Beta launch + 1 year anniversary

Announcement: reconfigured turns one year old and enters closed beta phase.

I am so very proud to let y’all know that reconfigured is now in closed beta, on the one year anniversary of our existence. 🙌

It has been truly an experience of a lifetime, and I cannot be anything but grateful to my team, our customers, our investors, and every single person that I’ve talked to over the past year.

A special honorary mention to our families that support us and pick us up when we are feeling low. :-)

TLDR:

• reconfigured enables analysts and data engineers to clean, combine and transform data in your own database or warehouse.
• we have built a graphical interface model any raw data visually, and then run the code in your own infrastructure.

The Problem

In my experience, data projects almost always stumble on the spectrum of converting business context to specification in a timely manner.

Trust in data is low. Source data changes, business requirements evolve, and data breaks down upon the smallest of additions. Not to mention when the original author leaves the company and no documentation can be found.

Yet business always have a need to make data-driven decisions, to utilize their hard-collected data.

Stakeholders don’t always fully grasp the complexity that goes into delivering seemingly simple reports.

For example:

1. profit loss calculations
2. cohort based close rate calculations
3. unifying revenue streams
4. building a unified customer profile
5. Combining event data into meaningful activities

These are all seemingly easy to understand concepts, yet as soon as you dig in to the details it gets complicated.

Data professionals are left to make these happen on extremely tight schedules.

That’s what we are here to solve: help your company grow by making sense of your data.

Reconfigured has allowed us to organize our data in a way that helps us to reveal hidden behaviours from our day to day operations in almost real-time. So far we’ve been able to uncover many operational inefficiencies in our business and address those right away.

Erno Berger, Chief Product Officer, Yeply

Sensibly Customizable

Let’s face it - every company is unique and will always have some form of tailored needs when it comes to data.

That’s why we employ data professionals to craft complex reports, right?

The problem with most solutions is two-fold. Either they are:

• Rigidly templatized, meaning they only work with a basic use case, OR
• Are a blank canvas, fully customizable leaving you without any structure.

Templates get you there 80% of the way, but leave you frustrated as soon as a corner case shows up.

Blank-canvas products allow you to build anything you want visually, but force you to still do the hard data modeling work you would have to do anyway if you had written the code yourself.

reconfigured takes a hybrid approach.

Our data modeling engine is built to withstand any data transformation use case.

You design the what, we handle the how.

Need to combine multiple data sources, join data across 5 tables? We’ll figure out the optimal join path.

Need to apply transformations in a unified way across columns? We got you.

Find yourself re-writing models every time you need to add anything?  No more.

Our engine recalculates the necessary components and makes sure editing models is just a few clicks away.

In other words, the product imposes a set of guard rails for your work. You figure out how the end result needs to look like semantically speaking, and we do everything from staging to attribute resolution.

And if you really need to, you can always “pop the hood” and customize the model using our home-grown expression language.

reconfigured['s entity resolution engine] allows me to build and upgrade data models without having to spend +3 hours on investigating dependencies and relationships each time when working with new schemas. Not only that, it is able to figure out how to join data from any number of source tables.

Shivandra Bidua, Camunda

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Closed Beta - what you get

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We have been blessed to work with customers for the past year. We knew we had two engineering problems:

• A flexible yet performant compiler that can generate complex SQL code to match specification
• An intuitive UI that is easy to use and understand, yet powerful enough to support custom use cases.

The product currently fully supports:

• Integrating Core Business Entities from multiple sources. For example aligning customer data between your production system, Analytics and CRM.
• Attribute Resolution, both pre-made logic and custom.
• In case there are multiple potential sources for single attribute, we can use different kind of resolution strategies such as priority based on source
• In case you need to write logic yourself, you can use any SQL logic or existing dbt-macros
• Expression-based Attribute builder.
• When you need to combine multiple data points from the sources and/or add custom or conditional logic we have expression builder for that. If you know how write simple SQL functions or use Excel formulas you’ll find it familiar.
• Automatic JOIN resolver.
• You configure allowed joins between individual tables once, and after that you can use any attribute from any source anywhere, and the joins are automatically generated for you.
• Layered data architecture: Entities form the semantic, subject oriented data warehouse layer and on top of that you can build the Business / Datamart layer with our presentations feature.

“The transparency and flexibility of the tool and the team sets reconfigured apart from other products. I can see – and understand – what's happening under the hood through the compiled code and even go beyond predetermined functionality by configuring their engine, chaining functions, and adding custom macros.”

Tom Hämäläinen, Phaver

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What’s next?

We are now fully focused on finding and helping new customers in their data journey. The more real-life use cases we can throw at the product, the faster it becomes better. :-)

In addition to putting the engine to use, we are focusing our development resources on three main areas:

• Activities: Working and building with event data
• Optimization: Making the models performant to save you money
• Collaboration: Reducing the time from dialogue to delivering data.

Activities in particular has been a recurring topic in our discussions. Working with event data is already hard to begin with, but building meaningful activities (such as user became active, or worse - inactive) has been a continuous pain point both for small and larger data teams.

Now that we have done the groundwork with our entity and attribute resolver, we can focus our efforts on cracking the next piece of the puzzle.

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Interested? Get involved.

We are actively looking for new users, use cases, new ideas. If you are in the process of starting your data warehouse journey, or already have one in place - get in touch.

We are more than happy to help and if it makes sense for you, get you started with the reconfigured product.

Few ways to participate:

• Already have dbt in use? Join our closed beta group and take the product for a spin.
• Already have a warehouse, and things getting messy? Hit me up, we love working with existing setups.
• Using your BI tool to do heavy-lifting data transformation work, AND things are getting tedious? Let's chat, we'll help you get started.

Email us, message us on LI, or drop your email on the website.

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The Origin Story

It’s always nice to reminisce on where this all started.

I am originally a marketer (guess I am one again nowadays) who stumbled his way into operations, CRM, and eventually into data engineering.

My dream was to do fun, creative marketing, but I was always limited by the data I had at my fingertips. So instead of marketing, I spent most of my career sanitary engineering systems and their data flows.

When I worked at Paddle my last project was to setup a version of the modern data stack for the commercial side, to build a growth engine that unified sales, marketing, customer and part of product data.

To my demise, I had fantastic tools for every part of the data journey - except the model itself. dbt was supposed to be my silver bullet, but turned out I had to learn SQL and data engineering myself.

Here’s where my cofounder Niko comes in - he and I are best friends for 20 years already, and he is like a walking wikipedia when it comes to data topics. I kept asking him dumb questions on how this and that works, and eventually, we came to the conclusion that there must be a better way to enable people like myself to harness the power of the data warehouse.

After all, I was a growth engineer that needed to deliver outcomes, not excuses.

And hence, reconfigured was born - out of pure frustration of trying to bend data to pressing business needs.

Thank you for all the support. Excited to see how this evolves next.

Much love to all y’all out there. ❤️

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Richard, Niko & the entire reconfigured team.

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