Enterprise Scenarios
Scenarios where MyDataWork delivers outsized value
When MyDataWork is deployed across an analytics organization, it changes more than how individual analysts work. It changes what becomes possible at the scale of strategic initiatives — the kinds of projects that involve dozens of people, span months or years, and depend on knowing what your organization actually has.
These are the scenarios where MyDataWork’s value compounds: where the inventory, dependency mapping, stakeholder visibility, and value documentation built as a byproduct of daily work become the foundation for confident decisions about consequential change.
Below are three scenarios where MyDataWork delivers outsized value to organizations and the IT and analytics leaders accountable for them. The pattern is consistent across all three: when you know what you have, the strategic initiative becomes plannable rather than reactive.
Tool Migration: From Inventory to Confident Execution
The most common reason tool migrations fail is that nobody knows what they have.
Most organizations attempting to migrate from one analytical platform to another — Alteryx to Python, Tableau to Power BI, legacy ETL to a modern data stack, on-prem databases to Snowflake or Databricks — discover the same pattern. The first 30% of the migration goes smoothly. The middle is full of surprises. The last 30% reveals workflows nobody documented, dependencies nobody mapped, and stakeholders nobody consulted.
The result: projects run two to three times over schedule, consulting costs balloon, critical workflows break in production, and analyst trust in the new platform erodes before it has had a fair trial.
The root cause is foundational. There is no current, accurate inventory of what exists, who depends on it, what it is worth, and what is connected to what. That inventory has to be built from scratch, manually, while the migration is already underway. Analysts get pulled away from production work to document their own workflows. Stakeholders get surveyed in panic mode. Dependencies get discovered when they break.
How MyDataWork changes the migration calculus:
- A continuous inventory of every analytical asset across every tool, built as a byproduct of how analysts already work rather than as a separate documentation initiative. The inventory exists before the migration starts, and it stays current throughout.
- Dependency lineage showing which workflows feed which downstream systems, surfacing the “if we change this, what breaks” question before it becomes a production incident. Migration phases can be sequenced by dependency rather than guesswork.
- Stakeholder mapping showing who depends on each workflow. Migration communication becomes targeted rather than broadcast. The right people get notified about the right changes at the right time.
- Value documentation showing which workflows are high-impact (migrate carefully, validate thoroughly) versus low-impact (candidates for deprecation rather than migration). Migration scope can be scoped intelligently rather than treating everything as equally important.
Migration becomes plannable. Phases get sequenced based on risk and value. Stakeholders get notified accurately. Dependencies get verified before changes ship. Work gets reused rather than rebuilt. Critical workflows survive the transition with their context intact.
The cost math:
A typical mid-size analytics organization migrating from Alteryx to a Python and dbt stack runs 12 to 18 months, $300,000 to $1,000,000 in consulting fees and new licensing, and a measurable productivity dip during transition. Failed or significantly delayed migrations cost multiples of that.
A Team Growth plan at $1,440 per year, deployed across an analytics function for 18 months, costs $2,160 in MyDataWork licensing — roughly 0.2 to 0.7 percent of typical migration cost. The inventory and dependency mapping it provides typically save three to six months of migration timeline by eliminating discovery work that would otherwise happen reactively.
The investment is small. The downside protection is substantial.
Agentic AI: Knowing What to Automate Before You Build
Most failed AI initiatives fail not because the models were wrong, but because the context was missing.
Organizations are spending unprecedented resources on agentic AI initiatives — autonomous systems that take action on behalf of the business, from forecasting and pricing to customer engagement and supply chain orchestration. Gartner estimates that 40 percent of these projects will be canceled by 2027, primarily due to rising costs, insufficient risk controls, and unclear business value.
The common thread across the failures is context. Agentic systems need structured, traceable, queryable context about what work exists, what it depends on, who owns it, and what outcomes it produces. Without that context, AI initiatives stall in the gap between “we have models” and “we can deploy them safely.”
The current state in most organizations is recognizable. Strategy decks identify high-value automation candidates based on top-down assumptions. Pilots get launched against those assumptions. Pilots stall when they encounter the reality that the underlying analytical workflows are fragmented, undocumented, and known only to the analysts who built them. Initiatives lose momentum. Budgets get scrutinized. Some get canceled.
The pattern is not a model problem. It is a foundation problem.
How MyDataWork changes the agentic AI calculus:
- A bottom-up inventory of every analytical use case in the organization, built from how analysts actually work — not assembled in retrospect from interviews and surveys. The candidates for automation become visible as a byproduct of normal documentation, ranked by value, scoped by complexity, traceable to the assets they depend on.
- Dependency mapping showing what each candidate workflow needs to function. Before an agentic system is built to execute a workflow, the inputs, transformations, and outputs are documented and connected. The system has the context it needs to operate safely.
- Stakeholder ownership for every workflow. When an agentic system fails or produces unexpected results, accountability is clear. Escalation paths exist. The system is governable rather than mysterious.
- Value documentation distinguishing high-impact use cases from incidental ones. Investment goes toward automation candidates with measurable business value rather than convenient ones.
AI strategy becomes evidence-based. The inventory shows what exists, what it is worth, and what it depends on. Initiatives get prioritized by real value rather than by what is most visible to leadership. Pilots succeed because the underlying foundations are documented before the model layer is built.
The cost math:
A single failed agentic AI initiative at mid-size scale typically consumes $250,000 to $1,500,000 in consulting fees, internal time, vendor licenses, and opportunity cost. Most organizations attempting agentic AI today are running multiple such initiatives simultaneously, increasing the aggregate exposure.
A Team Growth plan at $1,440 per year, deployed across an analytics function as the foundation for AI strategy, costs less than 0.1 to 0.6 percent of a single failed initiative. The context infrastructure it provides is what gives agentic AI initiatives their best chance of succeeding past pilot stage.
The math is asymmetric. The upside of preventing one stalled initiative pays for organization-wide MyDataWork deployment for many years.
Mergers and Reorganizations: Integrating What Each Side Already Has
Most analytics integration challenges in M&A come from not knowing what either side actually has.
When two organizations merge, or when a single organization undergoes significant reorganization, the analytics integration challenge is usually treated as an afterthought to the broader business consolidation. It rarely gets the attention it deserves until the integration team encounters the actual scope of what they are trying to integrate.
Two sets of analytical tools, often overlapping. Two sets of duplicative workflows producing similar outputs in different formats. Two sets of stakeholders accustomed to different reporting cadences. Two sets of tribal knowledge held in the heads of analysts who may not stay through the integration. Two sets of vendor relationships and licensing agreements with different terms.
Most M&A analytics integrations either fail to consolidate (leaving the combined organization with permanent duplication and higher run costs) or take years longer than planned (extending the integration cost burden well beyond the deal close). Some create lasting cultural friction when one side’s tools are arbitrarily chosen over the other’s, particularly when the choice was made without understanding what each side actually depended on.
The root cause is the same as in tool migration, applied to a more complex situation. Without a clear picture of what each organization has, integration decisions get made based on assumption, politics, or whichever team pushed hardest for their preferred outcome. The decisions that follow shape the combined organization’s analytical infrastructure for years.
How MyDataWork changes the integration calculus:
- Rapid inventory of both organizations’ analytical work, on the same platform, in the same format. What previously took months of consulting engagement to assemble becomes visible within weeks of deployment. Both sides see what both sides have.
- Overlap identification surfacing where the two organizations are doing similar work in different ways. Consolidation opportunities become concrete rather than aspirational. The decisions about what to keep, retire, or merge get grounded in evidence.
- Dependency mapping showing what each workflow needs to function, on both sides. Integration phases get sequenced by what can be safely changed when. Critical dependencies get protected during transition rather than discovered when they break.
- Stakeholder mapping showing who depends on what, across both sides of the integration. Communication during transition becomes targeted. Stakeholders get heard before decisions affect their work, reducing political friction and integration resistance.
- Value documentation distinguishing strategic workflows from incidental ones. Integration energy gets focused on the workflows that matter to the combined business rather than spread evenly across everything that exists.
Integration becomes governable. Decisions get made on evidence rather than assumption. Stakeholders get included rather than surprised. Critical work gets protected rather than disrupted. The combined organization emerges with a coherent analytical infrastructure rather than two parallel systems running indefinitely.
The cost math:
Analytics integration in mid-size M&A typically consumes 18 to 36 months and several million dollars in consulting, parallel licensing, and lost productivity. Failed integrations result in permanent run-rate cost increases that compound annually. Stalled integrations leave the combined organization paying for redundant tools and infrastructure indefinitely.
A Team Growth plan at $1,440 per year, deployed across both organizations during integration, costs a fraction of a percent of typical integration cost. The inventory and overlap analysis it provides typically save six to twelve months of integration timeline by replacing manual discovery with continuous documentation.
The investment is small. The exposure being mitigated — years of redundant cost, lasting cultural friction, lost institutional knowledge — is large.