Digital Transformation and the New Prisoner’s Dilemma
Taylor Culver

Taylor Culver

Jul 2020

Digital Transformation and the New Prisoner’s Dilemma

Data is the New...Prisoner’s Dilemma?

Data is an asset - nope. Data is a commodity or “oil” - sorry. Data is, well let’s be honest, sometimes more trouble than its seemingly worth. 

Firstly, data is not an asset. Consult the GASB or FASB (General & Financial Accounting Standards Board) and you will quickly see data doesn’t show up on the balance sheet. As for oil, you don’t see cars lining up to fill up their tanks with SQL queries. If anything, keeping the car analogy going, data is exhaust - an output from the engine offering little to no value back to the mechanism that produced it absent intervention. So what is data...well it’s a dilemma for most.

 

What is The Prisoner’s Dilemma?

For those of you who took PSYCH 101 read on. Otherwise, when two prisoners are suspected of committing a crime that they in fact committed with no witnesses, there is no way to convict them unless one effectively “rats” on the other. The police encourage the prisoners to confess by offering them a reward for ratting the other person out. Psychologically speaking, the reward is greatest to the individual for ratting out their co-conspirator while their co-conspirator remains silent. However, if both prisoners rat each other out they will both go to prison. This is the prisoner's dilemma. The only benefit to the collective would be if both prisoners kept their mouths shut and were let go because there isn’t enough evidence to convict either of them. Who has the courage to take that risk? How much do you trust the other person to act in their self interest?

 

How Does This Relate to Data?

This may be a little bleak, but the two prisoners in the world of data are the business and the respective data teams. Whether they like it or not, they need each other to survive and thrive. If they collaborate, digital transformation is a regular everyday occurrence. If they choose to compete the organization incurs gridlock. If the data leader “rats” out the business by pursuing their own agenda you get a science project and unadopted solutions. If the business follows the same rationale, you get data silos. The diagram below illustrates the outcomes of this organic conflict.

How Does This Impact Your Organization? 

This may seem a little abstract and theoretical, but it’s not. Organizations where the business and data teams are not effectively collaborating are working backwards. Time is being wasted, employees are disengaging, shadow processes begin to emerge, and productivity begins to drop. Furthermore, data teams that are strong arming the business are likely making significant investments that are going unused or adopting inserting a low value recurring cost into the organizations profit margin. Data silos are just as toxic for organizations. Trench warfare between departments between data silos leads to poor data governance, parallel processes, poor information sharing, and data fatigue.

 

How Do You Change It?

Not all is lost. You don’t need to shut the operation down. The reality is, having good data is like health. A simple metric and a pinch of self discipline will sort the issue out.

  1. Set Goals That Have Clear Business Impact: Teams that aren’t collaborating do not share the same goals. It is the responsibility of the data leader to establish clear goals that unite the business and data teams and obtain executive sponsorship.
  2. Ensure End User Engagement: If you don’t know who will be using what you’re building intimately, stop. All too often do we rush to solutions because it’s easier than understanding the problem. All too often are smart solutions delivered on time in a way that doesn’t solve the actual underlying problem.
  3. Build a Cross Functional Team With a Shared Responsibility of Attaining #1 & #2 Within a Timeframe: If another team is taking initiative with data, point other departments to their success. Establish a cross functional team that shares a common technology architecture to drive business outcomes with data.

As you can see, data can be quite the dilemma. Oftentimes the problems seem pretty abstract, but the reality is that the root cause is oftentimes people and teams. Understanding if the data or business is driving your data initiatives at your organization is step one. Observing the actions and behavior that occurs between both parties is step two. Once you understand who’s leading the way empower them to do so, but ensure collaboration under common goals. It’s not difficult to right size a data initiatives it just takes organizational focus, executive sponsorship, and a little bit of time.

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