You have read the articles. You understand that data readiness matters. You have a checklist and a timeline. Now the question: do you handle this yourself or bring in someone from outside?
The honest answer is that it depends on a few specific factors. Not your budget. Not your ambition. Your internal capacity, your timeline, and the complexity of your data landscape.
This is not a pitch for hiring a consultant. Some companies absolutely should do this themselves. Others will waste months trying before realizing they need help. Here is how to tell which camp you are in.
When DIY makes sense
DIY works when three conditions are true simultaneously.
You have the people. Someone on your team has experience with data analysis, system reconciliation, and documentation at a level beyond day-to-day operations. Not just someone who uses the systems, but someone who understands how data flows between them and can identify where things break.
You have the time. Data readiness work competes with everything else your team does. Month-end close, board reporting, FP&A, annual audit. If your controller is at 100% capacity already, adding “reconcile three years of historical data” to their plate means something else stops getting done.
Your data landscape is simple. One ERP, one CRM, straightforward revenue model, fewer than three acquisitions to integrate. If your systems are contained and your business model is not complex, the reconciliation and documentation work is manageable for an internal team.
If all three are true, do it yourself. The process is not mysterious. It is methodical work that follows a clear sequence. Read the checklist, run the 48-Hour Test, fix what fails, document everything.
When DIY does not work
DIY breaks down when any of these are true.
The team does not know what good looks like. This is the most common problem. Your controller has never been through a sell-side diligence process. They do not know what questions the buyer’s team will ask, what format they expect the answers in, or what level of documentation passes muster. They are preparing for a test they have never taken.
There is no capacity. The people who understand the data are also the people running the finance function, managing IT, or leading operations. They cannot stop doing their jobs to spend three months on readiness work. And they should not. The business needs to perform during the exit process, not decline because the team was distracted by a data project.
The data landscape is complex. Multiple ERP systems from acquisitions. A system migration in the last two years that broke data continuity. Revenue recognized differently across business units. Customer data spread across five platforms with no master data management. These situations require experience that most internal teams have not built.
The timeline is short. Six months or less. Not enough runway for a team learning as they go. An experienced team can assess and prioritize in two weeks because they have seen the patterns before. An internal team doing it for the first time takes six to eight weeks to reach the same point.
What DIY looks like in practice
Here is what an internal data readiness project typically involves.
Weeks 1 through 4. Assessment. Map your systems, run the 48-Hour Test, identify gaps. This is the part most teams handle well because it is analytical work with clear outputs.
Weeks 5 through 12. Remediation. Fix reconciliation issues, document definitions, cross-train team members, build adjustment documentation. This is where internal projects stall. The work is tedious, it competes with daily operations, and there is no external accountability to keep the timeline.
Weeks 13 through 16. Testing and documentation. Run a mock diligence, update documentation, prepare the team. This is where teams discover that their fixes from phase two do not quite work under pressure.
The total is roughly 16 weeks, or four months. In practice, most internal projects take six to eight months because of competing priorities, scope creep, and the learning curve.
The risk is not that the team cannot do the work. It is that the work takes longer than planned and the exit timeline does not move.
What outside help actually does
An experienced data readiness team does three things you cannot easily replicate internally.
Pattern recognition. They have seen dozens of mid-market data environments. They know which issues show up in QoE reports, which ones cause diligence delays, and which ones do not matter. This saves weeks of figuring out what to prioritize.
Dedicated capacity. They work on your data readiness full time. They do not stop to close the books or run the monthly board deck. The work gets done on schedule because it is their only job.
Diligence perspective. They know what buyers and QoE teams look for because they have been on that side of the table. They prepare your data and documentation to match what will be asked for, not what your internal team thinks might be asked for.
What they do not do (or should not do) is build a data warehouse, implement a new BI tool, or run a technology transformation. If someone is pitching you a six-month technology project as “diligence readiness,” they are solving a different problem.
The cost comparison
Direct costs are the easy part of this comparison. The harder part is opportunity cost.
DIY direct cost: Internal team time. For a typical mid-market company, estimate 200 to 400 hours of team time over four to six months. At a fully loaded cost of $75 to $100 per hour for the finance and data staff involved, that is $15K to $40K in direct labor cost.
Outside help direct cost: $50K to $150K for a focused engagement, depending on complexity and timeline. Higher for complex multi-entity, multi-system environments. Lower for straightforward single-entity companies.
DIY opportunity cost: This is where the real calculation happens. What is your controller not doing while they reconcile three years of historical revenue data? What is your head of operations not doing while they document KPI definitions? If the data readiness project distracts your team from running the business, and the business underperforms during the exit process, the cost is measured in multiple compression, not consulting fees.
Timeline risk cost: If DIY takes eight months instead of four, and your exit window was twelve months, you just lost a third of your preparation time. If the market window shifts during that delay, the cost is impossible to calculate but very real.
Decision framework: 5 questions to ask yourself
Answer these honestly.
1. Has anyone on your team been through a sell-side diligence process before?
If yes, they know what is coming and can prepare the team. If no, there is a significant learning curve that compresses your effective preparation time.
2. Can you dedicate someone at least half-time to this project for four months?
Half time means 20 hours per week, not “when they have a spare moment.” If the answer is no, the project will take twice as long as planned.
3. How many systems hold financial or customer data?
One to three systems: manageable internally. Four to seven systems: challenging internally. Eight or more: you almost certainly need help.
4. Have you made acquisitions that have not been fully integrated?
Each unintegrated acquisition multiplies the complexity. If you have two or more acquisitions with separate systems and data, the reconciliation work is substantial.
5. How much time do you have before the process starts?
Twelve months or more: plenty of time for DIY. Six to twelve months: tight for DIY, comfortable for outside help. Less than six: you need experienced people who can move fast.
If you answered “no” to questions 1 and 2, or if your systems count is high and your timeline is short, outside help will pay for itself in time saved and risk reduced.
What to look for if you do hire help
Not all data consultants are the same. Here is what matters for diligence readiness specifically.
Deal experience. Have they supported companies through actual diligence processes? Academic data management knowledge is not the same as knowing what a QoE team will test.
Mid-market focus. Enterprise consultants bring enterprise methodologies. They will propose a six-month data governance program when you need a two-month reconciliation sprint. Find someone who works at your scale.
Practitioner background. The person doing the work should have built and fixed data environments, not just assessed them. You want someone who has been the controller trying to close the books with broken data. Not someone who has only audited them.
Fixed scope and timeline. Be wary of open-ended engagements. Data readiness is a bounded problem. A good partner can scope the work, quote a fixed price, and commit to a delivery date.
Handoff plan. The goal is to leave your team capable of maintaining what was built. If the consultant’s work only works while they are in the room, you have not solved the problem. You have rented a solution.
The bottom line
DIY works if you have the people, the time, and a simple data environment. It does not work if any of those are missing, and it definitely does not work if your timeline is compressed.
Outside help works when you need speed, experience, and dedicated capacity. It does not work if you hire someone who treats this as a technology project instead of a data and process project.
Either way, the work needs to happen. The cost of not preparing is measured in multiple compression, extended timelines, and management distraction. For the math on what that actually costs, see How Data Problems Cost One Company Half a Turn.
Start with The Complete Data Diligence Guide to understand what buyers test. Use the timeline to scope your preparation path.
For a weekly brief on data readiness for PE-backed companies, subscribe to Inside the Data Room.