CLIENT PROFILE
- Industry: Technology
- Client: Software and Technology Provider in the HR/Payroll Space
- Solution: Data Governance & Data Management Assessment and Implementation
BACKGROUND
Enabling data-driven decision-making is a key component of maximizing success in today’s business world, regardless of industry or organization. To be effective, data needs to be complete, accurate, and reliable – a clearly defined Data Governance strategy will ensure that is the case.
Without an effective Data Governance strategy, there are likely to be insufficient or ineffective data policies and procedures. This can lead to poor data quality and ineffective decision making, as insights into patterns, preferences, issues, root causes, and associations could be incorrect.
To build a reliable platform, companies need to start with a clearly defined Data Governance strategy, which then becomes the primary driver to enable the implementation of effective Data Management across the enterprise.
Once implemented, a Data Governance strategy will ensure accurate and trustworthy data, which will then guarantee the tools to support data virtualization and/or visualization are reliable and impactful. This allows companies to begin to focus on more advanced data strategies around artificial intelligence (AI) and Advanced Analytics, which can include machine learning, predictive analytics, statistical modeling, etc.
THE PROBLEM
The client, a software and support organization for small businesses, is gearing up for an Initial Public Offering (IPO). They faced the challenge of demonstrating robust data governance practices to satisfy regulatory requirements and attract investor confidence as well as supporting their increased investment in analytical capabilities. Their existing data management policies and decision-making lacked the necessary transparency and accountability for the scrutiny of public markets.
THE CHALLENGE
The client was looking to have organized and standardized approaches to Data Management and Data Governance, which was a strategic end goal and a top priority across leadership at the organization.
However, with inadequate Data Governance strategy in place, the client was struggling to address prevailing pain points that manifested within reporting, business intelligence, and analytics platforms in the form of conflicting versions of the truth and extensive manual efforts. The client was at risk of failing to achieve its strategic vision.
As a result, there was a strong desire to mature the approach to data to effectively solve current system issues and create a foundation for successfully leveraging data through accelerated development of technologies like data warehousing and business intelligence.
THE SOLUTION
Acknowledging the need for a transformative approach, Kenway launched a phased approach centered around performing a Data Governance Maturity assessment to help the client understand where the gaps were, providing a set of recommendations, building the related roadmap defining a path to address the underlying issues, and implementing the recommendations defined in the roadmap.
Data Governance Maturity Assessment
The client shared a long-term vision which sought to simplify its data experience but was unsure how to go about implementing the changes needed to deliver such an experience. The organization required a current state assessment to surface underlying issues, determine gaps, and understand root causes for the pain points it was experiencing.
Assessments are key to understanding existing processes and capabilities. Lack of an assessment can lead to unrecognized gaps and/or missed opportunities to improve certain aspects of Data Governance/Management, ultimately limiting the client’s ability to meet future state goals and align with their vision.
Kenway’s assessment approach included reviewing existing data processes and interviewing various key employees across the Finance, Accounting, Customer Success (Operations), and Business Intelligence business units. The interviews focused on the following general areas:
- Data Processes (Inefficiencies)
- Data and Systems (Technology)
- Data Quality and Profiling
- Current State of Data
- Legacy Business Processes
- Importance of Data Governance
The Kenway team then synthesized the findings from the stakeholder interviews and workshop to document the current state high-level, data-related processes and procedures and the related pain points encountered across the organization.
The current state was then assessed based on Kenway’s Data Governance assessment criteria:
Rankings for each of the criteria were aggregated and aligned to the Data Governance Maturity Curve for the organization, which provided the client with insight on their Maturity Level.
With the assessment completed and the Data Governance Maturity defined, Kenway was able to provide the client with a set of recommendations and a related roadmap to help address the pain points and achieve its strategic goals while increasing its Data Governance Maturity. This led into the second stage of the engagement, the implementation.
Recommendations & Road mapping
Based on the findings, Kenway ultimately defined a set of key recommendations for the client:
- Stand Up Data Governance Decision-Making and Execution
- Develop Enterprise Change Management Plan
- Develop Report & Query Governance
- Create Documentation Artifacts
A roadmap was then established to define a path toward implementing change that would address the recommendations and deliver meaningful and measured value over time. This is shown below:
Implementation
Taking the roadmap from the assessment stage, the Kenway team began the Data Governance implementation by establishing, assessing, reviewing, and validating the foundation. This started by securing organizational buy-in and authority. Kenway took a bird’s eye view, examining at an enterprise level, to understand who within the organization had its best interests in mind and held adequate coverage across the enterprise to enable decision-making capabilities. Kenway facilitated conversations to ensure groups identified would work well together. Kenway facilitated conversations to ensure groups identified would work well together.
Once a Data Governance Charter was defined, and ideal candidates had been selected to create the Steering Committee, Kenway proceeded to create an Operating Model and establish key success metrics. This choreography is captured in the diagram below.
Identifying and onboarding the right participants across the enterprise with decision-making capabilities to represent the Steering Committee posed a hurdle as decision-makers have limited capacity to provide. Kenway entrusted these individuals by communicating the importance of Data Governance within the organization, having numerous conversations with each in alignment with a strategic change management plan comprised of risk assessments, and necessary training and communication plans to aid key decision makers on the committee.
Once the foundation was in place, the next stage of the initiative was to identify one high priority problem or opportunity for the organization to tackle. The diagram below outlines the circular art of accelerating, refining, and reaching market success by solving business problems at hand.
Throughout the journey, obstacles to ensuring the Steering Committee could function together and make decisions in a timely manner were faced. To mitigate this challenge, Kenway facilitated the conversations to ensure folks were examining all considerations, and everyone was confidently representing their scope of business and ultimately reaching a decision unanimously. Kenway shared industry best practices, and expertise from prior implementations at other clients, providing the Steering Committee with unique perspectives on what went right and what went wrong at these other firms.
Kenway also leveraged an in-house decision-making framework to aid the Steering Committee to make decisions that allowed for open and candid feedback with the structured decision-making process. Kenway adapted its own style to align with the existing framework that was more familiar at the organization.
RESULTS
- Defined and instantiated Data Governance Decision-Making and execution:
The definition of a Data Governance Charter outlining roles and responsibilities, along with the formation of a Steering Committee and the implementation of a Data Governance Operating Model have empowered the client with the capacity to streamline data-based decisions and gain confidence in their data
- Incorporated Change Management Practices:
Implementation and BAU Data Governance Activities are now supported by change management practices including a Training Plan, Communication Plan, and Change Management Strategy
- Improved data policies and documentation artifacts and enhanced data literacy:
The client’s systems and policies are updated and aligned with Data Policies with support of an extensive documentation comprising Data Lineage Diagram, Data Catalog, Data Classification, System Flow Diagram. This has permitted the generation of enterprise-wide data ownership and accountability Path to Growth:
In collaboration with Kenway, the client has a comprehensive backlog of opportunities to tackle in subsequent iterations, as well as Data Governance talent acquisition job descriptions and recommendations, both internally and externally
The client did an excellent job of marketing their initial successes with an enterprise level communication plan, outlining the progress made, steps to maturity, and a plan for continued growth.
CONCLUSION
The effective implementation and operationalization of a data driven decision making framework highlights the importance of strategic collaboration, technical expertise, and adaptive Data Governance and Data Management policies.
The results of this synergy have allowed the client to realize ROI on their technology investments and fully utilize the tools being implemented.
Explore how our tailored approach to data governance and management can transform your organization's decision-making capabilities. Contact us today to discover how we can help you achieve data-driven success.
