At this point, we all know the potential value of our organization’s data. Without making effective use of this data, we may severely lack the insights to prosper as a business and propel us past our competitors. Solving seemingly straightforward problems, identifying new sources of revenue and even operating our core business efficiently can prove challenging. To support these endeavours and truly optimize the power of our data, we must ensure our data is of high quality, trustworthy and easily accessible. This is where Data Governance comes in.

What is Data Governance?

In short, Data Governance is a strategic initiative guided by principles and practices that formalize the management of your organization’s data assets. A data governance strategy ensures your data is largely consistent, correct, discoverable and secure. Moreover, it defines who can take action, on what data, and in which circumstances using what prescribed methodologies.

Now, more than ever, as companies face strict data privacy regulations and increase their dependency on data analytics to help strengthen operations and drive decision making, it’s becoming increasingly necessary to implement a data governance strategy.

I want to take a moment to briefly discuss data concepts that are often described synonymously with the data governance discipline, but rather need to be recognized as standalone initiatives.

Data Stewardship: is often described as data governance in operation. Data stewardship involves the coordination and mobilization of your data stewards to execute your data governance strategy. Tasks can include:

  • Identifying and documenting data sources and critical data data points
  • Defining operational procedures that meet requirements defined by a data governance framework
  • Establishing benchmarks for quality and usability of your organization’s data
  • Managing metadata
  • Resolving discrepancies or issues as they related to your organization's data

Data Management: is just one of the core competencies of data governance and defines the process of ingesting, storing, organizing and maintaining data created and collected by your organization. I recently wrote about how implementing a modern data stack can help you realize effective and efficient data management here.

Master Data Management: is the coming together of your data enablers and various business teams to create a single or group of master datasets that serve as a trusted data source across your organization. Guided by your organization's data governing policies, data here is de-duplicated, reconciled and enriched with the goal of enabling accurate reporting and decision making.

Why is Data Governance Important for Your Business?

Though it may not be formally regulated, your organization has likely already adopted some form of data governance. And it is for this reason that data governance is typically institutionalized through a natural evolution from informal to formal practices and controls. Data governance is often formalized when your organization reaches the point where seemingly rudimentary data-related requests or tasks can no longer be executed efficiently.

Adopting a formalized data governance strategy requires identifying best practices, policies and regulatory and legal requirements that your data must meet, then creating policies and enforcing these policies through manual or automated processes.

Data Governance mandates:

  • Where and how data can be stored
  • Who has access to data
  • How data is propagated, traversed and transformed
  • Data lineage
  • Compliance with various government and regulatory bodies
  • When data is archived and subsequently destroyed
  • Codification of data encryption and protection methods

Without the deployment of a sound data governance strategy, organizations need to consider the following:

  1. Are we comfortable with failing to leverage our data in an organized fashion to increase the value we deliver to our clients and/ or out pace our competitors?
  2. Do we have concerns maintaining multiple sources of the truth that result in inconsistent business insight and generate error prone analytics?
  3. What is our tolerance level for regulatory and operational system risk that may result from postponing the implementation of data governance?
  4. What is our appetite for inefficiencies in our business operations that result from inconsistent or unreliable data?

Benefits of Data Governance

An effective data governance strategy can deliver value across all individuals and teams in your organization. Deploying data governance can help your organization achieve the following:

Dismantling data silos. Data silos can limit every facet of your organization from limiting communication and collaboration, decreasing your data’s quality and integrity, and generally slowing down the speed at which you can make business decisions. When there is no centralized system for managing data, individual teams become responsible for handling their own data and this data is rarely shared across teams. Data governance breaks down these silos by ensuring your organization has visibility and control over the data being gathered across your various departments. In an effort to foster broader insights and in turn, better business decision making, data governance encourages collaboration between people, teams and departments. Deploying governance tools like a Data Catalog (more on this below) improves one's ability to understand your data and fuels a data-centric culture.

Improved data management. Despite being unique initiatives, the data management policies organizations put in place are in fact part of their larger data governance strategy. Data governance institutes codes of conduct and best practices in data management, ensuring compliance in a variety of areas traditionally not addressed by tech teams, including compliance, security and legal. Moreover, these guidelines reduce the likelihood that data will be leaked, lost, duplicated or contain errors. With respect to legal requirements, data governance provides a platform for complying with various governmental regulations including GDPR, HIPAA and PCI DSS.

Common understanding of data. Managing and documenting your organization’s data is often facilitated through a Data Catalog. A data catalog provides a single, overarching view and deeper understanding of all your data assets and the relationships between these assets. Data assets may include structured and unstructured data, analytical reports and dashboards, semantic and business logic and data science and machine learning models.  Data catalogs often document the asset’s metadata, the cardinality between data sets and the various layers of transformations and attribution as your data is propagated from source through to target. This catalog enables anyone in your organization with access to your data to search through all of the available data assets, gain a pervasive understanding of the data and ultimately enable them to leverage this data for their various analytics and business purposes.

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Improved data quality. Simply put, you cannot achieve high quality data without a sound data governance strategy. A high degree of consistency, accuracy and completeness describe sound data quality and in an effort to make trustworthy and agile business decisions, your organization needs high quality data inputs. Without this, you may be subject to missed business opportunities, operational inefficiencies and ultimately, financial loss. Data quality issues may arise from having to manage multiple sources of data, consistently changing data needs, a lack of computing resources, poorly defined data and a variety of human inflicted errors. Resolving these common causes by establishing a pervasive standard of quality data, empowering Data Stewards to ensure the fitness of your data assets and defining policies and practices to maintain data quality at the outset will ensure the improvement and preservation of your organization’s data quality.

Who is Responsible for Data Governance?

In an effort to safeguard the success of your data governance initiative and realize the benefits described above, you need to enable the right team coupled with ensuring executive level sponsorship. Effective data governance involves mobilizing your entire organization. More often than not, organizations nominate a data governance team responsible for identifying priorities and goals, formulating a governance model or framework, soliciting budget approval and selecting applicable technology to support the initiative. These data governance teams are typically made up of:

Data Owners: are individuals who have the knowledge and authority to make decisions on definitions, quality, accessibility and retention as it relates to the data asset they own. As subject matter experts, data owners are commonly tasked with authoring and maintaining data dictionaries. A data dictionary catalogs your data’s structure, contents and provides meaningful descriptions on all data elements.

Data Custodians: are responsible for the technical accountability of the data and their roles can be divided even further by their areas of expertise including: data modelling, data architecture design, data warehousing administration, etc. They are primarily responsible for configuration, scalability, availability, accuracy, consistency, business rule implementation, security and disaster recovery.

Data Stewards: are tasked with overseeing all assets of the data lifecycle including creating, preparing, using, storing and deleting data all while ensuring these activities prescribe to the organization’s data governance principles. Similar to that of a Data Owners, Stewards often possess a high degree of business and functional knowledge and like Data Custodians, maintain the accessibility, usability and safety of your data.

Pillars of Successful Data Governance

So far we’ve defined data governance, described how it would benefit your organization and who the various players are, but how do we guarantee its success? Here’s 5 practices you can employ to ensure your data governance strategy delivers value.

1. Secure Executive-level Support

Most successful data governance projects are championed by top management. Engaging your leaders at the outset of your project will minimize change management challenges when instituting new practices and policies as they relate to the management of your organization’s data assets. Securing their buy-in requires understanding their personal data challenges, highlighting more pervasive organization-wide data limitations and clearly articulating the role and benefits derived from a data governance strategy. Demonstrating how deploying data governance can solve specific pain points and ultimately improve business and financial outcomes is a great way to secure leadership support. As your top management likely has the power to influence and mobilize resources, budget, culture and compliance with new best practices, it is in your best interest to safeguard their commitment.

2. Integrate with Digitization Goals

Data is the root of most organization’s digital transformations. Is it therefore paramount that these companies develop a data governance strategy to methodically execute this transformation. Linking your data governance initiative to your organization’s strategic goals not only secures senior management buy-in, it shifts the responsibility of instilling governance from the data teams towards all business and functional teams. Through this integration, team managers, project managers and product owners alike now have the added responsibility to ensure their respective domains align with the organization’s data governance strategy.

3. Deploy the Right Amount of Governance

As you’ve likely ascertained, data governance can be a daunting initiative with virtually no end in sight. It is for this reason that you have a responsibility to curtail the scope of your data governance strategy by starting small and iterating accordingly. Instead of attempting to deploy data governance across the entire organization, identify key departments and target your implementation to these teams. Conducting a retrospective by identifying various successes and failures from the initial implementation will secure the overall initiative's victory as you expand the implementation across the entire organization. Moreover, data governance needs can vary depending on the goals of an organization and as such,  it is best to adopt a needs-based approach. If your organization’s goals are to enable scale, create value or simply comply with local government regulations, balancing the right amount of governance can mitigate scope risks and drastically improve your ability to realize these goals.

4. Engage the Right People at the Right Time

Failing to identify and engage the right stakeholders early in the implementation can put the project at risk. Engagement is much more than keeping stakeholders informed. Many of these stakeholders may be data owners and as such, bring valuable business insight to the table. Consulting stakeholders on critical decisions in their area of expertise and keeping them informed about decisions outside their area of expertise will prove fruitful over the course of your implementation.

5. Generate Excitement

When your organization is excited about the prospective benefits derived from implementing data governance, they are more likely willing to comply with the policies and best practices outlined by the data governance strategy. Generating excitement is often cited as one of the most challenging tasks in a data governance program.

Data governance is the business equivalent of working out - we know it’s good for us, but really, we’d rather be doing something else.

Organizations who successfully generate excitement leverage a combination of eliciting top level management support, disseminating information by offering training and learning resources and raising awareness through real life use case demonstrations.

How Narrator Enables Data Governance

Two of the most notable benefits resulting from the deployment of a data governance initiative is quicker insights and better decision making. A single source of truth coupled with a well-defined and simplistic data model facilitates agile, consistent and trustworthy analytics.

Front-End Development
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Narrator’s Activity Stream creates a 360-degree view of your customers by aggregating various customer touch points in one place. Facilitated by an 11 column, time-series data model, Narrator’s tooling allows you to centrally track a variety of customer activities including the sales funnel flow, clickstream/ web analytics data, customer service tracking, and social media engagements to establish a single source of truth and create a high degree of consistency across data entities and business activities. A centralized and simplified data model not only ensures data-driven decision making is sourced from trustworthy data, but it also facilitates self service analytics by equipping decision makers with the right data at the right time.

For more information on how the Activity Stream enables data governance including some of the benefits you can realize adopting Narrator in your data stack, please continue reading here.

Conclusion

At its core, data governance is a strategic initiative exercising policies, directives and regulations as well as managing roles and responsibilities to ensure continuous data quality and data management improvement. What’s evident is that data governance is critical to capturing value through digital, analytical and other transformative initiatives. Keep in mind that data governance is not a one-off project and the practices and policies that you institute need to evolve as your organization and the landscape in which your business operates evolves. Securing leadership support early, embedding governance with the organization’s transformative goals, engaging the right staff and the right amount of governance, and ultimately provoking excitement will promote an effective data-driven culture and accelerate your organization’s digital transformation.

Want to learn more? Get in touch to find out how Narrator can help.