I recently finished reading Thinking, Fast and Slow – by Daniel Kahneman and got to thinking about how we try to supplement the mind’s ability to transfer deep experience and knowledge to gut reactions and instant decisions. In general I am thinking about things like the existence of graphs to represent data sets and how the invention of the concept of graphs created a situation where people could quickly analyze a data set and make decisions based on that analysis. Now graphs have been around for a long time, but KPI dashboards with red, yellow, green are a little newer but were created out of the same need to transfer the information from a larger dataset into a view that can be digested/acted on quickly. These aren’t inclusive of all the productivity enhancements that have been created over time, but you get the idea of what I think the goal of productivity tools ends up being. If you have read the book (or go read it and come back to this post), I am talking about the transfer of information that needs to be processed by slow system 2 thinking to a state where it can be processed by fast system 1 thinking.
The thinking here is that as I look at the changing landscape of productivity software, I see a lot of areas that we as an industry have made interesting assumptions, skipped areas of thinking that could benefit from productivity tools, and places where we just haven’t evolved very far yet.
One of those areas is how most productivity tools are geared towards making decisions faster. There are not a lot of tools that are designed to take refined representations of data and helping people break them apart to understand what kind of data needs to be behind them. Things like getting people to recognize when a quick decision is simply not possible. For example if the data set that is supporting a graph is not that great we don’t really tell people. I am thinking of things like if the graph represents a statistical interpretation of the data and the data set contains less than 30 samples, we generally aren’t informing the reader of the graph that a quick decision may not be pertinent and that they should spend more time with the underlying data. Part of this problem is that the tools we usually make for productivity are tools for owners of information to collect the information and represent it or manipulate it. This is obvious by the places where we do make note of bad data, for example there is a trigger in excel that highlights when a formula in a single cell doesn’t match the formulas in the surrounding cells. Yet we don’t build much of anything into document readers to analyze the language for validity or the viewers of KPI dashboards to understand what kind of data needs to be behind those red, yellow, green statuses. I think that we as an industry need to examine more deeply where quick decision making makes sense and where we can inform people at the point of consumption where a longer decision process is warranted.
People pay for knowledge, they don’t want to build their own rules that are SOX, GLBA, or HIPAA compliant just like they don’t want to understand the underlying logic of statistics that drive the decisions they are making. We know that people would rather pay for someone else to create compliance rules because so many companies and consultants are making money guaranteeing they have interpreted those regulations to the best of their ability. Similarly we know that people would rather buy a system, be it a weight loss or a stock picking system instead of understand the underlying fundamentals of how the body gains and loses weight or how a market prices stocks. Capitalizing on that need for data to be interpreted by a valid third party is done in specialty scenarios yet it isn’t done often in common scenarios. Why can’t my software suggest that I need to know I used the word first to start two paragraphs? Why can’t my instant messaging client inform me that the person I am talking to is tweeting our conversation? Software bringing standard knowledge and collected data sets together is another large gap. It seems that the Microsoft Office products are trying to pull some of those data sets in with their new application platform; however, I am not talking about productivity only in the context of applications like those found in the Microsoft Office suite.
Another example where we are missing the boat on building useful productivity tools is related to the app connected-ness that doesn’t seem to exist. A lot of tools have been built to measure our interactions with the world, assumingly so that we can be more productive in the world. Fitbit measures steps, weight, etc. Rescuetime measures computer interactions, etc. The list really goes on and on with tools that have been built to measure our interactions; however, they don’t really do anything to transfer those measurements into any kind of actionable presentation. For example, if I measure my steps per day & my calories per day, I don’t get a Groupon email alert at 11:20am suggesting a discounted restaurant fits with my fitness goals (e.g. an easy mile walk away to get additional steps in). I don’t get a healthy recipe alert at 4pm with a coupon to the grocery store because I had cake at the office party. Connecting these disparate measured-self applications along with the other features on the web will be a critical component to the app world advancing.
A recent example of app integration beyond the realm of measured self is twitter trying to establish itself as a platform. Of course it started with the @ symbol which was rooted in comment systems and message boards for decades as a way to position a response to someone. They used it for the same purpose and for the younger generation, twitter was the place where the ‘@’ response originated. They took over the hashtag and more recently have taken over what they are calling a cashtag, which until recently were the in-depth analysis of stocks by the social network on Stocktwits. What twitter missed in this integration was the productivity tools that stocktwits has built on top of their social platform. Stocktwits made their ticker streams a productivity tool by curating high quality stream participants while allowing anyone to participate, adding the voice of the company, adding pricing and technical analysis data, etc. This is again a method of representing more data so that the person consuming it can make better and faster decisions; however, it is in an area where we didn’t have the advantage in the past. With twitter subjugating the ‘$’ tag on their network they have minimized the value in a respectable attempt to bring the same value to more people (although it would be nice if they would actually bring the productivity value that stocktwits has created).
In the end, it is all about the private investigators that are hired by the psychics. In the productivity industry, we make all the hard work behind the data sets easy to manipulate and even easier to consume. We need to start focusing more on the person who’s palm we are reading and how we can help them understand what is being told to them if we are going to make another major improvement in productivity tools.