The word “Force Multiplier,” in military usage, refers to an enabler or a combination of enablers, which make a given force more effective than that same force would be without it.
So what force multipliers can be used in the world of Data Products ?
Based on our experience in curating intelligent data products for the industrial world we feel a Data Product needs a healthy dose of 2 things
1. Data Science = Seeing the unseen ( Surface actionable signals previously undetected to bake into a data product )
2. Behavioral Science = Making it relevant ( Making data product relevant to the daily context of tasks a person does to accomplish concrete business outcomes )
While there has been a lot of conversations around data science, we at Flutura felt that it was important to balance this with conversations around behavioral science which in our opinion is a force multiplier in driving adoption of data products.Having created intelligence platforms for the industrial world, we have our own share of successes and failures. One key learning from our failed data products was the need to weave behavioral science into data products. Allow me to share 3 real life stories from the trenches which shaped our thought process.
Why is Behavioral Science important to Data Products ?
1. It humanizes data.
It puts Big/Small data, Structured/Unstructured data, Low velocity /High Velocity , Machine learning, Map reduce, Collaborative filtering, Text mining, Graph theory, Apriori analysis and all the innovations which has enabled us to solve technical challenges and neatly bundles them it into a nice "box" which solves a real world business problem.
2. It aligns mental models ! Enhances "Consumer Resonance Index"
Consumers of data products think differently from the creators of data products ! Its a simple fact but as with many things simple, often overlooked. Incorporating learning's from behavioral science can heighten the resonance the data product has with real front line users which impacts a business outcome. ( CRI- Consumer Resonance Index as we at Flutura call it )
3. It makes the data invisible !
Data fades into the background and jobs which need to get done comes into the foreground. The best data products make the data invisible and the tasks visible ! It really should not "smell" of algorithms or data. Behavioral science understands these subtle nuances and the neural pathways which need to get activated in order to drive repeat engagement with the data product and get daily jobs done !
So how do we think about this ? 5 Conversation starters
1. HUMANIZING - How do we humanize a data product to heighten its engagement ?
2. RITUALS - Are we hooking the data product to daily "ritual" being performed by the data product consumer ?
3. IMPACT VISIBILITY - Are we amplifying the visibility of the outcome achieved based on the action signals sent by the data product ?