“We have been sitting here for hours, this is crazy” John said to nobody in particular. This was John’s first trip to China, and he and his wife were on a driving tour of the country or more appropriately a “stopped” tour. In turns out that John and his wife were stuck in one of China’s infamous traffic jams. What they did not know at the time was that they would be stuck in their car for the next 12-days.

You’re probably thinking that there is no way that is possible! But it is true it happened in Oct of 2015 on the , G4 Beijing-Hong Kong-Macau Expressway.

Apparently, traffic jams of this magnitude are fairly common place in parts of China, and are a result of several factors. First, most drivers in China are new to the experience of driving, and have not developed “good” driving habits. For example, they rarely wear seatbelts or drive in the designated lanes, and often stop in the middle of the road to consult their maps.

Second, the sheer volume of cars – 10 times more cars today than 10 years ago – has far outpaced the government’s ability to improve road infrastructure. Finally, driving laws have not been updated to encourage the right behaviors.

The net result is that driving within China is still perilous. Large traffic jams will continue until the infrastructure improves and motorists develop better driving habits and behaviors. But, what do traffic jams and bad driving habits halfway around the world have to do with data governance? More than you realize.

Driving will get worse before it gets better…

Like China, many financial services companies are staring at the potential of having more “cars” on the road, which could lead to more traffic jams and likely more accidents. As a result they will face (are facing) similar challenges in the form of:

1. More cars on the road.

Think about this - more data has been created across the globe in the past 2-3 years than in the entire previous history of the human race – and the growth is not slowing down anytime soon. As a result, companies will be continually challenged around data accessibility, timeliness of delivery, as well as whether they can efficiently scale their business and IT processes to handle the increased volume without suffering a higher accident rate for critical enterprise data flows.

2. More Stop Lights and Speed Limits along the way.

Rapidly increasing regulatory scrutiny around how data is managed (e.g., DOL fiduciary rule, BASEL, BCBS 239, etc.) will require companies to add transparency to the quality of their data and their data management practices throughout the data lifecycle.

3. Longer Commute in Less Time.

Increased regulatory scrutiny generally means more reporting not less, and faster, not slower delivery of information. This presents a challenge for some companies as they have not “cracked the code” on some of the foundational components such as implementing good data management practices and the underlying data architecture that supports operational processing and reporting.

But here is the reality; financial services companies are not idly standing by watching these challenges manifest themselves. In fact, the companies I have spoken with are addressing these and other challenges head-on. They work extremely hard and expend a significant level of effort – manually or otherwise - to put architecture, systems, and processes in place to control and govern their data and data flows (traffic).

More often than not the level of effort expended takes the form of large amounts of carbon-based computing power (humans) dedicated to “data management”, and/or large scale IT related programs that consume huge amounts of resources.

Make no mistake, these are not simple challenges that can be addressed overnight. As companies experience these and other challenges they are being forced to examine how they are doing things today, to understand what they need to do differently tomorrow – this is continuous improvement. So is there a better way or approach to addressing these challenges?

Get your learners permit first!

It is my belief that if companies examined how they manage data today and make some simple changes to their “rules of the road”, identify and leverage better habits around data management, and improve them where needed, they can prevent serious traffic jams and accidents.

Before we can take to the road, we need to understand and recognize what “good” driving habits look like. These include asking for clarification when you don’t understand what something means (defining business terms), focusing your data management efforts on the critical few (criticality), knowing how good the data is before you put it in a report or use it in an analysis (data quality), and ensuring you are getting data from the right source (data architecture).

Unfortunately, most people think they already have all the good driving habits and behaviors, so they don’t understand the need to change. True, many companies already have good data management habits and practices. However, they are rarely applied in a consistent, repeatable manner, and as a result, they are only locally effective, costly to expand and rarely sustainable – it is like having traffic lights in one half of the city and roundabouts in the other.

To move towards consistency would require half of the city to rip out costly infrastructure. What if tomorrow there are ten times as many cars on the road going four times as fast? Can organizations afford to have the number of traffic jams and accidents go up at the same rate? I would think not. It seems to me that there is a way for companies to produce better habits around data governance and data management….less stop lights, but in better places within the city.   Experienced drivers wanted…

We know from experience that even if we have good habits and apply them consistently, others may not - sometimes at great cost to us or the company. Whether we realize it or not, we are subjected to governance every day! – Traffic laws, speed limits, stop signs, insurance options, etc. these are all forms of governance that help to shape our habits and behaviors. And the truth is that we readily accept this governance without much thought.

Successful data governance and data management programs should be much the same way. How you approach data governance, data management and data architecture should be with the singular goal of instilling the “right” habits and behaviors across both business staff and IT partners.

As mentioned before, this will not happen overnight. As in the case of driving, we’ve all had many years to develop the right habits. We put on our seat belts without thinking; we tend to drive the speed limit (most of us) and we yield to pedestrians – even though there are no police officers everywhere making sure that we are following the rules.

We tend to accept these rules of the road without giving them much thought because they are simple, easily understood, and designed to promote better habits. Add to this the fact that we receive training in how the rules are applied (driver’s ed) and we have a set of tools at our disposal to ensure we are following the rules (speedometers, seatbelt notifications). This philosophy – simple, easy to understand rules enabled by the right tools – is what will result in the right habits and ultimately, sustainable data governance within an organization.

Free flowing highways

When you have implemented consistently good data management habits and behaviors within the business and IT communities it tends to be “smooth sailing” with far less accidents and injuries. So, how do we get to smooth sailing?

First, you need to establish clear accountability and responsibility for your data – in other words some type of stewardship structure. Habits and behaviors can’t be changed in a vacuum. Building a community of likeminded individuals all heading in the same direction and at the same relative speed will help to instantiate the good habits faster and more completely across the organization – smoothing out your traffic patterns.

Next, you should engage your stewardship community to implement a standard set of data governance practices. Establishing good data governance within an organization requires you to practice different aspects by executing a series of logical activities until they become second nature.

What if I told you that on average you will spend 39,000 hours driving a car in your lifetime? I think that would qualify most of us as experts according to the “10,000 hours of practice rule”. But, at some point in all those hours of practice, we stop practicing driving with are hands and feet and start practicing with our minds because the mechanics of driving all those years have now become second nature.

We have effectively changed our overall mindset and behaviors. The more you practice, the better you get at managing data and the more that others practice the same things, the smoother the drive down the highway will be. The end result is a governance practice that is part of your “operational DNA”.

Learning how to drive is hard…

If driving was easy, we would all be NASCAR drivers. But the fact is that driving is not easy and neither is trying to implement standard practices across an organization. Most people do not like change especially when the change is focused on their habits and behaviors - the way they do their job on a day-to-day basis.

Becoming a better driver does not happen overnight, it takes time and practice. This is why you should be deliberate and thoughtful in your approach to managing the change, ensuring that you help both business and IT staff through the proverbial change curve (figure 1) one step at a time.

Finally, what can you and others across your organization do tomorrow in order to change your habits and behaviors to become a better driver? Here are some simple practices you can follow:

1. Define your “owners” manual – gather a small group of people to identify the most frequently used business terms, develop a common definition for each, and publish them to the organization.

2. Gain a better understanding of what is critical to your organization – engage your subject matter experts to identify the sub-set of data that is most critical, define their specific quality requirements, and establish an on-going process for monitoring the quality of that data.

3. Identify, prioritize and track your data issues and improvement opportunities – start by building a list of your biggest data related issues, define them, assign ownership and start working through resolution.

Implementing these simple practices will provide you and your teams with a wealth of information about your area. The good habits and behaviors that are developed through this work will lead to valuable outcomes such as minimizing miscommunication and misinterpretation; lowering operational risk, improving the speed with which you identify and adapt to change, and identifying potential issues or opportunities earlier so that your organization is better positioned to realize gains or thwart major “accidents”.

Better drivers with better driving skills, habits, and tools have less accidents, spend less time snarling traffic patterns and get to their destination more quickly and reliably.

(About the author: Michael Nicosia is the vice president of strategy and data governance at TIAA, a national financial services organization with $915 billion in assets under management and the leading provider of retirement services in the academic, research, medical and cultural fields. He has been guiding the finance and actuarial area of TIAA on its data governance journey since 2011. The views expressed herein are solely those of the author and do not necessarily reflect the views of TIAA.)

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