How about saving up to 40% of your time in the data preparation & insights stages of your business AI?

The main challenge for any organisation that wants to start working with ML & AI, is to understand where to start. Maybe you already have a team of data scientists to build models or you are investing on this journey; but most of their time is & will be spent on the data preparation stages – wouldn’t it be great to fast track them direct to model building? In any data science team, it is the model building and model deployment that is considered the value-adding part. Preparing for this is a key necessity; but it’s also a very time- and resource consuming activity.

Share article:

Within any predictive modelling activity, whether that is using machine learning, AI, or old-fashioned predictive decisioning models, the major time-consuming challenge is to first identify which data is important and insightful. But this is not the only challenge; the data must have a meaning and be in a format that can be used – the old adage “a model is only as good as the data that is behind it”. 

 

However normally for a data scientist, this is not the easiest process; they need to first locate the data to analysis on, then they need to join the dataset, structure it, and finally format it ready for selection. Once these steps are in place, they can then start the process of analysing it – what are the correlations, what is predictive or not, do I have enough records to work with, is the data sample robust enough, do I have enough data points to get insights on, what do I miss. All these aspects take time away from what they really want to do, and that is work on the building a predictive model!

 

Finding the data that matters is crucial

The secret to usable ML is to get yourself ready & your data structured for ML. This is incredibly important step; recently we helped a client by providing 3rd party data for their new optimised segmentation. Before we were engaged in their project, they had manually tested up to 600 variables to discover which variables were the most predictive. Their own estimation is that from starting their project to the stage where they knew what data to build their new segmentation model on, was about a year. Now, some of this time goes with maturing the organisation, coupled with an internal adaption to a data-driven way of doing things, all of which always take time. But a substantial part of this period was spent testing data, structuring data, and trying to figure out how to build their new model. A structured approach to ML can shortcut this part of the process; by combining  a data formatting tool with inbuilt ML capabilities, you are able to quickly test variables using different ML models, enabling you to get an overview of each variable’s performance & significance (and the optimal ML model protocol to build your model with); all in a short period of time.

 

We have built an ML-driven engine to match, blend and rank data

The Geomatic data blending engine does just that; it is a tool that via complex matching algorithms, and data formatting techniques combining correlation & principle components protocols, matches and blends your internal customer data to our 3rd party data components in a controlled environment; that via machine learning algorithms helps to identify the predict nature and so auto-selecting the relevant data for further investigation & modelling within your internal data modelling services.  

 

The objective of the blending engine is to take the best of both worlds (internal and 3rd party data) by crunching data variables and ranking each variable in “predictiveness”. The business outcome can be viewed as a “change agent” to improve existing ways of working in applied model building functions that supports a wide variety of aspects from time to market, model preparation enhancement process, sensitivity analysis on “variables” predictiveness, fine tuning models, creating models on the ‘fly’ etc.

 

All in all, you can save up to 40% of your time in the data preparation & insights stages!

 

Move forward with the best performing data and models

Regardless of the size of your company or the maturity of your data and analytical usage our data blending tool will help you spend less time and fewer resources getting from the preparation stage to the model building stage. We have built it in as the heart of our Data Analytics Platform where our clients can also tap into our different modules – the Nordic data lake, get access to our model-building engine Predict and make use of our expert consulting services.

 

If you are curious to know more, click here or reach out to me for a chat

Do you want to know more?

Sign up below or get in touch with me directly to learn more

Ross Whalley portrait

Ross Whalley

CSO & Partner

+45 2018 8378

Linkedin