Articles & case studies

Why Migration Is a Strategically Risky Event

According to a 2025 Forbes Technology Council analysis, 94% of organizations are pursuing digital transformation, yet 58% still report weekly operational disruptions tied to legacy systems and the complexity of change. These disruptions are rarely caused by poor vision. They stem from flawed execution. One of the most underestimated contributors? Data migration.

Migration is not a technical footnote. It is the moment where legacy complexity, inconsistent logic, and incomplete ownership are most likely to break through. Inaccurate or misaligned data introduces risk that no system integrator, process designer, or workflow can solve after the fact.

The Hidden Costs of Poor Migration

Migration failures rarely reveal themselves during go-live. They originate much earlier, often during rushed discovery or when data is treated as a technical burden rather than a business priority. Among the most common causes:

• Legacy dependencies missed during early discovery
• Unvalidated business logic and assumptions
• Misaligned semantics across functions and geographies
• No formal rollback or reconciliation plan

These failures can get expensive very quickly. Post-go-live cleanup often exceeds the initial migration budget. In regulated industries, errors escalate to audit failures, compliance gaps, or damaged stakeholder trust.

When migration is rushed or treated as a one-time technical task, organizations fall into what experienced practitioners call the “code, load, and explode” trap, where data is half-loaded into systems before they’re truly ready, triggering widespread downstream issues.

A public sector example illustrates this clearly. In a high-stakes government implementation, first responders risked losing benefits due to a flawed migration. Definian was brought in to stabilize the program and recover the data.
Read the full case study.

As one CFO mentioned, “In the migration, they had a rule for payment terms where the legacy value was ‘I’ and they migrated that as Immediate...”

The result: a misconfiguration that caused major cash-flow issues post-go-live and required six months to fix.

A Lifecycle Approach to Getting Data Migration Right

Organizations that execute migration well treat it as a structured lifecycle with clearly defined phases:

Discovery and Landscape Assessment

The checklist begins with a discovery-focused phase that assesses the data landscape. This step involves mapping both legacy and future-state data, identifying data owners, and determining which data sets should be archived or retained. It also includes profiling data values to uncover gaps, duplicates, and quality issues early in the process. This foundational assessment ensures teams have a clear understanding of the data environment before any transformation begins.

Validation and Governance

Building on the initial assessment, the checklist emphasizes establishing strong validation and governance practices. Through sections such as “Create the Communication Process” and “Build the Quality, Transformation, and Validation Processes,” it outlines the creation of communication and readiness templates, the definition of a data quality strategy and review cadence, and the development of validation and reconciliation requirements. These steps help ensure alignment across teams and maintain data integrity throughout the initiative.

Transformation and Cutover Planning

Once governance structures are in place, the checklist shifts focus to execution readiness. The sections titled “Capture the Detailed Requirements” and “Execute the Transformation and Quality Strategy” highlight the importance of documenting conversion specifications, managing transformation activities, and capturing execution steps within detailed runbooks. This phase also includes preparing data load files to support a controlled and efficient cutover.

Reconciliation and Sign-off

The checklist concludes with a dedicated reconciliation and sign-off phase. In the “Validate and Reconcile the Data” section, both business validation and reconciliation reporting are required to confirm that the data is technically accurate and fit for business use. This final confirmation ensures confidence in the data before go-live and reduces the risk of post-implementation issues.

This approach does not slow programs down. It reduces rework, protects operational continuity, and sets the stage for adoption. Teams that adopt this rigor transform data migration from a risk factor into a risk control.

How Strategic Data Migration Compounds Business Value

Done right, migration becomes a multiplier. Clean data fuels user adoption, stabilizes reporting, and accelerates optimization. Business teams gain confidence in the system because it behaves as expected.

For regulated environments, strategic migration strengthens compliance posture and minimizes audit exposure. And across industries, it creates something even harder to quantify, like trust. Trust in the system, in the reports, and in the decisions they support.

One global automotive parts manufacturer failed two migration attempts before seeking external support.  The failures stemmed from automation and traceability issues. With structured automation and a fully integrated validation process, the third attempt succeeded with high trust and shortened cutover migration time from 6 to 2 weeks.

When the Migration Holds, Transformation Thrives

Definian’s approach embeds Accuracy, Completeness, and Continuity across the migration lifecycle. Our teams bring:

• Reusable migration templates that reduce execution time
• Automated validation checks built into data loading
• Cutover playbooks aligned with critical business cycles
• Exception handling and real-time collaboration with business stakeholders

The result is not just a clean cutover. It is a durable data foundation that powers transformation long after go-live.

Unlike most system integrators who focus narrowly on the final data load, often leaving the most complex activities to the client, Definian owns the full lifecycle. We build a predictable, repeatable, and highly automated process from initial extraction through load, including data governance, quality, and system readiness. In fact, the majority of the work happens before the first template is loaded.

Final Thought

Digital transformation can succeed without all reports or workflows on day one. It cannot succeed without trusted data. A broken integration delays a feature. Broken data undermines the business.

Executives are right to treat data migration as a business-critical priority. The organizations that get it right the first time do not just avoid remediation costs; they gain execution velocity, compliance confidence, and the trust required to scale.

Before your next go-live, validate whether your data is truly ready at definian.com.

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About the Author: Steve Novak is Principal and Data Engineering Practice Lead at Definian, where he specializes in high-stakes migration and transformation projects for Fortune 500 clients.

Challenges

One of the world’s most iconic technology brands set out to reinvent how it uses people data to power smarter, faster decisions across 200,000+ employees. At the same time, it was migrating to Workday, shifting to Snowflake, and rebuilding its entire dashboard ecosystem. Fragmented data and inconsistent definitions were slowing decision-making and eroding trust.

The Solution

Definian partnered with the client to modernize people analytics from the ground up.

• Migrated legacy HR data into Snowflake
• Aligned historical data with new Workday structures
• Built a scalable semantic layer in dbt for key metrics
• Rebuilt dashboards in Tableau with input from business users
• Delivered faster, more accessible insights across teams

How We Delivered

Working within the client’s Snowflake, dbt, and Tableau ecosystem, we preserved all historical data, implemented role-based privacy, and validated every metric. With a product-first mindset and embedded collaboration, we delivered value at every stage, not just at go-live.

Results

• 10,000+ dashboard views per month
• 30+ refreshed dashboards supporting workforce planning
• A single source of truth for HR, recruiting, and performance data
• Executive trust rebuilt through consistent, accurate insights
• Decisions made faster, with more confidence

Today, the platform powers real-time planning across the organization and has transformed how leadership interacts with data.

Get in touch with us

In high-stakes business environments, decision latency creates strategic risk. When a key metric appears in a leadership meeting, the first response is often not action but interrogation: Is the number accurate? Who owns it? What does it include or exclude? This is not anecdotal; it is systemic.

Organizations have invested heavily in data infrastructure, from cloud platforms to BI dashboards to AI models, yet confidence in the data remains low. According to Accenture, nearly half of CXOs report insufficient high-quality data to operationalize GenAI. A Gartner Report predicts that by 2027, 60 percent of AI initiatives will fail due to inadequate data governance.

The issue is not the technology. It is the absence of clarity.

Where governance lost strategic alignment

Data governance began as a compliance function focused on minimizing risk, securing access, and meeting regulatory obligations. Over time, it absorbed every undefined or misaligned element of the data environment: inconsistent definitions, undocumented lineage, fractured ownership, and semantic drift.

As a result, governance became marginalized, perceived as bureaucratic overhead rather than a source of business value. It remained reactive instead of strategic.

The future of governance must shift from control to enablement. Its purpose is confidence, and confidence depends on clarity.

Data Clarity: the foundational prerequisite to decision velocity

Clear data is not a qualitative aspiration. It is a measurable driver of enterprise agility. In practice, clarity means:

• Metrics are consistently defined across systems and stakeholders
• Data lineage is transparent and traceable
• Quality thresholds are understood and agreed upon
• Ownership is explicit, not assumed

Without these elements, organizations face decision friction that slows execution. Clarity shortens validation cycles, reduces rework, and enables confident action.

If an executive team cannot align on the definition of customer acquisition cost within minutes, they cannot steer marketing, product, and finance toward a unified outcome.

Where data clarity breaks: applied examples

A large US port authority: Misaligned hierarchy impacted regulatory reporting

Monthly tonnage reporting depended on a roll-up called general cargo, but finance and operations defined the category differently. The conflict raised immediate questions about accuracy and eroded confidence in the data. The issue was bigger than a single metric. When reference data and hierarchy roll-ups aren’t consistently defined, reporting and forecasting inherit the ambiguity. A clear, shared definition resolved the discrepancy and restored trust in the metric.

At a state medicaid agency: Conflicting definitions of a health provider

The agency struggled with a basic question: who qualifies as a provider? Traditional definitions failed when atypical providers were involved, such as a neighbor reimbursed for transporting a patient to a distant hospital. Without standard definitions, teams applied different interpretations, leading to inconsistent reports and increased audit exposure. Defining a provider as any person reimbursed for Medicaid services created a single, durable standard that stabilized reporting and removed ambiguity at the source.

Inside a global retailer: Product attribution lacked governance

A vendor introduced a complex color called Cornflour. When teams entered new items, they interpreted the simple color differently. Some mapped it to yellow, others to blue. The correct simple color was blue, but without a governed process for attribute management, the decision was left to individual judgment. This inconsistency flowed directly into reporting, assortment planning, and presenting products online. By establishing a controlled, complex-to-simple color mapping that is managed once and published for enterprise use, the retailer eliminated interpretation, improved data consistency, and restored confidence in product attributes.

These are not isolated cases. They reflect a broader clarity gap.

The unspoken cost: analyst throughput and strategic bandwidth

When clarity is missing, high-value resources are pulled into low-leverage work:

• Identifying data origin
• Revalidating definitions
• Reconciling contradictory reports
• Requesting manual context

How Definian Makes Data Clarity Operational

Data clarity is not a side project; it is a leadership lever. Definian helps organizations transform governance from a static control function into a performance enabler. The goal is straightforward. It eliminates the execution drag created by unclear definitions, fragmented ownership, and inconsistent quality standards.

The work begins with a diagnostic. Definian identifies where decisions stall, where critical definitions diverge, and where systems or tools are misused because teams lack a shared understanding. From there, the process moves into visibility. A comprehensive inventory of critical data assets ensures that governance is grounded in reality rather than assumptions.

The next step is semantic alignment. Definian facilitates agreement on the meaning of terms such as “customer,” “margin,” and “booking” so that they stand up across functions. This is not theoretical. It is how teams stop debating metrics and start trusting them. Defining what good data looks like follows three principles: completeness, timeliness, and reliability. These thresholds are agreed on, not guessed.

Accountability is then operationalized. Every data set has a clear owner, and every conflict has a defined resolution path. This structure enables scaling clarity without increasing complexity. Importantly, Definian does not expect organizations to fix everything at once. It starts with a single, high-friction issue that is already blocking execution, resolves it, demonstrates value, and builds from there.

This approach turns clarity into a capability, not a project.

What leaders gain

Executives do not need more dashboards. They need fewer unknowns. When clarity is embedded into operations, decision cycles shrink, cross-functional alignment improves, and leaders spend less time validating and more time executing. The result is higher confidence in every strategic move, from reporting and forecasting to AI and automation.

In a world where most companies are data-rich but trust-poor, clarity separates the organizations that move quickly from those that stall.

Solving the Bottleneck First

‍For CDOs, CFOs, and CTOs facing governance fatigue and inconsistent data trust, the starting point is simple. Ask one question: Where is unclear data slowing us down? That single blocker is where clarity begins.‍

Talk to Definian today!

One of the world’s most recognized tech brands, operating across six continents with 200,000+ employees, faced a major challenge: fragmented, inconsistent, and underused people data.

Despite world-class infrastructure, HR, payroll, recruiting, and engagement systems remained siloed. Leaders couldn’t easily access insights without analyst support. Definitions for key metrics, such as attrition, varied across systems, causing delays, confusion, and eroding trust in analytics.

The Solution

Definian helped the client reimagine people analytics as a product rather than just reporting.

What We Built

• Unified Data Stack: Combined HR, payroll, recruiting, and performance data in Snowflake.
• AI Search: Built a natural language tool for managers to ask questions like “What’s my team’s attrition risk?” and get clear, permissioned answers.
• Predictive Forecasting: Deployed machine learning pipelines using dbt, reducing forecast time from days to minutes.
• Interactive Dashboards: Designed smart interfaces that prioritize key insights and allow quick follow-ups.
• Privacy by Design: Role-based access controls and audit trails from day one.
  

How We Delivered

• Co-developed the platform with users through weekly feedback
• Insights tested and refined continuously
• Delivered tangible value in the first month
  

The Results

• Faster Decisions: Forecasts that took days now run in minutes.
• Wider Access: Managers get real-time answers without waiting on reports.
• Smarter Questions: Trusted data empowered more strategic decision-making.
• Lasting Change: The platform became a daily tool for leadership and planning.
  

Want to see what this could look like for your organization?

Get in touch with us

Every October, we find ourselves drawn to what hides in the shadows. But in enterprise data, the real fear isn’t what jumps out; it’s what stays hidden.

In business, the darkness doesn’t wear a mask. It creeps in through uncertainty. Quietly. Systematically. And it often originates not outside the organization, but deep within its data infrastructure.

Across industries, two specific fears continue to surface. Both thrive in the murky space between speed and understanding.

1. The Fear of Broken Truth

Every organization is chasing modernization with AI-powered insights that promise agility and foresight. Yet Gartner reports that 60% of digital transformations stall. The reason? A lack of trust in internal data.

Velocity increases. But lineage becomes a blur. Automation scales. Governance struggles to catch up. When a CFO starts questioning the numbers, or when two dashboards disagree on a single KPI, what emerges isn’t just inefficiency. It’s doubt.

And doubt, as it turns out, is expensive. Deloitte estimates the cost of poor data quality at $12.9 million per year for the average organization.

Trust in data isn’t a byproduct of more tools. It comes from clear traceability. Data lineage and governance must be embedded directly in the operational core, not as afterthoughts, but as the scaffolding for every decision. When insight can be traced to its origin, confidence follows.

2. The Fear of Unseen Bias

Artificial intelligence is the new frontier. But like all frontiers, it comes with shadows.

Many enterprises are learning the hard way that opacity in models creates risk. Gartner predicts that by 2026, 60% of AI projects will fail. The root cause? Foundations that weren’t designed to support AI in the first place.

We see it often: models that drift quietly, algorithms that inherit bias, recommendations that look accurate but lack context. And the cost isn’t just technical, it’s cultural.

When leaders can’t explain why a system made a decision, they begin to lose confidence in every decision. Which is why explainability in AI is no longer a feature; it's a necessity. Frameworks that prioritize transparency ensure every model output can be traced, audited, and understood because real intelligence isn’t just predictive, it’s accountable.

Turning Fear into Foresight

Uncertainty doesn’t vanish with new software. It only recedes when clarity is baked into the system. And clarity isn’t a dashboard. It’s an environment. Built on verified lineage, sustained through active governance, and strengthened by transparent models and continuous validation.

Enterprises that prioritize clarity replace digital anxiety with data confidence. Architectures built for understanding help every stakeholder see, question, and trust the insights in front of them.

That’s why every transformation must begin with a simple but high-stakes question:
Can we trust our data enough to act boldly?

Because true modernization doesn’t just update systems, it upgrades confidence.

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What is Data Visualization?

With so much information being collected through data analysis in the business world today, we must have a way to paint a picture of that data so we can interpret it. Data visualization gives us a clear idea of what the information means by giving it visual context through maps or graphs. This makes the data more natural for the human mind to comprehend and therefore makes it easier to identify trends, patterns, and outliers within large data sets.

Why is Data Visualization Important?

No matter what business or career you’ve chosen, data visualization can help by delivering data in the most efficient way possible. As one of the essential steps in the business intelligence process, data visualization takes the raw data, models it, and delivers the data so that conclusions can be reached. In advanced analytics, data scientists are creating machine learning algorithms to better compile essential data into visualizations that are easier to understand and interpret.

Specifically, data visualization uses visual data to communicate information in a manner that is universal, fast, and effective. This practice can help companies identify which areas need to be improved, which factors affect customer satisfaction and dissatisfaction, and what to do with specific products (where should they go and who should they be sold to). Visualized data gives stakeholders, business owners, and decision-makers a better prediction of sales volumes and future growth.

What Are The Benefits of Data Visualization?

Data visualization positively affects an organization’s decision-making process with interactive visual representations of data. Businesses can now recognize patterns more quickly because they can interpret data in graphical or pictorial forms. Here are some more specific ways that data visualization can benefit an organization:

• Correlations in Relationships: Without data visualization, it is challenging to identify the correlations between the relationship of independent variables. By making sense of those independent variables, we can make better business decisions.
• Trends Over Time: While this seems like an obvious use of data visualization, it is also one of the most valuable applications. It’s impossible to make predictions without having the necessary information from the past and present. Trends over time tell us where we were and where we can potentially go.
• Frequency: Closely related to trends over time is frequency. By examining the rate, or how often, customers purchase and when they buy gives us a better feel for how potential new customers might act and react to different marketing and customer acquisition strategies.
• Examining the Market: Data visualization takes the information from different markets to give you insights into which audiences to focus your attention on and which ones to stay away from. We get a clearer picture of the opportunities within those markets by displaying this data on various charts and graphs.
• Risk and Reward: Looking at value and risk metrics requires expertise because, without data visualization, we must interpret complicated spreadsheets and numbers. Once information is visualized, we can then pinpoint areas that may or may not require action.
• Reacting to the Market: The ability to obtain information quickly and easily with data displayed clearly on a functional dashboard allows businesses to act and respond to findings swiftly and helps to avoid making mistakes.

Which Data Visualization Techniques are Used?

There are many different methods of putting together information in a way that the data can be visualized. Depending on the data being modeled, and what its intended purpose is, a variety of different graphs and tables may be utilized to create an easy to interpret dashboard. Some visualizations are manually created, while others are automated. Either way, there are many types to meet your visualization needs.

• Infographics: Unlike a single data visualization, infographics take an extensive collection of information and gives you a comprehensive visual representation. An infographic is excellent for exploring complex and highly-subjective topics.
• Heatmap Visualization: This method uses a graph with numerical data points highlighted in light or warm colors to indicate whether the data is a high-value or a low-value point. Psychologically, this data visualization method helps the viewer to identify the information because studies have shown that humans interpret colors much better than numbers and letters.
• Fever Charts: A fever chart shows changing data over a period of time. As a marketing tool, we could take the performance from the previous year and compare that to the prior year to get an accurate projection of next year. This can help decision-makers easily interpret wide and varying data sources.
• Area Chart (or Graph): Area charts are excellent for visualizing the data’s time-series relationship. Whether you’re looking at the earnings for individual departments on a month to month basis or the popularity of a product since the 1980s, area charts can visualize this relationship.
• Histogram: Rather than looking at the trends over time, histograms are measuring frequencies instead. These graphs show the distribution of numerical data using an automated data visualization formula to display a range of values that can be easily interpreted.

Who Uses Data Visualization?

Data visualization is used across all industries to increase sales with existing customers and target new markets and demographics for potential customers. The World Advertising and Research Center (WARC) predicts that in 2020 half of the world’s advertising dollars will be spent online, which means companies everywhere have discovered the importance of web data. As a crucial step in data analytics, data visualization gives companies critical insights into untapped information and messages that would otherwise be lost. The days of scouring through thousands of rows of spreadsheets are over, as now we have a visual summary of data to identify trends and patterns.

Conclusion

We need data visualization because the human brain is not well equipped to devour so much raw, unorganized information and turn it into something usable and understandable. We need graphs and charts to communicate data findings so that we can identify patterns and trends to gain insight and make better decisions faster.

At Definian, we understand the importance of data visualization and what it means to our clients. We provide them with user-friendly and beautiful visualization features and tools to depict their data in a clear and meaningful way. We’re here to ensure our clients have everything they need to make quick and informed decisions based on sound data that is easy to interpret. Contact our friendly team of professionals at analytiks today to hear how we can better your business.

Accurate, consistent, and well-governed master data is critical to a business’ operations. For our manufacturing client, their master data was managed inconsistently across systems, creating inefficiencies in operations. The organization was uncertain about the effectiveness of the proposed ERP implementation. This feeling was driven by concerns over data quality, reporting issues, and duplication across legacy systems – all of which were impacting business efficiency.

As the client prepared for a future Enterprise Resource Plann (ERP) consolidation, an MDM strategy was essential to ensure data integrity, reduce manual workarounds, and improve decision-making for master data used throughout the organization. Definian stepped in to provide a helping hand in crafting an MDM strategy and potential solutions to set them up for their future implementation.

Business Challenges

• System Fragmentation Due to M&A: The client had multiple business units on different legacy ERP systems resulting from mergers and acquisitions, leading to data duplication and inconsistent reporting.
  
  • Duplication in Supplier information led to inaccurate supplier performance reporting, operational inefficiencies, and even strained supplier relationships. Inaccurate addresses created problems with product returns and on-time payments.
  • Duplicate customer records led to duplicative communications and damaged relationships. Inaccurate delivery addresses and contact details disrupted communication, delayed payments, and sometimes led to missed deliveries.

• Lack of a Centralized Source of Truth: There was no authoritative system for customer, product, or supplier data as the information is all coming from different legacy systems. A lack of a source of truth hindered reporting and diminished operational efficiency.
• SKU Proliferation: Significant data quality issues stemmed from SKU proliferation. Our root cause analysis revealed that poor interfacing and design decisions between their product configurator and ERP system led to the continuous creation of redundant and overly specific product records.
  
  • The product configurator created a new SKU for every unique product permutation, and the ERP blindly accepted each as a new, valid SKU.
  • Existing processes resulted in millions of SKUs, despite the client offering only around 50 base products.
  • Analysis showed that 80% of SKUs had no inventory or transactional activity for over two years, indicating massive redundancy.
  • There was no SKU lifecycle management process, and no logic in place to check for existing similar configurations before creating new SKUs.

• Lack of Governance and Ownership: There was no defined data governance model to manage data quality, stewardship, or ownership, which is critical for sustained MDM success.

Facing these business challenges and heading into a future ERP implementation, the client needed to assess the best steps to take to ensure success. It was imperative they considered MDM as one of those steps.

How do I know that I need MDM?

There are four primary triggers for an MDM Implementation:

• My organization is considering an ERP implementation. There is no better time to investigate your data management process and data quality than when you are considering a mass migration to a new target system. A solid MDM plan can save your resources hours upon hours of work dealing with bad data.
• My organization is considering an MDM tool. The right tool for you is out there, but you need experts to help you figure out just exactly how to utilize that tool.
• My organization has gone through mergers and acquisitions. M&A can be a headache on data quality, as numerous legacy systems are often brought into the mix and allow for duplication and inaccurate reporting.
• My organization has a Product Information Management system that needs an overhaul. Your PIM needs assistance, as it is likely to create more SKUs than you need due to custom product attributes.

Our client faced all these issues, so it was clear from a business perspective to pursue an MDM implementation. In came Definian, and after months of work, the team delivered solutions and a scalable strategy.

MDM Strategy and Solutions We Delivered

• Master Record Structures: Defined master record structures for customer and product data, focusing on essential attributes and matching rules critical to daily business operations. This supports data consolidation and cleansing efforts.
  
  • Key attributes included Customer Name and Class and how to match up to the address and contact information in the legacy systems. This analysis streamlined the uncovering of duplicate Customers, including over 1300 duplicate groups in one legacy system.
  • Through match and merge rules, the entity resolution process will accurately consolidate these records, merging multiple customer instances into a single, authoritative golden record.
  • Through this consolidation, the client maintains an accurate, unified customer record, which simplifies reporting and eliminates redundant data management efforts. This process establishes a trustworthy foundation for operational efficiency, informed decision-making, and reliable strategic insights.
• Governed Workflows: Developed onboarding workflows determining where data should originate (e.g., engineering (PLM) vs. sales (ERP)) and how approvals and data updates should be managed, tailored to centralized MDM models.
  
  • This allows for a streamlined process in which everyone knows their role, and the process does not rely on outside factors to inhibit the efficiency of data entry. Reducing manual entry minimizes user error and its impact.
• Root Cause Analysis of SKU Issues: Definian identified the configurator logic and ERP behavior as causes for these issues. We provided the following recommendations to the client as solutions:
  
  • Implement SKU lifecycle management: to archive or purge inactive SKUs.
  • Add validation mechanisms to the ERP to detect and reuse existing SKUs
  • Improve cross-functional communication between business units and system stakeholders to reduce fragmentation.
  • Integrate MDM as a preemptive step before ERP consolidation to create a clean, authoritative source of master data, including products.
• Data Governance Framework: Introduced accountability for data ownership and stewardship within master data domains, and embedded governance considerations into MDM designs to ensure long-term data health.
• Support for Future ERP Migration: Positioned MDM as a preparatory step to ensure clean, consistent data feeds into the planned ERP consolidation, reducing downstream complexity and risk.

Definian provided a pathway to data readiness for the client. The business benefitted from proactively addressing their data quality before launching an ERP transformation, recognizing that clean and governed master data is foundational for successful digital transformation. It is important to build the foundation and toolbox for migration with an MDM framework in place and smartly look for a partner to help your organization solidify that foundation before embarking on an ERP migration. By being proactive, this client allowed themselves to understand the best tools to utilize the build up their data quality before it needs to be fixed.

Looking for a reliable partner for your ERP implementation? Reach out to our data experts for more information!

Recent changes in federal reporting requirements are forcing organizations to rethink how they manage and maintain Human Capital Management (HCM) data. The rollback of affirmative action mandates, evolving EEOC guidelines, and new executive orders mean that HCM data leaders must reassess their data retention policies, compliance strategies, and reporting mechanisms. These shifts create practical challenges: what data must be retained, how should it be structured, and how can organizations ensure they meet compliance standards without introducing legal risks? Staying ahead requires a proactive approach to understanding these regulations and implementing structured data management strategies.

The Legal Landscape: EEOC, AAP, and Executive Orders

Recent policy shifts have altered the way companies must handle demographic and employment data. A key example is the revocation of Executive Order 11246, which previously mandated affirmative action programs (AAP) for federal contractors. While AAP requirements may be gone, Equal Employment Opportunity Commission (EEOC) reporting obligations remain. This means that businesses must still retain and report demographic data, but they must ensure it is not used in ways that could be interpreted as discriminatory in hiring, promotions, or transfers.

From a data management perspective, this creates a unique challenge: companies must maintain high-quality demographic data for compliance while ensuring that such data does not influence employment decisions in an unlawful way.

What This Means for HCM Data Migrations

For organizations transitioning to Oracle HCM Cloud or another ERP system, these changes impact how demographic data is handled during conversion. Key considerations include:

• Retaining Critical Data Fields: Even if affirmative action reporting is no longer required, demographic details like race, gender, and veteran status should still be preserved for EEOC reporting and internal compliance audits.
• Handling Opt-In and Missing Data: Employees can choose to opt out of providing demographic data. However, for reporting purposes, some organizations may attempt to infer or categorize missing data—a risky practice that can lead to ethical issues or compliance violations.
• Role of Legal and HR Collaboration: Given the legal complexities, data migration specialists should work closely with HR and legal teams to ensure that reporting structures comply with the latest regulations.

Best Practices for HCM Data Conversions

1. Define Required vs. Optional Data Fields: Understanding which fields are legally required vs. optional helps create clear data validation rules.
2. Implement Pre-Validation Checks: Running pre-validation checks before migration ensures missing or incorrect data can be corrected before entering the new system.
3. Leverage Automated Tools: Specialized data migration tools or pre-built data validation scripts can accelerate the conversion process and reduce manual errors.
4. Ensure Compliance Through Documentation: Maintain thorough documentation of data decisions and mappings to provide an audit trail in case of compliance reviews.
5. Educate Stakeholders: Training HR and IT teams on new compliance requirements ensures that data is handled correctly post-migration.

Looking Ahead

HCM data compliance is not a static process. As policies continue to shift, organizations must remain proactive in updating their data governance strategies. Engaging with legal and compliance experts early in the migration process can help mitigate risks and ensure a smooth transition.

For those involved in HCM data migrations, the goal is clear: deliver accurate, compliant, and well-structured data that supports business operations while aligning with legal obligations. By taking a structured approach to data conversion, organizations can navigate regulatory changes with confidence and efficiency.

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CHICAGO, IL, January 29, 2025 — Premier International, a global leader in technology and data solutions, today announced the acquisition of Analytiks, an advanced data analytics firm. The strategic acquisition expands Definian's capabilities in transforming complex enterprise data into actionable business value.

Analytiks, a Colorado-based business intelligence and data analytics company, has established itself as a trusted partner in helping organizations harness their data assets to drive growth and operational excellence. The company's expertise spans data visualization, data science, business intelligence, data integration, and governance frameworks that enable real-time decision-making for enterprise clients.

The integration will strengthen Premier International's analytics practice, with Analytiks' CEO Mathieu Stark assuming the role of Practice Lead for Data Value Realization. Under Premier International's umbrella, the combined organization will continue serving Analytiks' prestigious client portfolio, which includes leading global technology enterprises.

"This acquisition represents a significant milestone in Premier International's mission to help clients unlock the full potential of their data assets," said Craig Wood, CEO of Premier International. "Analytiks' proven expertise in real-time analytics and data visualization complements our existing capabilities, enabling us to deliver even more comprehensive, innovative solutions to our global client base. We are thrilled to welcome Mathieu and the Analytiks team to Definian."

Mathieu Stark, Founder and CEO of Analytiks, added, "By joining forces with Premier International, we can dramatically scale our impact and deliver enhanced value to clients worldwide. Our shared vision of data-driven transformation and core values makes this partnership particularly compelling, and I'm excited to lead the Data Value Realization practice in this next phase of growth."

The acquisition follows Premier International's successful integration of Information Asset in 2023, further solidifying the company's position as a comprehensive provider of data value chain solutions for enterprises globally.

Jane Buckley, Vice President of Renovus Capital Partners, Premier International's growth capital partner, noted, "This strategic acquisition aligns perfectly with our vision of establishing Definian as the market leader in enterprise data solutions. The newly combined entity creates a formidable player in the data space that will drive significant value for clients."

Terms of the transaction were not disclosed.

For more information on Premier International's Data Insights offering, please visit this page on our website.

About Premier International (now Definian)

Premier International helps organizations unleash the full potential of their data. Our expertise in data-led business transformation, data fundamentals and migration, risk management and data value realization helps companies maximize the value of their data, empowering them to achieve success across the entire data value chain. Having successfully completed thousands of projects since our founding in 1985, many of the world’s leading brands, technology companies, and system integrators rely on Premier International to solve their most complex data challenges. For more information, please visit Definian.com.

About Renovus Capital Partners

Founded in 2010, Renovus Capital Partners is a lower middle market private equity firm specializing in the Knowledge and Talent industries. From its base in the Philadelphia area, Renovus manages over $1 billion across its three sector focused funds and other strategies. The firm’s current portfolio includes over 25 U.S. based businesses specializing in education and training, healthcare services, technology services and professional services. Renovus typically partners with founder-led businesses, leveraging its experience within the industry and access to debt and equity capital to make operational improvements, recruit top talent, pursue add-on acquisitions and oversee strategic growth initiatives. For more information, please visit renovuscapital.com.

Data quality is a critical business imperative. Poor data quality impacts organizations of all sizes and industries, leading to broken processes, inaccurate reporting, and a loss of trust in decision-making systems. Despite this awareness, many organizations still struggle to address data quality due to the scale of the challenge. Traditional approaches, which rely heavily on manual processes, are no longer sufficient. The modern solution is to embrace a machine-first approach that leverages automation, AI, and machine learning to streamline data quality management, ensuring faster and more accurate outcomes.

The Shift to Modern Data Quality: From People-First to Machine-First

Traditional data quality frameworks are reactive, requiring manual effort to define standards, build rules, and address issues. This model often leads to delays and inefficiencies, particularly as data volumes grow. Moreover, traditional solutions can be difficult to scale, limiting their ability to handle the increasing variety and velocity of data.

Modern data quality flips this script by taking a machine-first approach. Organizations can use AI and machine learning to automate key processes, such as data profiling, rule generation, and anomaly detection. This reduces the need for manual intervention, allowing organizations to maintain high data quality standards more efficiently and achieve faster time-to-value.

Key Features of Modern Data Quality Solutions

1. AI and Automation: Modern solutions use AI/ML to automate data profiling, rule generation, and anomaly detection. These solutions analyze vast datasets to identify inconsistencies and suggest corrections, streamlining the remediation process and reducing the workload for data stewards.
2. Real-Time Monitoring and Observability: Unlike traditional frameworks that rely on batch processing, modern solutions offer continuous monitoring of data flows and pipelines. This enables organizations to detect and address issues in real time, minimizing disruptions.
3. Scalability and Adaptability: Modern solutions integrate easily with cloud-based systems and can adapt to new data sources. This ensures that organizations can maintain consistent data quality across all systems, no matter how complex their data environment.
4. Self-Service and User Empowerment: These solutions feature intuitive, user-friendly interfaces that empower non-technical users to engage directly with data quality processes. This reduces dependency on IT, fosters greater transparency, and encourages broader data literacy across the organization.
5. Robust Issue Identification and Resolution: Modern data quality solutions enable organizations to quickly identify and resolve data issues at various levels, including attribute, table, and dataset. Equipped with advanced dashboards, root cause analysis, and impact analysis through data lineage, these solutions help teams detect and correct data problems more efficiently, ensuring data reliability and minimizing downtime caused by errors.
6. Customizable Solution Capabilities: Rather than relying solely on pre-built features, today’s data quality solutions offer a flexible mix of ready-to-use functionalities and customizable workflows. This enables organizations to tailor the platform to their specific data quality needs, enhancing adaptability and effectiveness across varied use cases.

Why Modern Data Quality Matters: Key Business Benefits

Poor data quality impacts every aspect of an organization, from business processes and customer experience to delivering accurate insights and enabling strategic decision-making. Inaccurate or incomplete data leads to bad decisions, inefficiencies, and missed opportunities. By adopting a machine-first approach to data quality, organizations can achieve:

• Faster Insights: Automation reduces the time needed to prepare data for analysis, providing faster access to insights and quicker decision-making.
• Increased Trust and Confidence: Consistently high-quality data builds trust among stakeholders, enabling informed decisions with confidence.
• Cost Savings: Reducing manual intervention lowers operational costs associated with maintaining data quality programs.
• Scalability and Flexibility: Modern solutions can scale effortlessly, integrating with various data environments, whether on-premises, in the cloud, or across hybrid systems.

The Power of Data Quality as a Service (DQaaS)

Implementing a comprehensive data quality solution can still be daunting. Building and maintaining in-house data quality teams requires significant resources, making it a time-intensive and costly commitment that may not suit every organization’s needs. To address this, Definian International, a leading data services company, has partnered with DQLabs to offer Data Quality as a Service (DQaaS). This joint solution allows organizations to access advanced data quality capabilities without a significant capital investment.

DQLabs is an automated, modern data quality and observability platform that delivers reliable and accurate data for better business outcomes. The platform harnesses the combined power of Data Observability, Data Quality, and Data Discovery to help data producers, consumers, and leaders turn data into action more quickly, easily, and collaboratively. The platform is built with an automation-first approach, featuring self-learning and self-serve capabilities to empower all data users.

With DQaaS, organizations can:

1. Deploy Quickly and Efficiently: Connect data sources to the DQLabs platform and start seeing results within weeks. The service manages setup, integration, and monitoring, ensuring a smooth deployment.
2. Benefit from Predictable Costs: DQaaS is offered at a standard monthly fee, allowing organizations to manage their budget effectively while testing the benefits of modern data quality without significant capital expenditure.
3. Leverage Expert Guidance: Definian International’s team works closely with clients, providing expert guidance from setup to continuous improvement, ensuring alignment between data quality initiatives and business objectives.

Start Your Journey with Modern Data Quality

Transitioning to modern data quality doesn’t have to be complex or expensive. With DQaaS, Definian International and DQLabs bring a comprehensive, machine-first data quality solution to your business, enabling you to see immediate benefits. This service provides a low-risk, cost-effective way to experience modern data quality, helping you improve data accuracy, drive better business outcomes, and prepare for future data challenges.

If you’re ready to transform your data practices, contact Definian International today. Learn how DQaaS can help you achieve data excellence and unlock the full potential of your data.

Why Business Intelligence is Important

We are living in the age of technological progression. Digital advancements have completely revolutionized our everyday lives, and one of the largest impacts felt has been in the business world. Companies now have access to data-driven tools and strategies that allow them to learn more about their customers and themselves than ever before, but not everyone is taking advantage of them. Today we’re going to breakdown Business Intelligence and why it’s crucial to the success and longevity of your organization.

What Is Business Intelligence?

Before we jump into the importance, we must first understand Business Intelligence and how it applies to your company’s strategic initiatives. The term Business Intelligence (BI) refers to the technologies, applications, strategies, and practices used to collect, analyze, integrate, and present pertinent business information. The entire purpose of Business Intelligence is to support and facilitate better business decisions. BI allows organizations access to information that is critical to the success of multiple areas including sales, finance, marketing, and a multitude of other areas and departments. Effectively leveraging BI will empower your business with increased actionable data, provide great insights into industry trends, and facilitate a more strategically geared decision-making model.

To illustrate BI in action, here are a few departmental specific examples of insights and benefits that can come from its adoption and application:

• Human Resources: HR can tremendously benefit from the implementation of Business Intelligence utilizing employee productivity analysis, compensation and payroll tracking, and insights into employee satisfaction.
• Finance: Business Intelligence can help finance departments by providing invaluable and in-depth insights into financial data. The application of BI can also help to track quarterly and annual budgets, identify potential problem areas before they cause any negative impacts, and improve the overall organizational business health and financial stability.
• Sales: Business Intelligence can assist your company’s sales force by providing visualizations of the sales cycle, in-depth conversion rates analytics, as well as total revenue analysis. BI can help your sales team to identify what’s working as well as points of failure which can result in dramatically improved sales performance.
• Marketing: BI provides the marketing department with a convenient way to view all current and past campaigns, the performance and trends of those campaigns, a breakdown of the cost per lead and the return on investment, site traffic analytics, as well as a multitude of other actionable pieces of information.
• Executive Leadership: Plain and simple, Business Intelligence allows organizations to reduce costs by improving efficiency and productivity, improving sales, and revealing opportunities for continuous improvement. Business Intelligence allows members of Executive Leadership to more easily measure the organization’s pulse by removing gray areas and eliminating the need to play the guessing game on how the company is doing.

Why Is Business Intelligence Important?

Now you know what Business Intelligence is, what it’s capable of, but the question remains; why is Business Intelligence so important to modern-day organizations? The main reasons to invest in a solid BI strategy and system are:

• Gain New Customer Insights: One of the primary reasons companies are investing their time, money, and efforts into Business Intelligence is because it gives them a greater ability to observe and analyze current customer buying trends. Once you utilize BI to understand what your consumers are buying and the buying motive, you can use this information to create products and product improvements to meet their expectations and needs and, as a result, improve your organization’s bottom-line.
• Improved Visibility: Business Intelligent organizations have better control over their processes and standard operating procedures, as the visibility of these functions is improved by a BI system. The days of skimming through hundreds of pages of annual reports to assess performance are long gone. Business Intelligence illuminates all areas of your organization helps you to readily identify areas for improvement and allow you to be prepared instead of reactive.
• Actionable Information: An effective Business Intelligence system serves as a means to identify key organizational patterns and trends. A BI system also allows you to understand the implications of various organizational processes and changes, allowing you to make informed decisions and act accordingly.
• Efficiency Improvements: BI Systems help improve organizational efficiency which consequently increases productivity and can potentially increase revenue. Business Intelligence systems allow businesses to share vital information across departments with ease, saving time on reporting, data extraction, and data interpretation. Making the sharing of information easier and more efficient permits organizations to eliminate redundant roles and duties, allowing the employees to focus on their work instead of focusing on processing data.
• Sales Insight: Sales and marketing teams alike want to keep track of their customers, and most utilize Customer Relationship Management (CRM) application to do so. CRMs are designed to handle all interactions with customers. Because they house all customer communications and interactions, there is a wealth of data and information that can be interpreted and used to strategic initiatives. BI systems help organizations with everything from identifying new customers, tracking and retaining existing ones, and providing post-sale services.
• Real-Time Data: When executives and decision-makers have to wait for reports to be compiled by various departments, the data is prone to human error and is at risk of being outdated before it’s even submitted for review. BI systems provide users with access to data in real-time through various means including spreadsheets, visual dashboards, and scheduled emails. Large amounts can be assimilated, interpreted, and distributed quickly and accurately when leveraging Business Intelligence tools.
• Competitive Advantage: In addition to all of these great benefits, Business Intelligence can help you gain insight into what your competitors are doing, allowing your organization to make educated decisions and plan for future endeavors.

Conclusion

In summary, BI makes it possible to combine data from multiple sources, analyze the information into a digested format, and then disseminate the information to relevant stakeholders. This allows companies to see the big picture and make smart business decisions. There are always inherent risks when it comes to making any business decision, but those risks aren’t as prominent or worrisome when implementing an effective and reliable BI solution. Business Intelligent organizations can move forward in an increasingly data-driven climate with confidence knowing they are prepared for any challenge that arises.

The Definian team is here to improve your organization’s efficiency by leveraging your existing data. We will provide you with the tools your business needs to transform complex, unorganized, and confusing data into clear and actionable insights. This helps to speed your decision-making processes and ensures that all your business decisions are educated and backed with reliable data, and lots of it! Get in touch with the Definian team today to see how we can improve your business!

Last week, some of our team hit the road and headed to Vegas for Workday Rising. This year’s conference offered a deep dive into the challenges and opportunities facing organizations in a fast-moving digital landscape, where there is more technology and data available than ever before. Didn’t make it to the show? Here are the five key takeaways we brought back from the event:

1. Data Quality Remains a Priority (and a Challenge)

Organizations continue to struggle with data quality, highlighting the difficulty in accessing the data they need. A recurring pain point at the conference was the issue of handling historical data—how to store, access, and analyze it without burdening production systems. The sheer volume of data accumulated over time makes it hard to maintain quality while ensuring that critical information is accessible when needed. Companies need better strategies to tackle these data challenges, especially as they continue to rely on data-driven decisions.

2. Change Management Doesn't End After Go-Live

A crucial reminder from the event was that change management extends far beyond the go-live date of any new system implementation. Many attendees noted that using new systems posed unforeseen challenges, reinforcing the need for continuous support and training. Adapting to a new way of working requires time, and organizations must be proactive in managing change post-implementation to drive user adoption and system optimization.

3. Analytics: A Tool for Better Decision-Making

Another major focus at the conference was the importance of analytics in driving better-informed decisions. Organizations that can effectively leverage their data with the right analytical tools are able to make smarter decisions faster. Workday's commitment to improving analytics was evident throughout the conference, with discussions on how these tools can empower teams across finance, HR, and operations. As businesses evolve, those who excel at using analytics will gain a competitive edge.

4. Don’t Underestimate Data Migration

A key discussion repeated throughout the conference was the difficulty of data migration. Many organizations admitted that they were not fully prepared for the complexities involved in moving large amounts of data into new systems. Migrating data—especially when it spans multiple systems, formats, and decades—requires more than just technical know-how; it demands comprehensive planning and strategy. Workday Rising attendees shared stories of underestimating this process and the lessons learned from the experience.

5. Workday’s “Forever Forward” Vision: The Future of AI and Innovation

The overarching theme of the conference, “Forever Forward”, was perfectly aligned with Workday’s mission to continuously innovate. One of the most exciting announcements was Workday Illuminate™, a next-generation AI product designed to accelerate decision-making and problem-solving in HR and finance. In a world moving at lightning speed, Workday’s new AI tools, combined with Orchestrate, and Extend, are unlocking new possibilities for businesses to grow and innovate. This forward-thinking approach signals a future where AI is central to business operations, and those that leverage these technologies can achieve exponential growth.

As Workday continues to push the boundaries of what’s possible, companies have an opportunity to follow suit, embracing new technologies and strategies to stay ahead in a fast-paced world.  If your organization is looking to tackle any of these key areas, the Definian team would love to help. As an official Workday partner, we have been successfully helping many companies successfully migrate to Workday and unlock the full potential of their data beyond implementation.

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