Does My Company Need Data Preboarding?

Does my company need data preboarding?

We often say that every company that trades data in files with data partners already does data preboarding, somehow. That’s not marketing spin, it’s absolutely true. If you receive data you must have some process for storing it, identifying it, checking if it is good, and moving it to downstream consumers.

When you ask yourself, do I need a data preboarding solution, you’re really asking if you need to invest in upgrading your flat-file infrastructure to a next-level capability. “Professionalizing” it, so to say, with a solution like CsvPath Framework and FlightPath Server.

That’s a much more interesting question!

There are two parts to the answer. There is an ROI consideration. And equally, a tool-for-the-job consideration. Can preboarding deliver found money? Is preboarding the best way to find money?

First, the Economic View

At a super high level ask yourself:

• Are my costs are unacceptable?

• Is my revenue not as strong as required?

The costs-too-high problem is about the same as the revenue-is-insufficent problem. The difference being that if costs are too high the improvement from better preboarding is captured by a RIF, i.e. shrinking the team, or intentionally outgrowing them over time. On the other side, if revenue is insufficient, the human resources spend stays the same and the productivity improvement goes into expanding the business.

Here is a table showing the basic problems as they might be seen in a representative B2B tech-enabled services business. Say, an invoice processing or billing company. Inefficient preboarding primarily hits three groups in five very generalized ways.

If data preboarding is the right lever, how does it unlock each of these problems?

Unblocking revenue

Data preboarding unlocks new revenue when BizOps is the main bottleneck. If your data operations can’t handle a marginal increase in data throughput, and the business has contracts unfilled, automation that increases BizOps capacity effectively delivers the average revenue per employee for every additional FTE’s worth of automation. When the whole company is blocked, every person’s-worth of unblocking is a person’s-worth of additional revenue.

Data preboarding is first and foremost about the automation of manual effort, raising throughput and reducing risk. Throughput is increased by:

• Creating business rules that take the place of manual validation and upgrading

• Minimizing data handling (copying files, renaming them, etc.)

• Minimizing rework; less manual effort, fewer mistakes, less rework

BizOps and DataOps teams spend a surprising amount of time checking files, moving them, checking headers, fixing date formats, and the like. Low value but necessary stuff. CsvPath Framework makes it much easier to have an automated file handling workflow with most human judgement minimized. Used successfully, it is a force-multiplier that can increase throughput dramatically.

A rule as simple as:

$[*][ date(#ordered_on, “%Y-%M-%D”) header_name(#1, “ID”) ]

can eliminate a visual check on the format of order dates and the presence of an “ID” header in the correct place. The work doesn’t happen twice as fast. It doesn’t happen 10x faster. From the perspective of units-handled-per-person, it is infinitely faster. Most improvements based on validation and upgrading rules aren’t that dramatic, but the simple example makes the point that the potential is huge.

Revenue delayed

It would seem that adding business rules would lengthen the time needed to onboard a new customer. Creating rules requires interviewing SMEs, coding, and testing. However, there’s more to it. A preboarding framework should be able to stamp out a new data partner project, allow reuse of rules from other data partnerships, minimize the scripting involved in moving data around, and minimize process creation and learning time.

CsvPath Framework delivers by:

• Minimizing project setup time down to a few minutes, requiring no code or configuration-as-code

• Allowing validation and upgrading to be composed from small, easy to read, easily tested rules shared across projects

• Applying a consistent pattern to all data partnerships with significantly less drift and fewer exceptions that slow down rollout and maintenance

Opportunity cost

Data engineering and BizOps teams commonly lose 50% or more of their time to manual data management and fire-fighting. Fire-fighting is particularly pernicious because it is unpredictable and often all-consuming. That amount of time lost could be better spent on work supporting revenue generation, efforts to increase pricing-power, and/or product-line extension.

As well as the benefits already noted, CsvPath Framework tackles the causes of lost data engineering and SME time by:

• Making data immutable, runs idempotent, and processing assets versioned

• Providing a clear identity for data, as well as data and configuration files, that is traceable at every processing step

• Integrating most market-leading monitoring, alerting, and lineage platforms using the OpenTelemetry and OpenLineage standards

• Providing easy process rewind and replay options

• Making validation and upgrading rules easy to understand and test

Immutability, the ability to repeat processing with exactly the same outcome, based on immutable data sets that never change, makes finding and fixing errors far faster. When a step can be repeated using pipeline assets that are versioned and data that is unchanged, complexity is removed, clarity is increased, and the risk of experiments and tests drops.

These capabilities simplify and destress error recognition and replay. By shifting remediation left preboarding shrinks the blast radius, thereby lowering the cost of understanding what happened, fixing it, and restarting data processing. Fixing data at the point of error is significantly cheaper and faster.

Revenue risk

Data operations puts recognized revenue and captured margin at risk. There are a number of ways poor quality operations can hurt:

• Downstream SLAs may be broken and penalties or service recovery expenses incurred

• Partners or customers may require rework cascading beyond directly affected data

• Over time trust may erode and costs increase to the point that partnerships end and there are development costs of finding and onboarding new partners

• Lower customer trust in the data provided may cause churn

• Sales may become harder due to the loss of references, poor reputation, and demotivation of the sales team or channel partners

Hit to pricing power

Products that are founded on data obviously must work and when they don’t new product development and/or product extension takes the hit. Virtually all industries and categories are competitive. Products that don’t improve don’t grow and companies that can’t field new products are eventually passed by. It goes without saying that some of the opportunity cost of gaps in data preboarding will be paid by product development friction. Some of this cost can paid by product development crowding out internal systems work. However, gaps in internal work also constrain the business with their own friction.

For example, say a data operation is running at 50% efficiency. The higher effort crowds out data analytics work for sales team decision making. The lack of correct and inciteful analytics causes malinvestment in Sales, resulting in less revenue. The product side of the house looks at the revenue volume and sources and compounds the problem with their own malinvestment. Their investment in new product runs at full speed, to the detriment of the sales analytics, but results in fewer sales at lower prices, and therefore less revenue.

It will always be difficult to pin down linkages like these. However, it is easy to make the case that in a company that is data engineering and data SME capacity constrained agility is reduced. With fewer experiments and other feedback loops, information that could have better informed product choices is not available. The revenue impact may not be easily assessed, but the agility drag is easy to see and the revenue impact can be assumed from that.

The Right Tool For the Job?

All the economic reasons for data preboarding would be for nothing if data preboarding is not the right tool for the job. Happily we know that it is, because essentially all file-feed data partnerships have a preboarding component already and inherently. Everything written above assumes not that preboarding is absent, but that it is insufficient. So that’s not the question we want to look at.

The better questions are is better data preboarding a good investment for your company? And is CsvPath the right tool for the job? At first you might say, obviously yes, data preboarding is a good investment, for the reasons above. But it’s not that simple. In some cases, spending effort on next-level data preboarding is less valuable than other investment might be.

Let’s stipulate that FlightPath, built on top of CsvPath Framework, is, at least today, the only enterprise grade data preboarding solution that isn’t deeply embedded in a larger enterprise software package, such as an ERP system. That won’t be true forever, but let’s go with it. FlightPath is an open source product. Nevertheless, implementing any systems change is an investment. The question isn’t how much FlightPath costs. The question is, does it fit the use case.

CsvPath Framework targets use cases that match:

• Regular file-feed deliveries

• Delimited data

• Multiple data partners

• Consistent and automation-friendly data business rules

• Files in the megabyte to low gigabytes range

• Cost of errors is high

If multiple of these bullets is true, CsvPath Framework and its frontend FlightPath products are worth evaluating. Let’s spin through them briefly.

Regular file-feed deliveries

Investment in data preboarding is likely to be efficient if file deliveries are regular and the data is consistent. By regular we’re talking about daily, weekly, or monthly. Periodic deliveries that are not on a set schedule may also work. The reasoning for this is simple: if you’re taking data deliveries frequently there are likely to be more gains from automation.

Delimited data

CsvPath Framework support CSV, JSONL, and Excel. In the future it is likely that other document-form data will be supported, in particular XML and/or JSON, and/or EDI. But that is not the near-term. (Check back in mid-2026; things may have changed.) The overall preboarding workflow can work with any type of file, but the validation and upgrading at the core of CsvPath Framework only operates on tabular, delimited data. Luckily for the Framework, the universe of delimited, tabular data partner data exchanges is very wide.

Multiple data partners

The benefits of consistency, rapid partner onboarding, and business rules-driven efficiencies are greater and greater the more data partners your company has. CsvPath Framework works great with one data partner, ten partners, daily or weekly, or anything similar to that. However, it was created to handle far larger situations. Applying CsvPath Framework to hundreds or thousands of data partners results in efficiencies that are massively greater than the efficiency gains possible from one or two partners. The Framework scales horizontally and keeps its work simple and consistent, so the number of partnerships is not gated by the tool.

Automation-friendly business rules

There are obvious ways rules can be automation-friendly:

• Consistency

• No rocket science

• A reasonable number

If rules are not expected to be applied consistently, it is hard to see how automation can help. Most data partnerships have expectations that are clear and don’t change much. But if that is not the case, human judgement will continue to be needed.

If rules are consistent but they are so complex reducing them to automation could be impractical. In practice this is unlikely. What is more likely is that any complex rules simply take longer to nail down and automate. However, the harder it is to automate a rule the more likely it is that the rule nets you a high return from automating it. That’s because complex rules require expert humans that are expensive, slow, and error prone.

Lastly, it is much better to have a good number of rules, but not an overwhelming number of them. A “bunch” of rules is good for gains in efficency. Hundreds of rules slows development, testing, and operational performance. CsvPath Framework has features and use patterns specifically geared towards making very large sets of rules more practical. But it isn’t actually magical. At some point you end up in an impractical place. That said, if you have trouble using CsvPath Framework to automate a huge rules base, you’re undoubtedly having similar problems with your manual or bespoke preboarding.

Reasonable file sizes

CsvPath Framework performs similarly to how any Python programming would perform. It was built to handle very small files up to multi-gigabyte files. Beyond 10 gigabytes you’re getting into significantly long run times. Handling 10-50 gigabyte files is doable. But at some point there aren’t enough hours in the day and any replays only add to the burden. Realistically the vast majority of data partnerships generate files that are a gigabyte or less. Chopping files into smaller files before processing, moving data and compute into more independent, colocated, on-demand environments, and other standard strategies can also help. And of course the biggest consideration with large files is the number and complexity of business rules. Just as with SQL or any other validation and upgrading tool, the more complex the statements the longer the run time.

High cost of errors

Fairly obviously, if errors are free, we can afford many more of them. In that case, automation would be less valuable. In the real world, errors are typically expensive. In fact, errors that are closing in on free generally aren’t really treated as errors. The higher the error price and the more errors you have or could have, the more important it is to use a robust data preboarding tool like CsvPath Framework.

Does My Company Need Data Preboarding?

Here we are. We’ve answered two questions:

• Is there meaningful ROI from investment in data preboarding?

• Is data preboarding, particularly CsvPath Framework, the right tool for the job?

In both cases the answer is yes for a wide range of companies and use cases. The value of reducing friction is high and the fit good for many types of high frequency, multi-partner, complex rules situations. These aren’t surprising answers, but I hope we’ve been specific enough that you can clearly recognize if your case is right for the investment, or if you should invest elsewhere.

If your company is one of the multitude that could get substantial ROI from better preboarding, I recommend you evaluate and run a small trial project using CsvPath Framework and FlightPath Data to see if it’s the right thing for you. You may decide to use a more modern approach with those tools going forward. If you want a hand specing out an evaluation, by all means, get in touch. We’re there for you.