotech, our approach to trusted data governance is based on five key pillars:

  • glass


    You need to look at the volume and completeness of your data and the level of unique identities.

  • arrow


    How healthy is your data at the field level? Is it within expected ranges? Including person, location and company ID must remain consistent and accurate across your systems. This is especially important when you merge data fields from multiple sources into one centralized system.

  • computer


    At the record level, data should be as fresh as possible. Monitor your systems continuously to confirm the last update. Make sure company and contact information are always up to date to ensure contactability.

  • gears


    You should take special care when editing or enhancing your current business rules and workflows. Marketing campaigns and sales lead data flows should remain in sync. Track any errors via the lineage of data across sources, transformation and dependencies. 

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    Labels and Rules


    Standardize how your organization will visualize, describe and tag your unified data model. For example, your organization might apply a specific tag to GDPR or privacy fields to enforce governance. Nominate data authorities to be the source of truth across your connected systems.

No matter how you approach your data governance strategy, it should help you achieve consistent data that is high-quality, accurate and up-to-date. However, the most important thing to remember is that monitoring data is a continuous process. It costs $1 to verify a record initially, $10 to clean it later and $100 to do nothing. Without ongoing management, data begins to decay.

Once an effective data policy is in place, your business will begin to achieve better strategic outcomes through more informed decision-making.

Data Sharing Across Teams

One of the most valuable benefits of strong data governance is the ability to give every business user the deep insights they need to drive desired outcomes.

While organizations frequently rely on third-party vendors to source and collect customer information, the raw data supplied by these providers often inherently possess the many flaws associated with bad data, making it difficult to leverage for business purposes.

A Successful Data Strategy Leads to Business Intelligence Across the Entire Organization

  • horn

    With a stronger data policy, you’ll be able to break down data silos that exist across your marketing technology stack. This means marketers can access updated lead, contact and account profiles with the trusted data quality that is essential to better results.

  • comments

    Equip sales teams with the deep insights they need to have relevant, timely conversations with their leads, contacts and customers. Teams can engage prospects at the right time with the right knowledge using precise data-driven strategies to capture high-value targets.

  • hands
    Customer teams

    Turn your customers into your best advocates by giving them the information and experiences they need when they need it. Improve engagement, retention and loyalty by enhancing the customer journey.

  • gears
    Data teams

    Build, maintain and validate a scalable contact and account foundation for a unified, automated approach to data governance.

When information is being freely shared between data users, departments are powered by insight into improving operations, bettering products, saving money and discovering hidden opportunities. However, without a strong governance program in place, data will remain siloed across departments, making it impossible to apply it for better decision-making.

Creating a Data Governance Roadmap

Many organizations lack the expertise or resources to conduct regular manual data maintenance, making them vulnerable to compliance risks as well as the costly impact of gradual data decay. However, there are solutions to create a strong data governance program that can enhance operations and improve business decisions.

By leveraging a data governance platform with built-in intelligence tools, companies can maintain a consistent set of policies, processes and owners around their data assets. When teams can monitor, manage and control data movement effectively, a single-source of truth is created that is accurate, up-to-date and consistent.

Rather than your team trying to manually maintain and monitor an entire data environment, an advanced data management automates processes including:

  • Cleaning and deduping

    By defining what quality data looks like within your organization, your team can easily standardize this definition and apply it across all systems. Ensure each entry for a contact is the same across teams, ensuring that all business decisions are driven by accurate and up-to-date information.

  • Data unification

    Gain a single view of all leads, contacts and accounts for a holistic view of account activity regardless of where the data is stored.

  • Data governance

    Leveraging pre-built applications and databases, your team can implement data synchronization and maintain data integrity without the need for IT assistance.

Master data management by deploying a trusted data governance solution that will ensure that your organization is driving business decisions using the most accurate and up-to-date information available.

Leveraging a Data Governance Solution

Each organization’s digital environment is made up of an insurmountable — and ever-growing — amount of data that rapidly decays as soon as it is logged across systems. Manually managing and monitoring each entry across departments is nearly impossible.

At Zylotech, we know that enterprise data governance is critical to success. A strong policy and high-quality standards result in stronger data analytic capabilities, which in turn lead to better decision-making and improved operations.

Our team connects the information and insights you have to deliver the experience your customers need and the revenue you want faster.

If you’re ready to begin building a data governance framework that works for your team, contact us today.