Ready Signal was created from a simple idea - we wanted to make the lives of Data Scientists and Business Intelligence professionals easier. For any business, it is imperative to understand external factors that are impacting your bottom line. For BI Professionals and Data Scientists, not only is it important; it is also A LOT of effort to identify the right external factors for your business, then access, clean, and normalize the data for use within your organization. Our data scientists were all too familiar with how much work this was and wanted to create a better solution.

Ready Signal was created by data scientists, for data scientists to solve the problems we ran into every day.

Because Context Matters: The Importance of External Data

As with anything in life, context matters. Every business has a wealth of internal data on their customers, promotions, supply chain, etc. But there are also a plethora of external factors surrounding your business that have a very real effect on outcomes. If you don’t know how your business is responding to the external factors surrounding it, you aren’t looking at the full picture and are at risk of making uninformed business decisions. Whether it is economic factors like unemployment rate or median household income, or higher/lower than average weather patterns, it is imperative to know what these factors are and understand how your business reacts to them. This will inform your sales forecasts, evaluate your marketing efforts in context of surrounding forces, and identify possible leading indicators of business shifts that you can proactively plan for.

The Problem with External Data

Although these external data are readily available, often as open-source data, the downside is that they are time consuming and cumbersome to work with. Many sources, like the Federal Reserve of Economic Data, NOAA, and US Census, contain valuable information, but none are in a standardized or normalized format to work with at scale. For example, to cover localized temperature and precipitation for the entire US requires accessing data from nearly 1000 different individual weather stations! Oh, and you must translate the latitude and longitude of each station to a zip code to join it to your internal business data. This is all fine and good if you just need to target a couple key zip codes for your business, but if you must do this at scale, it’s a data science nightmare.

It is often the case that simply due to the heavy lift that is required to do this right, external factors can be ignored. A need to meet deadlines or not having the resources and time to track relevant external factors down and test them leaves many businesses with an incomplete picture of the landscape they operate in.

Automating Data Aggregation and Normalizing Data

One of the first core tenants of Ready Signal was to tackle that specific problem head on - to remove the need for a data scientist to do all the heavy lifting of aggregating and normalizing these data across multiple different sources and save time.

According to the 2021 State of Data Science Report, Data Scientists spend nearly 40% of their time collecting, cleaning, and organizing data. And from my experience, the majority of that is focused on the wealth of internal information that Data Scientists have at their fingertips within the organization. That number continues to creep up when you layer in the need to look for additional data sources outside your organization.

Our data scientists got sick of re-inventing the wheel for each of their projects and started to put a process in place to automate this task and make their lives easier. After seeing significant time savings for internal data science work, we wanted to share this with others, and so, Ready Signal became an automated feature engineering platform for everyone to enjoy.

Which External Factors Matter to Me?

The other challenge to working with these data is simply trying to understand which factors are most important to your specific business. Some things are obvious. If you are selling umbrellas, it is not too hard to figure out that precipitation would be a key factor to monitor for your business. But there are plenty of scenarios where it isn’t as clear cut, or there are underlying factors with a relationship to your business that you never considered.

There is plenty of data available to you to figure this out. And frankly, the world doesn’t need more data, but what we do need is some help in figuring out which data is most important to each of our specific business situations. The next logical step for us was to help our customers answer those questions.

We created a layer of intelligence within the platform called Auto Discovery. This feature allows our customers to upload their target variable (whatever they are looking for external relationships to) and the platform will programmatically test that against all the external features we have available. Upon identifying those relationships, Auto Discovery packages them up and makes them ready to integrate directly into whatever data science platform our customers are using. We also automatically test lags in the data to help our customers identify possible leading indicators for their business.

We have seen some interesting things come out of this process, and our customers have been able to uncover external factors that they either had not considered, or weren’t aware existed, that have been statistically significant predictors in their sales forecasts. This saves time and provides valuable insight that otherwise would have been ignored.

The result

On a very tactical level, our customers have saved countless hours of time in their Data Science efforts.

From a strategic perspective, our customers have gained valuable insight to answer questions like:

• Is my business facing a headwind or benefitting from a tailwind?
• What is the true impact of my marketing efforts in the context of market factors?
• What am I observing in the market today that I expect to impact my bottom-line weeks or months down the road?
• What market areas should my business expand into next?

Working with our customers, helping them identify external factors specific to their business and integrating those factors into their Data Science models, we have seen up to 20% improvement in data science model performance. That is a big deal when it comes to understanding your bottom line.

This is why we created Ready Signal: to save time, effort, and provide insight and context of external factors impacting your business

To learn how Ready Signal can help your business, contact us today and receive a two week free trial.