Using the Rise ScoreBook collector to create a “derivative” metric

The Rise ScoreBook collector was created to help ScoreBook managers to create derivative metrics from metrics (and score entries) they already have on any of the ScoreBooks they manage.

The benefit of this is to enable you to re-use the score entry data you’ve already brought into your ScoreBooks and so create a more sophisticated ScoreBook.

Examples of when you can use this Score Collector are:

  • You have 2 different ScoreBooks for the same group of users, and you decide you’d like to create a 3rd ScoreBook that will have a metric that will be a function of metrics in the first 2 ScoreBooks e.g. you’ve got ScoreBooks for 2017 and 2018 sales for your sales reps, and you want to create a ScoreBook that scores on the total of 2017 and 2018 sales.
  • You want to create a derivative metric on your existing ScoreBook that calculates the sum or difference of score entries recorded at different time points for another metric on the same ScoreBook e.g. you’re tracking weekly sales of your sales reps and you want to calculate the % change in sales between two consecutive weeks

An example of a ScoreBook that I’ve created using this feature is Twitter Followers Club pictured below:

If you’d like to follow my steps, I’ve provided full details of how I built this ScoreBook in this help article:

How do I create a ScoreBook with a metric which auto-calculates the difference between the two most recent score entries of an existing metric?


I’m a Data Stream Believer

I’m a data stream believer.

Let me explain what that means.

I see data arriving in a stream: I’ll either process and use it, or I let it pass me by. I don’t bother storing it.

Storing data is a popular paradigm (the “data ocean”) but I believe that people often fail to really understand that the value of data isn’t fixed – it only really has value in context and time.

For example if I tweet “hot cross buns going fast. come to the kitchen now” that data point only has value for a short period of time: a few minutes later, if you care about your buns being hot, and a little longer if you’re not so bothered. In any case, in an hour there will be no hot cross buns to speak of and the value of that data point has now decayed to zero.

To take advantage of the data stream, I need to listen to it and be watching out for key events. Events like those hot cross buns being available. To do that, I’ll either set up an alert for “hot cross buns” or I’ll make sure I follow the right twitter account. In both cases I have thought ahead and set up a listener.

I am now acting as a data stream believer. I’ve decided what I’m looking out for and set myself up with an alert for when it occurs. When it occurs I’ll then take action.

To me, this “data stream believer” should be our dominant strategy when working with modern data – particularly big and social data.

The alternative data ocean strategy is a lot harder to make work. Data ocean thinking goes a bit like this  – “I don’t know what data I really want right now but I am sure in the future it’ll be valuable. What we’ll do is store all the data and then when we need it we can query it. By storing everything we’ll never be caught short.”

I think there a couple of issues with this train of thought:

  1. Data storage costs just keep going up. Over time you’ll always need more storage as more data is created and stored. You’re not recouping the cost of storage anywhere so it’s just a black hole of cost. Admittedly data storage costs are going down but there’s still a cost somewhere.
  2. You aren’t using the data you do have. This is more important than cost – data oceans leads to woolly thinking and sloppy processes. You’re storing something but you’re not doing anything with it. It’s just sitting there gathering virtual dust. Even if its just cognitive cost (at the back of your mind you know that data is there) it’s still a waste of your attention.

Let’s make this real, with a consumer example of data stream versus data ocean thinking – digital photo storage.

Family 1 opts for a data ocean approach. Every photo they ever take is stored on the cloud. They never look at their old photos. Scanning through them is a boring chore as there are multiple copies of the same snap, a bunch of out of focus ones and some scanned expense receipts in there too.

Family 2 opts for a data stream approach. Every year they review the photos they took from the last year and choose 50 to go in that year’s family album. They get the family album printed, share it in the living room and never look at the cloud folder ever again.

Which family do you think is getting the most out of their data (in this case family photos)?

So we can see – a data stream believer takes the data stream and processes or discards it. Value is added immediately – and often it is pushed straight back into the stream.

It is with this philosophy how Rise works – data points are  pulled from the stream on each player, processed into scores and pushed back to the stream as a release of the leaderboard. This then feeds other people’s data stream activities – how did I do last week – time to optimise for next week.

The data stream creates a faster, more vibrant, feedback loop and uses data well.

How about you? Are you ready to become a data stream believer?



Improved Notification Emails for Managers

We’re rolling out a new feature this week to provide notification emails for managers around two key events:

  • data collection completion
  • release generation

The release generation email will let you know when a release has been created and provide a summary of metrics for all the players on the board.

UN Social 500 has completed generating a release   toby   Rise Mail.png

The data collection email will let you know when the data collector has completed, how many new score entries it found. It will also highlight any players where it wasn’t possible to collect data.