A practical, executive-level roadmap for reducing risk, restoring trust, and modernizing financial decision-making.
When I speak with finance leaders today, whether they are CFOs, CIOs, or CDOs, the conversation usually starts the same way: “We have more data than ever, yet our decisions aren’t getting better.”
They are right. A recent IDC study found that more than 80% of enterprise data is unused or underutilized. In financial services, that unused data often becomes data debt, the accumulated cost of outdated systems, inconsistent KPIs, manual reconciliations, and fragmented reporting.
You can see the impact everywhere. Close cycles drag on longer than they should. Regulatory numbers do not match across reports. Forecasts lose credibility the moment they are published. One CFO I worked with described the problem perfectly:
“It wasn’t that our team lacked data. It’s that we couldn’t trust what we had.”
If you have spent enough time in finance, you know this story. You also know that data problems rarely appear all at once. They creep in quietly through one manual spreadsheet, one inconsistent KPI, one temporary reconciliation, and then another. Eventually, the business is making million-dollar decisions based on information no one fully trusts.
This is where the 30–60–90-day plan becomes essential. It is the playbook I share with financial leaders who need clarity quickly without interrupting the daily operations of the business.
Days 1–30: Get a Clear, Unvarnished View of Your Data Debt
In the first month, most leaders discover that their biggest problem is not the lack of data. It is the lack of clarity.
I once watched a CIO map out their financial systems on a whiteboard. By the time he finished, it looked like a subway map designed by six different cities. Customer data in one system, GL codes in another, risk models in a third, and regulatory feeds no one could fully explain. The surprising part is that everyone assumed someone else had the full picture.
During these first 30 days, patterns always emerge:
- Finance and Risk calculate core metrics differently.
- Customer data exists in half a dozen variations.
- Manual reconciliations mask larger pipeline issues.
- SOX controls technically exist but do not reflect real processes.
- Legacy systems quietly shape forecasting logic no one remembers building.
This is also the stage where TechWish steps in to illuminate what has been hiding in plain sight. We meet with analysts to understand how financial results are prepared each month. We trace where definitions diverge. We show leaders the places where data is duplicated, contradicted, or completely ungoverned. Executives often see, for the first time, the real map of their ecosystem rather than the one they believed they had.
TechWish succeeds in these engagements because we combine financial domain knowledge with modern data engineering and data governance discipline, something most firms separate.
Days 31–60: Architect What the Future Should Look Like
By the second month, the mood in the executive room usually shifts from frustration to determination.
This is when we get bigger questions from leaders:
- If we rebuilt our financial data ecosystem today, what would it look like?
What are the risks of doing nothing? - What are the lowest effort moves that give us the highest lift?
I worked with a payments company whose CFO described this stage as untangling the knots. They did not overhaul everything because they did not need to. What they needed was a blueprint for where data should live, who should own it, and how it should flow without contradicting itself.
TechWish works closely with leadership during this period to make these decisions real. We help identify which pipelines cause the greatest downstream disruption. We clarify the data domains that require true ownership. We design modern, cloud aligned patterns that support finance, risk, and operations without overwhelming the teams who rely on them.
We helped the leadership team create a simple prioritization matrix, something far more practical than a large transformation program. It highlighted the areas where improving a single pipeline could resolve multiple downstream problems.
With this in place, we narrowed the focus to two pipelines that powered forecasting and regulatory reporting. That limited scope delivered surprising speed. Forecast refresh cycles shrank from four days to a single afternoon.
TechWish succeeds here because we understand where financial processes break and how to rebuild them in a way that aligns business logic with technical design.
With this in place, we narrowed the focus to two pipelines that powered forecasting and regulatory reporting. That limited scope delivered surprising speed. Forecast refresh cycles shrank from four days to a single afternoon.
TechWish succeeds here because we understand where financial processes break and how to rebuild them in a way that aligns business logic with technical design.
Days 61–90: Delivering High-Impact Implementation and Avoiding the Pitfalls That Derail Most Transformations
By month three, the focus shifts to implementation. This is where TechWish turns the architecture into working improvements, and this stage moves quickly because we avoid the mistakes that slow most transformations.
We don’t wait for a “perfect moment” to begin major changes. We define the end-state vision early and implement it in focused increments, ensuring every update supports the bigger picture. And because data problems are never just IT problems, we embed governance, shared definitions, KPIs, and clear ownership directly into the build so the business stays aligned. Before changes go live, we map the downstream impacts so updates in finance don’t create surprises for risk, operations, or compliance.
This is why results show up fast. When you remove a manual step that slows the close or rebuild a forecasting pipeline that drives critical reports, the payoff is immediate. I remember a finance director logging into her new data quality dashboard and seeing issues surface before they hit her reports. She said she trusted the numbers again.
A unified customer and vendor view reduces duplication. Automated checks replace manual reviews. Updated pipelines support predictive forecasting. Playbooks make ownership clear. These improvements begin generating ROI immediately because they eliminate the rework and delays finance teams have learned to accept as normal.
Implemented this way, each improvement reinforces the next. Momentum builds, and leaders finally experience their data as reliable, consistent, and aligned with how the business actually runs.
If Your Forecasts, Reports, or KPIs Do Not Feel Trustworthy, You Are Not Alone
Financial institutions everywhere are dealing with data debt. The difference between those who climb out of their data debt and those who stay stuck is simple. The most successful leaders begin before the moment feels perfect. They do not wait for the ideal budget or the ideal system. They take the first step and build clarity from there.
TechWish has helped finance teams across banks, lenders, payments companies, and investment firms regain trust in their numbers. We have seen firsthand how quickly an organization can move when the right ninety-day plan is in place.
If you are ready to turn your data from a liability into a source of confidence and leverage, we can help you build a roadmap that fits the reality of your business and the pace your teams can support.
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