5. Data using organizations don’t get distracted.

Written by Raleigh Gresham

Data using organizations are not distracted by technology. They acknowledge it has a huge role in data use, but not THE role. Data using organizations can do more with SQL and Excel than most complexity advocates and vendors want them to believe. They task technology to strengthen and enrich what happens after the analysis not make the management of data easier.

Data using organizations fight against sexy-tech for the sake of sexy-tech. If there’s a vendor selling it, a data using organization is skeptical. They know bleeding-edge toys are powerful but that they can distract them. They evaluate all tools through the lens of hunting down action.

Data using organizations are not interested in the academic game of defining Big Data, the debate over the effectiveness of visualization techniques, the arguments over data science’s role, or the fights over competing approaches to data architecture. They master the recipe of technology and analytics, using the appropriate mix of each to create the perfect action hunting ecosystem.

Data using organizations pledge no allegiance to a specific tool, technology, or approach. SAS, R, OBIEE, Microsoft Access, Business Objects, Tableau, an abacus . . . they would choose a stone tablet over any of them if it meant creating to-do lists faster. If a smoke signal gets people moving, they use it. Big or little data, structured or unstructured, distributed computing or not – data using organizations are happy with each of them when they complement getting past the analysis, not distract from it.

Data using organizations are not distracted by the push to use and master every analytical tool available. They know what they know and can confidentially solve the vast majority of the problems they encounter with the small set of tools they’re comfortable with – even if these tools aren’t the most efficient, most expensive, newest, or most popular. They let their analysts be like experienced craftsman, each with a well curated and limited set of tools they use based on what works for them and what doesn’t.

Data using organizations tend to play with data more than they analyze it and avoid the distracting aspects of data environments that make playing hard. They think like hackers and prefer “development” environments where they can go against convention to answer tricky questions in rule-bending ways. They need the freedom to change the approach on the fly and see the impact quickly. The laws of traditional business intelligence frustrate them and the requirements of production level data environments, while necessary for classical reporting, hold them back. Data using organizations build and work in data playgrounds.

Data using organizations are not distracted by the data “unicorns” the Harvard Business Review writes about. They ignore the hype surrounding data in today’s popular press. They’re not interested in winning the analytical lottery. They don’t expect to cure cancer with every data set and analysis. They don’t demand or require PhD math backing, and instead lean on everyday analytics and techniques because of their effective simplicity – simplicity that makes for exciting actions not necessarily exciting journalism.

Data using organizations are less passionate about decimal places, confidence intervals and significant digits than they are about getting a to-do list in the right person’s hands. Unless they’re landing airplanes or selling pacemakers, they don’t let perfection become an excuse. They recognize most data sets and models can’t scientifically achieve absolute perfection.

Data using organizations don’t wrestle with data incompleteness and are immune to outlier paralysis, not letting 3% of cases render data or systems useless. They accept and design for the norm of 80% accuracy, 80% population representation, and 80% completeness – they use what they’ve got and get going. This simultaneously frees up time and resources while empowering the data to do more.

Data using organizations work with what they have. They bootstrap. They hack. They do anything to keep from waiting for something better to come along before they use data. Budget plays little role in a their ability to use data. Data users don’t let one more variable, one more row of data, one more type of chart, one more byte of processing speed, or one more team member distract them.

Data using organizations are comfortable with the wild nature of data and work with what the data gives them. They don’t try to force the data to be something it isn’t, spending very little time trying to teach it tricks or behave in certain ways.

Data using organizations start as manually as possible. They learn to feel the rhythm of the data and the analysis before automating it. They don’t let the scalability of what they’re doing distract them too early.