A Simple Method to Help Data Analysts Understand Clients

A disconnect often exists between the teams that package data and the clients that use it. Odd as it may sound, a role-playing exercise can bridge that gap.

Written by CS Chan
Published on Mar. 23, 2022
A Simple Method to Help Data Analysts Understand Clients
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We live in a day and age where data has become at least as as valuable as oil, gold and other physical commodities. Many companies exist for the sole purpose of generating, handling and consuming data. Knowledge has become a key driver for the advancement of our modern society and economy. Yet, the knowledge economy has created a dichotomy between producers and consumers.

On the one hand, engineers and researchers go through extensive training to create complex data infrastructures and generate vast amounts of data with strict adherence to detailed specifications. Nevertheless, these specifications are usually light on explanations and justifications for the data themselves. It is not uncommon to find engineers and data researchers who, despite their in-depth training, are completely ignorant about how their clients use what they produce.

On the other hand, clients consume these data, taking on all the plusses and minuses, while giving little thought to the plethora of concerns that data producers had to consider just to come up with consistent data. They expect to see predictions about what will happen tomorrow already showing up in their terminals yesterday. And when they don’t, they fire off angry emails to complain. As a result, customer success teams often find themselves stuck in the unenviable position of being the bearers of bad news, either telling frustrated clients how a certain request can’t be fulfilled or warning their colleagues that a dissatisfied client is keen to go to a competitor.

Companies that are eager to keep their clients happy and satisfied can’t afford to keep their employees ignorant about how the clients use the work they produce. But this isn’t something you can simply teach; any such teaching would just go in one ear and out the other.

More From CS ChanYou’ve Finally Nailed Down Your Company Values. What Happens Next?

 

Your Data Staff Needs a Purpose

In his book Drive: The Surprising Truth About What Motivates Us, author Daniel Pink argues that the three elements necessary for motivation for an intellectual task are autonomy, mastery and purpose. Although strict, detailed specifications certainly serve as a useful reference on the job, they are ineffective in driving positive results from data workers at best and deeply detrimental at worst. The reason is simple: specifications alone do not satisfy an innate human need to direct our own lives (autonomy), to get better at something that matters (mastery), and to do what we do in service of something larger than ourselves (purpose).

Most businesses are great at managing their staff to the point of compliance. Many fail to truly motivate staff, however, because at least one of these three elements is amiss. Autonomy requires certain levels of mastery from the staff, and mastery is propelled by purpose. The good news is that many businesses are ready to give their staff autonomy once there is mutual trust. Accordingly, they are happy to support their staff on the road to mastery. 

The bad news is that, despite the fact that purpose is the most fundamental of the three elements, it is also the most obscure, the most elusive.

In a manufacturing society, purpose was easy to translate and relate to: Good screwdrivers allow people to tighten or loosen screws with ease. Simple. In a knowledge economy, however, the answers aren’t so obvious anymore. After all, what is the real-life benefit of collecting and organizing various facts and figures of mergers and acquisitions (M&A) for our clients? (Trust me: I tried for years to explain to my own family what I did for a living. In the end, the best answer they could come up with was: “He works in finance, but neither at a bank nor as a stockbroker … We think.”)

To identify the true nature of purpose in our work, we have to chip away at it layer by layer.

What Is the Overall Purpose of My Work?

We create value for our clients. For a data provider, this means supplying data that helps clients make decisions faster, more efficiently and more accurately.

What Kind of Value Is My Work Bringing to the Clients?

Our work brings value to clients by solving their problems. As discussed above, a data provider supplies data that help clients make better decisions. More specifically, this means different things for different areas.

  • For engineers, creating easier-to-ingest data schemata, faster API calls, more robust search functions, better error and fraud detection and so on.
  • For researchers, developing more comprehensive data coverage, more precise and updated taxonomy and more pre-calculated values used in common automation.
  • For customer success teams, this means appreciation of both pain and pleasure points of the business process, suggesting alternative solutions and workarounds to a problem and any other client-facing tasks.

What Problems Do Our Clients Need to Solve?

The solutions to these problems will move the business’s mission forward. For instance, in finance, the problems would involve facilitating the flow of capital from one party to another. A financial data provider will need to bear in mind that the supplied data needs to help clients move money more easily between buyers and sellers, borrowers and lenders and so forth.

What Are the Business Missions of Our Clients?

That depends on the industry of the client. There is no single answer.

To sum up, this is the main purpose of your data team:

“Whatever the industry of your client, your data should ultimately help your client solve problems more easily. Your data output and customer support should incorporate features that help clients make better decisions faster, more efficiently and more accurately.”

What? That statement wasn’t helpful? What do you mean, “It was incomprehensible!”? Haven’t we just painstakingly laid the entire process out, layer by layer?

Of course it wasn’t helpful! As we said before, we aren’t building screwdrivers anymore. The question of purpose is much more complex today. So, let’s move on to the next step: role-play.

 

Role-Play: Not Just for Nerds Anymore!

Role-playing sounds like something best reserved for nerdy gamers during off hours, but that is far from the truth. Well-designed role-play exercises will open your staff members’ eyes. After all, their objective is to produce data that is better aligned with your clients’ needs. Before they work on improving data alignment, they need to understand what they’re aligning the data with. What better way is there to let them know your clients’ needs by putting them in your clients’ shoes?

Just because your staff works in a marketing tech company doesn’t mean they understand what marketing folks do day-in day-out. The same thing goes for fintech workers and their understanding of investment banks and fund managers. So, the first thing they need to learn before a role-play is how the industry you serve actually functions.

Role-Playing for Data Analysts

  1. Walk through your clients’ typical day.
  2. Dig deep into how data is consumed.
  3. Put them in the clients’ shoes.

 

Step 1: Walk through Your Client’s Typical Day

I used to manage a global team of M&A researchers. Although some of them had been hired for their keen interest in finance, the vast majority were brought in because of their multilingual backgrounds (M&A is a truly global field in the world of finance) and not for their financial acumen. On the other hand, investment banks, which were the clients we served, were notorious for keeping the inner workings of their industry mysterious to outsiders.

During the onboarding process, we didn’t start right away with what the new team members would do at the job with lists of instructions and specifications. Instead, I led new joiners in hands-on workshops, presentations and even a scripted web drama series, to give them a bit of insight into what investment bankers do on a daily basis.

Notice that the introduction shouldn’t be indiscriminate in terms of detail. Your goal is to enable your staff to sympathize with your clients, not to prepare them to switch fields. Focus on the human problems your clients are trying to solve, the data and tools they need to solve them, and the tangible outcomes they get when they succeed (or fail) to solve the problem.

 

Step 2: Dig Deep Into How Data Is Consumed

Once your data staff has a better understanding about the industry it serves, home in onto the parts of the daily process where data is consumed. Show them real life examples of client outputs using your data. These may be in various formats, such as pitches, reports, fact sheets, presentations, infographics, animations and so on. The key here is to show that your data, whether at a macro level or a micro level, is used to generate actual value for your clients.

You may provide the data through various mechanisms: full-fledged client-side applications and web-based interfaces or API calls, among other formats. Your data staff should be able to use these mechanisms the same way your clients do. If they don’t, Step Two is where you teach them how to do so. They should learn how to set up on their computers, how to input search parameters and how to download and ingest the data generated. Ultimately, your staff should reach a point where they can generate the same sets of data used in the pitches, reports and presentations that you have shown them, even if they are not precisely replicated as the data has evolved since the snapshots were taken.

The key here is to link the raw data produced by your staff to meaningful outputs and highlight how these outputs advance your clients’ business missions. This reinforces the notion that the work, as obscure as it might have seemed at first, has a real purpose.

 

Step 3: Guide them into the shoes of your clients

By now, your staff has become familiar with a typical day in your clients’ operations and how the data your staff produces is consumed. It’s time for them to put this knowledge to work. Set up a fictional but credible scenario and design tasks where they have to use the data they produce as if they’re clients.

M&A bankers need to win advisory mandates from companies — you can think of them as real estate agents working on behalf of their clients to buy and sell properties — so a big part of their job is to convince prospective clients that they are the best agent for a potential deal. Once they have won the mandate, their focus shifts to persuading potential suitors to strike a deal with their client. Think of this part as real estate agents coming up with price ranges and compelling, salient features for the properties on the market.

In an in-house M&A training program that I had prepared for engineers, M&A researchers and customer success teams, I created a fictional company that had decided to put itself up for sale in an auction. This scenario led to two full role-play exercises, with plenty of guides, references and resources:

  • Exercise 1: Win the mandate to represent the company
    Program participants were split into different teams of “investment banks” that wanted to be hired to advise on a sale. The teams needed to use our data to build a pitch deck and give a compelling pitch. The teams were rated on how effectively the data was used to create convincing and creative pitches.
  • Exercise 2: Negotiate a sale with potential buyers
    Program participants were split into new teams of “investment banks,” half of which worked on behalf of the fictional target company, while the other half were working on behalf of fictional potential buyers. The sell-side and buy-side teams were pitted against each other. They needed to use our data to generate reasonable price ranges for the company and corroborate them with other talking points to strike a deal. The teams were rated on how well the data was used to come up with the negotiation slide decks rather than whether a deal was struck at the end.

The friendly, competitive nature of the role-plays had truly engaged the participants; naturally, people wanted to come out victorious. But at the end of the day, who won the advisory mandate or which bank struck a better deal wasn’t the goal. The objective was to cultivate a deeper appreciation of the tools of the trade: the data, the work that our engineers and researchers did day-in day-out and that our customer success teams spend most of their working hours explaining, and the clients’ needs. That was the most prized outcome. 

Going through a simulation of solving real world problems humanizes the data that you produce, making it much more intuitive to relate to. There was no denying that graduates of the program had become more in-tune to client requirements and engaged to perform their jobs.

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Get Theatrical

People are motivated by autonomy, mastery and purpose. Since data is virtual by its very nature, it is sometimes hard for employees at data providers to find a meaningful purpose for their work. It’s even harder for employers to convey this through traditional training and lectures. Despite taking more effort to prepare and execute, role-playing can deliver much better results than other formats.

Through thoughtful, well-paced introduction of the clients’ industries, profit drivers, day-to-day work process, and meaningful role-play exercises, your engineers, data professionals and customer service teams gain much-needed human insight to clients’ evolving needs. This allows them to produce better data and products, generating better value for clients.

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