麻豆传媒视频

Every professional in the construction industry understands the cost of rework. Whether it happens in the field or the back office, fixing preventable issues is almost always more expensive than getting things right the first time.

The same principle applies to . Inconsistent data creates rework, extends timelines, increases implementation costs, and slows time-to-value.

The Total Economic Impact鈩� of SAP Advanced Data Migration and Management by 麻豆传媒视频, a commissioned study conducted by Forrester Consulting on behalf of 麻豆传媒视频 and SAP in 2026, found that manual processes and poor made large-scale transformation slow, costly, and risky. Interviewed organizations, including a construction company, reported that duplicate records, incomplete information, and nonstandard formats across legacy systems required extensive manual review and correction before the data could be trusted for .

Understanding where transformation costs accumulate helps construction organizations avoid budget overruns and improve transformation outcomes.

Where Construction Transformations Actually Lose Money

The costs associated with SAP transformation are often attributed to technology, implementation, and change management. In reality, many of the most significant expenses stem from underlying data issues that disrupt operations before and after go-live. For construction firms, these costs typically emerge in procurement, project coordination, and decision-making, where accurate information is essential.

Material and Asset Data Inconsistencies Lead to Procurement Inefficiencies

Inconsistent material, equipment, and asset data create friction throughout the procurement process. It forces teams to manually reconcile information, increasing the likelihood of duplicate orders, delivery errors, missed volume discounts, and unplanned purchasing. Over time, these issues drive up costs and reduce project margins.

During transformation initiatives, these inefficiencies are even amplified. As organizations implement new systems and attempt to centralize procurement, inconsistent data gets embedded into standardized workflows, making errors harder to detect and more expensive to fix. Procurement slows down as teams compensate with manual checks, while project teams often bypass the system altogether to avoid delays. Instead of unlocking efficiencies, the transformation ends up scaling poor data quality across the business, cutting expected savings and delaying ROI.

Fragmented Project Data Increases Coordination Costs

Many construction companies keep critical project information across disconnected systems, spreadsheets, and teams. Without a reliable single source of truth, teams rely on constant back-and-forth communication to align on basic facts, leading to inefficiency and higher coordination costs in the form of delays, duplicated effort, and decision-making bottlenecks.

Transformation doesn鈥檛 automatically solve this. In fact, when data structures and reporting standards are inconsistent, integration becomes more difficult and trust in the new system decreases. Teams often fall back on parallel processes and offline tracking to maintain control, which maintains the same inefficiencies the transformation was meant to remove.

What Changes When Data Is Standardized

When data is standardized and governed across the organization, transformation efforts become more predictable because teams spend less time correcting errors, reconciling records, and resolving issues that should have been addressed earlier in the process. Instead, they can focus on execution, decision-making, and delivering project outcomes.

Standardization improves data conformity by ensuring information follows consistent definitions, formats, and business rules across the organization. Higher data conformity creates a stronger foundation for transformation. For construction firms, this means project teams, procurement, finance, and operations work from the same trusted data, reducing confusion and improving decision-making. Higher data conformity leads to:

• Fewer corrections, less rework: Standardized and clean data makes it easier to identify issues early, rather than waiting for them to surface during procurement, testing, or project execution. This reduces downstream corrections, project disruptions, and the cost of post-go-live remediation. In the TEI study, organizations reported lower project costs and fewer operational issues as a result of .
• More predictable project execution: A strong data foundation improves visibility, coordination, and across teams. With fewer manual processes and less time spent validating information, organizations can execute projects more efficiently and with greater confidence.

Real-World Example: From 45% to Nearly 99% Data Conformity

A construction organization interviewed by Forrester for the TEI study increased data conformity from approximately 45-46% to 98-99% after leveraging and strengthening their data foundation. The company also improved overall data quality by more than 40%, enhancing customer experience and preventing process disruptions.

Efficiency Gains a Cost Lever

Many enterprises face massive cost overruns during SAP rollouts because of data quality issues and limited tools or internal resources to handle complex data at scale. This gap forces a heavy and expensive reliance on external contractors and specialists just to keep the project moving.

Using an integrated data platform such as SAP ADMM addresses these gaps. Moving from manual workflows to a standardized, automated model lowers costs across three key areas:

• Less manual reconciliation across teams: Tracking changes via spreadsheets and emails creates administrative overhead and operational friction. Centralized workflows and automated task sequencing ensure data handoffs occur efficiently and in the correct order. This reduces time spent tracking approvals and allows teams to focus on strategic work.
• Fewer redundant workflows: Under legacy models, teams repeatedly re-enter, profile, and cleanse data across disparate systems. Automating validations and data cleansing captures errors early. This stops poor-quality data from entering production systems, minimizing IT support tickets and post-migration remediation.
• More predictable program timelines: Traditional migrations frequently require consecutive weeks of downtime to extract and snapshot data, which stalls project momentum. Automated transformation lifecycles replace this uncertainty with structural predictability, enabling multi-year programs to progress with minimal business disruption.

The Forrester TEI study found that organizations implementing SAP ADMM can achieve a 30% reduction in time spent on data management tasks and save around $2.1 million in cost through improved data and processes. The result is based on the experiences of interviewed organizations, which are combined to create a composite organization with 10,000 employees and annual revenue of $5 billion.

The Hidden Cost of Governance (and the Savings When It鈥檚 Solved)

is often viewed as a compliance box to check, but weak governance comes with a heavy price tag that extends far beyond compliance.

Without a centralized system to track data modifications, verification steps, and approval histories, companies must rely on scattered spreadsheets, emails, and manual records. This multiplies administrative overhead, requiring internal teams and expensive external contractors to spend hundreds of hours manually piecing together evidence for compliance. Because these documentation gaps also leave stakeholders blind to rule changes and error origination, any project adjustment often requires restarting the verification process from scratch. This lack of continuity introduces serious compliance risks and causes costly late-stage project delays.

When governance is embedded in the process, the impact is immediate. In the TEI study, the composite organization reduced audit preparation time by 80% by building consistent governance and traceability into core data processes. Governance and better data quality also strengthen the companies鈥� ability to support ongoing transformation initiatives without proportionally increasing headcount or external spending.

Strong data governance doesn鈥檛 just reduce compliance. It also eliminates recurring work, lowers administrative overhead, improves accuracy, and prevents late-stage issues that quietly inflate transformation costs.

Building the Foundation for Long-Term Transformation

The most successful SAP transformations do more than modernize systems. They create a foundation that enables organizations to operate more efficiently long after go-live.

For construction companies, that foundation is built on trusted, . Having standardized and governed data across projects allows teams to reduce the time spent fixing issues, executing repetitive tasks, coordinating around inconsistencies, and managing avoidable risks. This helps organizations enhance operational efficiency and cost savings.

The findings from the Forrester TEI study reinforce this connection between data quality and business value. From improved operational efficiency to reduced audit preparation effort, higher data conformity, and lower remediation costs, the study highlights how a trusted data foundation can translate into measurable financial impact.

To learn more about the financial and operational benefits organizations have achieved, download the full Forrester TEI study and explore the data behind the results.