Please see below for my questions and a background…
Has anyone found a way to streamline/automate the collation of their data from various sources into one data set? (e.g. a single spreadsheet from all of their different apps etc)
Has anyone utilised any Business Intelligence/data analysis tools to analyse trend/spot patterns etc, in the data they collect? (e.g. Microsoft Power BI or the various other similar applications
Background…
I have collected a considerable amount of data manually and via various tools over the years. The challenge I have is that it’s all stuck in spreadsheets and basic line graphs. It would be more useful if I started analysing and manipulating the data better so that I can spot trends and test hypotheses by optimising my inputs (e.g. what I do, food I eat, exercise, sleep etc. etc.) Otherwise, I am just recording data for the sake of it and not maximising the benefit of it!
The various data sources I have are…
Apple Watch/Apple Health data
MyFitnessPal
Polar Heart rate app (For exercise)
My WIthing Scales
My spreadsheets (For my mood, energy levels etc.)
My KPIs for my business (My idea is that I could work out why my performance improved/reduced on certain weeks based on a correlation with other data points)
An API that fed apple health data in a suitable BI tool would do (The rest could be imported via CSV files)
The first wave of the quantified self-movement has worked well in that we get data from various apps/wearables etc. Still, the key challenge at the moment is getting all of the data from the multiple sources collated (Via an API type solution) and into a tool that we can start ANALYSING the data AND the RELATIONSHIP between the different data-points so the user can optimise and improve their behaviour/inputs and test hypotheses etc. (There is a massive gap in the quantified self-market for someone who could create a simple consumer accessible solution for this in my opinion!)
I hope other people have encountered this challenge and have some suggestions?
We are currently creating a platform that could fit some of your needs. Apologies for the pitch - but we aggregate data from multiple source, allow you to tag your events (possible to tag KPIS soon), and enable you to run a Pearson’s correlation coefficient to see if your KPIS and biometrics hold a relationship (fully operational soon)
Some of the APIs are still being built out from a frontend perspective, but they will be up soon, and we’d be more than happy to work with you to see how we can add value for you
If you are interested - you can message me your email and I can give you access now (https://www.cambrean.com) - if not - apologies for the message.
Our main points of differentiation in this busy space are:
Data driven insights (you’re probably tired of hearing this - but we have yet to see a competitor deliver on this promise) - your data is used for your bespoke insights ( not summaries/ trends - actual insights). We analyse your data to determine your normal ranges, we have appraised 100s of research papers that relate to your metrics and the factors that influence them - allowing us to generate bespoke, meaningful insights). To your question of how advanced the analysis is - we have not seen the depth of our analysis in any major competitor - the analysis gets as sophisticated as you want it e.g. you get more detailed insights based on your inputs ( both passive inputs via data collection or active inputs via tags etc) and integrations.
View all your data in one place - We are not creating a dashboard to show you a certain score, loading it full of emojis, promoting our views of what we think is important to you - We have focused on creating a slick UI ( not an app/web-app block builder) - so you can manage your integrations ( Whoop, Fitbit, MyFitnessPal, Garmin etc etc) and see the data you want.
-Customisable graphs - View it the way you want. Whether it’s bar chart, line graph, box-plot etc, table view (see above) - You can map your data, your way.
We aim to have our ‘Lab Mode’ ( feature that allows you to discover relationships between your data ( subjective & objective) and our test-flight app ( its currently a web-app) up in the next month.
We are still starting off with a small team - but we are building fast (with our fantastic bunch of early users) - and hope to deliver more ( data/dashboard export, V-AR model integration, ++ more insights and integrations) soon.
Nothing is concrete and we have yet to run simulations ( still early), but we are considering adopting Shiryaev-Roberts changepoint procedures due to asymptotic properties and suitable ability to handle dependent data.
Different models will be used, depending on the metric and f0/f1 characteristics, to keep an up to date p series of observations. Large variance will always be an issue - so may need to revise - but that’s the line of reasoning at the moment
