Digital transformation is often described in terms of software: a new ERP, a new sales force app, a new dashboard. Yet projects most often struggle for less glamorous reasons: unreliable data, unclear ownership and processes nobody has written down.
Fix the data before the tool
New systems display whatever data they are given. If product codes, outlet lists and distributor records are inconsistent, a dashboard will make the inconsistency more visible, not fix it. Start with master data: one product list, one outlet identifier, one definition for each metric.
Map the process as it really works
Write down how an order, a claim or a report currently moves from one person to the next. Include the informal steps, such as the spreadsheet someone emails every Friday. Removing unnecessary steps first often delivers more benefit than automating them.
Assign owners
Every data set and report needs a named owner who is responsible for its accuracy and who can answer questions about it. Without ownership, errors are noticed but not corrected.
Start small and prove value
Pick one report or process that causes visible pain, improve it end to end, and measure the effect in time saved or errors avoided. A small success builds trust and gives the team a model to repeat.
Plan for people
- Involve the people who will use the system in its design.
- Train on real tasks, not generic demos.
- Keep a clear channel for feedback and fixes in the first weeks.
Measure what matters
Define success before launch: fewer manual hours, fewer data corrections, faster month-end close, or quicker decisions. Report the same measures after launch so the value is visible.
Five questions for checking data quality
Data quality can sound abstract until it is broken into questions that a team can test directly.
| Dimension | Question to ask | Example of a problem |
|---|---|---|
| Completeness | Is anything missing? | Two distributors have not submitted this month's file |
| Accuracy | Does it match reality? | Reported sales differ from invoices |
| Consistency | Does it match across systems? | A product has different codes in the ERP and the sales app |
| Timeliness | Does it arrive when needed? | Weekly data arrives after the review meeting |
| Uniqueness | Is anything counted twice? | The same shop appears under three spellings |
Score each of your main data sets on these five points, even roughly. The result shows where to begin, and it gives you a baseline that you can use to demonstrate improvement.
A first master-data cleanup
- Choose one list. Outlets or products are good starting points, since most reports depend on them.
- Export from every source and combine them into one sheet.
- Find duplicates and gaps, using matching on names, addresses and codes, followed by a manual review.
- Agree on a single identifier and a mapping from old codes to new ones.
- Set a rule for new records: who can create one, and which fields are required. Without this, the list will drift back to its previous state.
Questions to ask before buying software
- What specific problem will this solve, and how will we measure it?
- Which of our processes will the system require us to change, and are we willing to change them?
- Who will own the system and its data after the vendor's implementation team has left?
- Can distributors and field teams realistically use it, given their devices, connectivity and training?
- How will data be moved in, and can we export it if we later change vendors?
- What is the total cost over several years, including licences, support and internal time?
Why projects stall
Some patterns recur across transformation efforts. Scope grows until nothing can be delivered in a reasonable time. A project runs as an IT initiative with little involvement from the business. Old spreadsheets continue alongside the new system, so there are two versions of the truth. Sometimes the launch itself is treated as the finish line, and no one is left responsible for adoption. Each of these can be anticipated and raised early.
A practical first 90 days
- Days 1 to 30: understand. Map the priority process, list the main reports, and score their data quality.
- Days 31 to 60: fix. Clean one master list, assign owners, and agree on definitions for your core metrics.
- Days 61 to 90: prove. Improve one painful report from end to end, measure the time saved, and share the result with leadership.
By the end of this period, you will have cleaner data and a real example of value. You will also have a far better understanding of what you actually need from any larger system, which puts you in a stronger position to choose one.
Small improvements count. Structured templates, such as the Business Report Generator, make recurring reports consistent while larger changes are planned.
This article is general information, not professional advice. See the disclaimer.