The bridge between big whales and the danger zone.

Imagine that your cloud-based app is a runaway success. Word has gotten around and the orders are coming in. You finally land that great whale of a corporate customer that you’ve dreamed about. You’ve signed the contract and now everyone's all LFG!

Your next step? To integrate your customer’s data into your own system.

But wait: there’s a LOT of data. And it’s not at all clean.

In fact, it’s inexpressibly filthy and verminous.

This scenario is very real. It's a critical period in the life of many software as a service (SaaS) providers. We call this period the “danger zone." (cue the infamous Top Gun intro scene, but also not really)

The danger zone is that crucial moment in every business deal that follows the handshake and the signing of documents. It is that time when the executives leave the room and the data managers are left to make the exchange happen. It’s when the rubber meets the road.

The bad news is, time flies

You may have the greatest app to hit the market, but if you can’t get your customer data loaded into it quickly, that won’t make much of a difference. When milestones are missed, deals are broken and business is lost.

From the moment you sign a contract with a new customer you want to prove to them that they made the right choice when they chose to work with you. You want to get them up and running fast and without hiccups so that they can see how beneficial your solution is, how much time and money you will save them. And that means integrating your customer’s data.

The truth is that there is no universal standard for how data is organized. Most companies build their databases using generally-agreed-upon best practices, but without the foresight to think they will need to share that data en masse with another organization. Typically, each data store is its own entity, abiding by its own rules. When it comes time to put this data into a report or use it in another system, they export it. Usually these exports are small subsets of the database that are intended to be used to analyze a specific time frame or a narrow context, say, a sales report or a quarterly market projection. They are typically downloaded as comma separated values (.csv) files, shared, and then manually imported, requiring that each row and column be checked for erroneous and missing data.

The good news is, you're the pilot

In the SaaS onboarding process, businesses are generally sharing much more than a quarterly sales report. It can amount to thousands and thousands of rows of data. Cleaning this data so that it will integrate properly with another system is a process that can—and often does—take weeks or months to accomplish. And all along, your big whale of a customer is just waiting . . . and  waiting.

Flatfile was built to solve this very problem.

Our founders were tasked with importing a lot of data, and they knew that their frustrations were shared by businesses everywhere. So, they created a universal data exchange platform that works for any business.

The simplicity of Flatfile’s design means that it solves the data exchange conundrum for businesses large and small, for web applications, Internet of Things (IoT) manufacturers, mobile apps, and enterprise management systems. It does its job quickly and efficiently, and it even gets better at it every time it is used.

If you are in the process of landing new business, getting your data to its new home quickly and securely simply is a must to ensure your next great whale won't get away.

Remember boys, no points for second place.

-Goose aka Ashley Mulligan, sometimes engineer and sometimes marketer at Flatfile