The Rise of the Data Personal Trainer

The more I ponder the problem of data, the more I find the human. Today an almighty and ubiquitous technology creates a “there is an app for it” effect, where anything should be possible, simple and affordable, and yet what someone does with that app makes the difference. The more data is produced or exchanged in the world, the more it counts who uses that data and for what reasons. A “Digital Neohumanism” era is dawning. In the 21st century, thriving companies connect their human and data ecosystems in the most efficient, frictionless and safest manner.

We all know that data is not for the fainthearted ones. Its incredible potential is locked behind thick walls of misconceptions, an overinflated hype, a healthy dose of anxiety, a general resistance to share and challenge “established” knowledge, and an ever-complexifying technological infrastructure. When you arrive in an organization’s first Data Officer role, the messianic expectation is overwhelming. Patience runs out quickly as the over-starved hunger for data is unforgiving and requires immediate satisfaction. The relentless ask for a “data strategy” as the manifestation of a magic wand, with an AI zest, miraculously solves all the ailments. 

In reality, organizations behave like someone who has the urge to run a marathon and thinks they can compete professionally after having practiced only a bit of amateur sports and a few sessions with an excellent personal trainer. So, if organizations want to be professional Data Athletes (DA), they need to be supported by a meticulous Data Professional Trainer (DPT)--you, their data leader! 

If I were a personal trainer, I would want to assess my athletes, their health status, nutrition, habits, and most importantly, their ambitions, intent, and mindset. On that basis, I would probably set  methods, which would include diet, rest patterns, hydration, mindfulness, exercise schedules, and fitness machines. Then, I would get to the critical part — the measure, which would track their performance, hopefully one day fulfilling their ambitions to run a marathon under four hours (a respectable time).

So when the DA enters your Data Excellence Gym for the first time, it is good practice to perform a thorough “assessment” and, in line with the approach above, to ascertain:

1. Mindset:  in effect, the culture of the company, inclusive of its strategic ambitions

2. Methods:  the company’s mechanisms and capabilities for change 

3. Measure:  the value creation that should be part of a business strategy. 

1. Mindset Is culture important for a DA? Suppose you believe that data-driven ambitions are enabled by a consistent data lingo across the enterprise, and you observe that your internal “tribes,” who are holding on to their dialects, are making that difficult. In that case, the answer is a resounding YES. Thus, as a DPT, the first thing to do is to concentrate on the DA’s standardization muscle: 

   1. how flexible it is in accommodating nuances of meanings

   2. how strong it is in pushing system owners to adopt consistent definitions

   3. how reactive it is to the quality of the data.

      You might find out that the standardization muscle needs a supplement in the form of business experts that should be elevated to the rank of data standard leads who would define what good data looks like. Now, all your DA needs is to exercise its antagonist muscle —the data execution one (read: Functional or Business Unit Data Officers), and your cross-tribe DA can thrive in its newly found enterprise culture.

2. Methods: We all go through change. They say that change is the only constant. Evidence tells that, in the last decades, change has been quite forgetful of data. We conceive changes. We produce requirements and designs for businesses and systems. We consequently and consistently build processes and systems. And, all of a sudden, we are reminded during the test phase that “the data is not good enough to go live.” 

   Our DAs have been focusing on the wrong schedules, using gym machines with scarce attention to repetitions or weight management, and their musculoskeletal evolution is not right. The helpful DPT analyses how the DA goes about change and inserts data gymnastics in the existing change methodology. 

   Is the DA producing “user stories”? How about evolving them into “data consumer stories”?  The final user of data articulates what data they want and what data quality, security and accessibility are expected and describes the value generated. Instead of just building pipelines and applications, there is an opportunity to remove a bit of “data fat” (data debt) accumulated in an organization under the guise of inaccurate, inconsistent or over-retained data and uncharted data flows. Change is a crucial moment and should be intended as an opportunity to practice the exercises of making data better.

3. Measure:  Finally, the juiciest chapter is the “measure,” the highly-anticipated moment in which the scales tell us if we lost weight or the stopwatch is giving an elating or disappointing verdict on our performance improvement. During the first days at the DeX (Data Excellence) gym, you will probably be staring in the face of DA wannabes who can’t articulate (or conversely would have the hyperbolic expectation of) the value that they are going to get from all this data exercise, and are not aware of how much sweat is required.

   If asked how they would want the data to be, they would be very binary; either their data is perfect or they cannot achieve anything. Perfection is an expensive aim to accomplish. You might want to do a counter test asking, “What if the data is 90% accurate?” Their puzzled faces will be symptomatic of a brain drawing a blank. They simply have no way of linking their performance to the required exercise. But, if they have been zealously and assiduously following the two previous steps (mindset culture and method change), they should be fit enough to know how far they can go. Even better, if they correctly exercised and applied agile methodology, they could establish a very fruitful relationship between the minimum viable product (MVP) and the minimum effort of data debt resolution, as outlined in this short paper I authored with an old schoolmate of mine.

They say that the journey to good data is not a sprint but a marathon. So it is up to us Data Personal Trainers to build in our Data Athletes the necessary stamina, focus, rituals and habits to sustain them. Although we are going to be there to coach them, encourage them and push them if necessary, they will have to realize that to win the race of the digital world, being a Data Athlete is not a fad but a change of lifestyle.

About the Author

Roberto Maranca has almost 30 years of experience in IT and Data. He started his career with Nissan and Ford in Rome, then joined General Electric Capital where he spent most of his work life. As the first Chief Data Officer for GE Capital’s International Division in London, he was in charge of setting up data governance, data quality and advanced analytics, from supporting risk model validation to leading regulatory reporting initiatives. Subsequently, as Group Chief Data Officer at Lloyds Banking Group, he reshaped their data strategy, setting up their metadata capability and dividing his time between the BCBS 239 and GDPR programs. Finally, in 2018, Maranca joined Schneider Electric as the Data Excellence Vice President, delving into the cultural and methodological aspects of becoming a data-driven company. Over the last four years, he set up and has been owning essential data excellence capabilities like metadata, data quality, data retention and data risk  management. Maranca has a master’s degree in aeronautical engineering from Federico II Naples University in Italy.