In a previous post, I used the Large Hadron Collider as a metaphor for big data and big analytics where the creative destruction caused by high-velocity collisions of large volumes of varying data attempt to reveal elementary particles of business intelligence.

Since recent scientific experiments have sparked discussion about the possibility of exceeding the speed of light, in this blog post I examine whether it’s possible to exceed the speed of decision (i.e., the constraints that time puts on data-driven decision making).

Is Decision Speed more important than Data Quality?

In my blog post “Thaler’s Apples and Data Quality Oranges,” I explained how time-inconsistent data quality preferences within business intelligence reflect the reality that with the speed at which things change these days, more near-real-time operational business decisions are required, which sometimes makes decision speed more important than data quality.

Even though advancements in computational power, network bandwidth, parallel processing frameworks (e.g., MapReduce), scalable and distributed models (e.g., cloud computing), and other techniques (e.g., in-memory computing) are making real-time data-driven decisions more technologically possible than ever before, as I explained in my blog post "Satisficing Data Quality," data-driven decision making often has to contend with the practical trade-offs between correct answers and timely answers.

Although we can’t afford to completely sacrifice data quality for faster business decisions, and obviously high quality data is preferable to poor quality data, less than perfect data quality can't be used as an excuse to delay making a critical decision.

Is Decision Speed more important than Decision Quality?

The increasing demand for real-time data-driven decisions is not only requiring us to re-evaluate our data quality thresholds. In my blog post “The Circle of Quality,” I explained the connection between data quality and decision quality, and how result quality trumps them both because an organization’s success is measured by the quality of the business results it produces.

Again, with the speed at which the business world now changes, the reality is that the fear of making a mistake can not be used as an excuse to delay making a critical decision, which sometimes makes decision speed more important than decision quality.

“Fail faster” has long been hailed as the mantra of business innovation. It’s not because failure is a laudable business goal, but instead because the faster you can identify your mistakes, the faster you can correct your mistakes. Of course this requires that you are actually willing to admit you made a mistake.

(As an aside, I often wonder what’s more difficult for an organization to admit: poor data quality or poor decision quality?)

Although good decisions are obviously preferable to bad decisions, we have to acknowledge the fragility of our knowledge and accept that mistake-driven learning is an essential element of efficient and effective data-driven decision making.

Although the speed of decision is not the same type of constant as the speed of light, in our constantly changing business world, the speed of decision represents the constant demand for good-enough data for fast-enough decisions.

How quickly do you decide? Please share your thoughts on how decision speed affects data quality, business intelligence, and data-driven decision making.

This post originally appeared at OCDQ Blog.