Data Strategy – apiphani

5 Steps for Implementing a Data Governance Program That Fits Your Company Culture

Apiphani Team — Thu, 15 May 2025 09:01:49 +0000

Authors:
James Kendrick, Principal Director, Professional Services, apiphani
Duane Tomlinson, Data Success Manager, Atlan

Data informs nearly all business decisions and is the main driver of company innovation. A 2025 Gartner poll found that 89% of CEO and senior business executives say effective data, analytics, and AI governance is essential for enabling business and technology innovation. Yet, many organizations have neither a data strategy nor a data governance program in place.

Recipe for Data Success

Implementing a data strategy and governance program doesn’t happen overnight. Companies that rush into it often lack a clear vision and goals for their data. Without these, and without a plan for broadly communicating the strategic importance of your data program – and its critical success factors – you may be putting the program’s success at risk.

In an informed organization, everyone understands why data strategy and governance are important. They also know their role in its implementation and have been educated on the end benefits. Conversely, poor communication creates confusion between the data strategy designers and those involved in its execution. This confusion wastes time, can be hard to recover from, and puts your whole data program at risk.

How to Implement a Data Strategy & Governance Program

Apiphani worked with Atlan, a leading active metadata platform and a modern data collaboration workspace, to create a combined human / technical environment for our client, a global manufacturer of innovative gas turbine components used by the clean energy industry.

Our objective was to instill data as a critical part of the client’s evolving digital culture. Together, we implemented a catalog and governance program for data product assets that were classified into 10 data domains. This gives our client the ability to sustain value at scale, as new and evolving data products are continually developed.

In this article, we share what we learned from our experience and explore a framework that includes the five steps necessary to achieve a data governance program, in motion, that fits your company’s culture.

Step 1: Set Context for Governance Over the Expansive Use of Data

The success of any initiative depends on how well it’s executed. A clear vision with well-defined goals and outcomes are a critical success factor.

Successful organizations start by engaging a data champion who can advocate for expanding the role of data in driving successful business outcomes. Next they involve key business leaders who either have a need for specific data, or who have already requested specific data to drive better business outcomes.

Engaging with these leaders uncovers opportunities to use data more expansively. Understanding their business challenges is also necessary. What’s preventing them from leveraging specific data for key business decisions? Difficulty extracting it from its current source? Lack of a “single source of truth”? Finding the answer begins to lay the foundation of the value of data governance.

Once you involve the right business leaders, identify the following information:

Specific data opportunities, data challenges, data initiatives, or compelling events
Current state of data strategy and progress in advancing data usage
Current data and analytics environment, including its strengths and weaknesses
Data domains and data products around which to organize the future state

Often, we see the initial data governance effort tied to retroactive governance. This means there has been an initial push on the existing data within the organization, and there has been some effort to enrich this data and then drive awareness of the enhanced discoverability of the data, considering “Enabling Self-Service” complete.

What’s often forgotten are the future-state human behaviors that must be adopted to ensure the data enrichment is proactively embedded within the organization’s business processes. Ask these questions:

What is the operating model?
What are the governance priorities for data domains and products?
What will the beneficiary’s workflow look like?

These are all considerations to ensure retroactive work becomes proactive in the future.

Step 2: Formulate and Align Around a Governance Scope

Next, formulate a data governance program to match the current state. It may change or expand over time in accordance with data’s expanding role in driving successful business outcomes (see Step 1).

Start scoping by evaluating and understanding the current state and goals of the business in the context of the following five pillars of expansive use of data:

Strategy

Data Strategy
& Roadmap

Delivery

Data Products,
Analytics, and
Advanced Usage

Access and Governance

Data & Analytics
Cataloging and
Discovery

Data Platform

Modern Data
Stack
Configuration

Managed Data Service

Ability to Deliver
and Support Data
Assets

Get to know the true current state of data in the following areas:

Use of data to drive decisions
Governance of data used to drive those decisions
People involved in the end-to-end processes, from getting data to delivering it in the requested consumable format
Technology used along the way, noted for each persona

A true reflection of the current state allows you to identify opportunities for removing, improving, or creating processes in the end-to-end flow. This will inevitably unify data and governance and will present an operating model for success. Success, meaning the organization addressed the defined outcomes and goals for their business.

Step 3: Determine the Best-Fit Governance Program

Ensure you have a clear vision and scope that is achievable, adds value, and clearly outlines the value of a sustainable, long-term governance program for your organization.

Next, select the culture of the organization, as it is today, according to how business process changes are driven:

Top down
Function driven
Hybrid

With the details from Step 2, along with your culture selection (above), you’ll be better equipped to determine how the program should be introduced, communicated, spoken about, implemented, and what expectations to set when onboarding teams that are either part of the process and/or beneficiaries of the outcomes.

This will clarify who within the organization should communicate the program so that there is an increased probability of success and decreased resistance to change.

This aligns perfectly with Prosci’s ADKAR Change Management framework. Use this framework to help ensure maximum impact in driving awareness, building desire, and determining how reinforcement communications should be delivered, and by whom.

Step 4: Expand Motivation and Create Momentum for the Governance Program

Awareness and reinforcement communications create momentum for the governance program by tying it to critical business needs in each data domain. Assimilating findings and drawing conclusions about the situation, opportunity, and how to proceed will expand motivation as the program takes shape.

Consider using the following activities for awareness, desire, training, capability assessment, and reinforcement planning:

Develop a read-out for working sessions
Create a one-page executive summary for the C-suite
Hold feedback sessions with key stakeholders
Gain alignment and agreement on the business case
Set clear expectations that align with the organization’s SMART goals
Communicate how you will measure the organization’s involvement or the effectiveness and success of new behaviors

This approach enables the organization to reach a unified vision. Providing key stakeholders with an opportunity to ask questions and provide input drives an even greater sense of participation and unification around the identified goals.

Step 5: Proceed to Governance Program Implementation Tied to Goals from the Assessment

Typically, implementation falls into one of three patterns. These patterns are interdependent and can often evolve into other patterns. How a pattern gets defined depends on the operating model and the organization’s culture (top down, functional, or hybrid, as defined in Step 3). The patterns are as follows:

Pattern 1: Data Strategy Led

The implementation plan for the governance program is clear, organizationally aligned, and showcases key wins. Expansion follows achievement of key objectives.

Pattern 2: Use Case Led

Mobilization and build-out of the program is focused on individual use cases, impacting a broad range of business units or teams. The success of each use case can be defined differently for each business unit or team. Expansion follows use-case identification and delivery.

Part 3: Legacy Led

The scope of implementation is focused on a core business unit, function, or team. Objectives and goals have a process impact on fewer individuals, while outcomes remain of strategic value.

Success of each pattern hinges on your ability to execute according to fit with the organization’s culture and structure. Selecting the right pattern, along with the required attributes to support the ongoing program, is key.

Reinforcement communications play a pivotal role here and are often forgotten. How well you communicate your expectations depends on your change management skills. There is a direct correlation between a clear data vision / goals (communicated methodically) and better business outcomes. If you follow Prosci’s ADKAR change management model, for example, you know that an organization is 7x more likely to achieve success with an initiative.

Data Governance – A Modern Approach

Get started with your program today. Contact us to schedule an assessment /roadmap workshop that fits your organization and culture. We can help you find the right starting point and tailored steps to create a successful data foundation and data governance program using the framework we’ve described.

Read our case study to learn how apiphani helped Power Systems Manufacturing build a data pipeline.

About Apiphani

Apiphani is a managed services and IT services provider that believes in human exceptionalism in the time of AI. By integrating decades of industry experience with Deep Automation™ and machine learning we are able to drive extreme efficiency and reliability in support of our client’s mission critical workloads. To learn more about Apiphani, please visit our website and follow us on LinkedIn.

About Atlan

Atlan is the next-generation platform for data and AI governance. It is a control plane that stitches together a business’s disparate data infrastructure, cataloging data and enriching it with business context and security. With Atlan, data and business teams can easily find, trust, and govern AI-ready data. To learn more about Atlan, please visit our website and follow us on X (@AtlanHQ) and LinkedIn.

Want to unleash the business value of SAP’s Business Data Cloud announcement…Here’s how

Apiphani Team — Wed, 26 Feb 2025 08:38:37 +0000

In today’s fast-moving business landscape, data is both a competitive advantage and a challenge. Enterprises generate vast amounts of information, but making sense of it — especially when it’s scattered across different systems — can be a daunting task. SAP has recently announced their Business Data Cloud (BDC) which is said to help change that.

What SAP is pushing with BDC is that organizations will streamline the way they collect, process, and leverage data. Instead of struggling with fragmented reporting and manual data extraction, executives can access real-time insights that drive better decisions, improve operational efficiency, and ensure compliance — all within their existing SAP ecosystem.

For business leaders, this isn’t just another IT tool — it’s a strategic enabler. Whether you’re looking to optimize financial planning, enhance supply chain visibility, or strengthen risk management, the theory of SAP’s BDC is to provide a centralized, automated, and intelligent approach to data. We here at apiphani have taken data very seriously in the past few years leading to the formation of our Data & Analytics Practice which works with large organizations to change the way they think about data and uncover hidden value. Let’s explore why all of this matters, how it works, and what it means for organizations looking to turn raw data into actionable intelligence.

Why Data Collection Matters

Data has become one of the most valuable assets for modern enterprises, yet many organizations still struggle to harness its full potential. Executives make high-stakes decisions daily, but too often, those decisions rely on incomplete, delayed, or inconsistent data. Without a streamlined approach to data, businesses face challenges like siloed information, manual errors, and a lack of real-time visibility — all of which can lead to missed opportunities or costly inefficiencies.

For leaders focused on growth and resilience, effective data isn’t just an IT concern; it’s a business imperative. When organizations can efficiently gather and unify data across their operations, they gain:

Real-Time Decision Support – Fast, data-driven insights help executives respond to market shifts with confidence.
Operational Efficiency – Automating data flows reduces manual work, minimizes errors, and accelerates reporting cycles.
Risk Mitigation & Compliance – A structured approach to data collection ensures transparency, governance, and adherence to evolving regulations.
A Competitive Edge – Organizations that leverage high-quality data are better positioned to optimize performance, identify trends, and unlock new revenue opportunities.

In a world where business agility is critical, the ability to collect, process, and act on data seamlessly separates industry leaders from those struggling to keep up. The question isn’t whether data collection is important — it’s whether your organization is doing it effectively.

Real-World Examples

For executives, the impact of effective data collection isn’t theoretical—it’s measurable. Organizations that have streamlined their approach to gathering and processing data are seeing tangible benefits across key areas of their business. We here at apiphani have recently completed a project to enable data-driven business through data pipelines with PSM (Power Systems Manufacturing).

Data Agility as a Competitive Advantage

In today’s fast-paced business environment, data isn’t just an asset—it’s a competitive advantage. But raw data alone isn’t enough. Without the right pipelines in place to collect, process, and deliver insights in real time, even the most valuable data loses its impact.

Executives who prioritize agile, automated, and scalable data strategies aren’t just improving efficiency—they’re positioning their organizations to outpace competitors, adapt to market shifts, and make smarter decisions faster. The companies leading their industries tomorrow are the ones investing in data agility today.

Only time will tell how SAP BDC is adopted in the market. The question isn’t whether data is important—it’s whether your organization is set up to do it better, faster, and smarter. Whether you’d like to explore BDC or review other options, we here at apiphani are here for you.

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New Year, New Strategy: Webinar Explores Data’s Expansive Role in Business Success

Apiphani Team — Fri, 24 Jan 2025 16:32:19 +0000

Data is at the core of every business decision and process made by today’s organizations. This trend will become even more important in 2025, and yet, many organizations do not have a modern data strategy in place that maximizes value from the data they have access to.

Realizing the potential power hidden in their data, Power Systems Manufacturing (PSM) turned to apiphani to modernize their approach to data and analytics on an enterprise-level by leveraging the apiphani Data Pipeline.

To discuss the implementation, stakeholders from apiphani, PSM and AWS recently came together to discuss how they took data from PSM’s mission-critical systems and other operational sources to create strategic data pipelines and self-service data products.

In this webinar, you’ll learn what it takes to successfully implement and realize data’s critical role in business strategy and operations in 2025 and beyond, including:

Ways to connect the CEO and C-Suite in a data strategy – a crucial first step for implementing any modern data strategy.
Keys to creating data domain roadmaps.
How apiphani used existing data sources, custom applications, and AWS tools to build a modern data platform to drive positive business outcomes for PSM.
The benefits of developing a modern data operating model to build and sustain value.

Continue down the path towards achieving a modern data pipeline.

Watch the webinar

Learn more about how apiphani, PSM, and AWS partnered to implement the apiphani Data Pipeline by reading the case study.

“We didn’t want a proprietary solution. It was important to have the pipeline completely integrated into our AWS cloud infrastructure, retaining control of the data and the systems ourselves. That was essential.”- John Thorburn, Head of IT for PSM

Apiphani’s Data & Analytics Practice – Helping You Build Trusted Data Pipelines for BI, ML, and AI

Apiphani Team — Tue, 23 Jul 2024 08:47:00 +0000

This video introduces Apiphani’s Data & Analytics practice and its data pipeline solution designed to help organizations extract greater value from their data across BI, machine learning, and AI initiatives. The offering combines strategic planning, the development of high-value data products, a secure, governed data platform, and managed services to create reliable, business-ready data ecosystems.

The pipeline enables integration across cloud environments, databases, and third-party applications while supporting domain-driven data strategy and structured investment prioritization. It also includes migrating existing analytics environments using modern best practices for development and deployment, with the stated goal of accelerating data delivery and reducing total cost of ownership.

FAQ

What is Apiphani’s data pipeline?

What types of systems can the pipeline connect?

Does the offering include analytics migration?

How does the pipeline support strategic planning?

What performance improvements are highlighted?