6.1 First Principles

The key component of data analysis and quantitative consulting is the ability to apply quantitative methods to business problems in order to obtain actionable insight. But it is impossible for any given individual to have expertise in every field of mathematics, statistics, and computer science. In our experience, the best consulting output is achieved when a small team of consultants possesses expertise in 2 or 3 areas, a decent understanding of related disciplines, and a passing knowledge in a variety of other domains.

This includes keeping up with trends, implementing knowledge redundancies on the team, being conversant in non-expertise areas, and knowing where to find information (online, in books, or external resources).

In future modules, we will present an overview of a variety of “domains” related to quantitative analysis:
  • survey sampling and data collection;

  • data processing;

  • data visualization;

  • statistical methods;

  • queueing models;

  • machine learning;

  • simulations;

  • optimization;

  • Bayesian data analysis;

  • anomaly detection and outlier analysis;

  • feature selection and dimensions reduction, and

  • trend extraction and forecasting;

  • etc.

 
Strictly speaking, the domains are not free of overlaps. Large swaths of data science and time series analysis methods are quite simply statistical in nature, for instance, and it is not unusual to view optimization and queueing methods as sub-disciplines of operations research.

By design, our treatment of these topics will be brief and incomplete. Each module is directed at learners who have a background in quantitative methods, but not necessarily in the topic under consideration.

Our goal is to provide a quick “reference map” of the topic, together with a general idea of common challenges and traps, in order to highlight opportunities for application in a consulting context.

These modules are not always meant to be comprehensive surveys: they often focus solely on basics and talking points. Perhaps more importantly, a copious number of references are also provided.

We will complement some of these topics with write-ups of real-world consulting projects. For the time being, however, we focus on the non-technical aspects of quantitative work. Note that these are not just bells and whistles; analysts that neglect them will see their projects fail, no matter how cleverly their analyses were conducted.

This module is a companion piece to Data Science Basics; the latter contains a fair amount of must-read material for would-be data scientists and consultants, including:

  • objects, attributes, and datasets;

  • modeling strategies and information gathering;

  • ethics in the data science context;

  • the “analytical” workflow;

  • roles and responsibilities of data analysis teams, and

  • asking the right questions.

In the rest of this section the terms consultants, data scientists, and data analysts are used interchangeably, as are the terms clients and stakeholders.66

6.1.1 The Consulting/Analysis Framework

The perfect consultant/data scientist is both reliable and extremely skilled; in a pinch, it’s much better to be merely good and reliable than great but flaky. [Bronwyn Rayfield]

Consulting is the practice of providing expertise to an individual or organization in exchange for a fee.

Consultants may be hired to supplement existing staff (importantly, they are NOT hired as employees – consultants enjoy an at-arm’s-length relationship with their client) or to provide an external perspective. Consulting duties could include some of the following:

  • making recommendations to improve products or services;

  • implementing solutions;

  • breathing new life into a failing project;

  • training employees;

  • re-organizing a company’s structure to remove inefficiencies, etc.

At first glance, this seems fairly straightforward, but there could be complications:

  • Even though consultants are brought in by the organization, their presence is not always appreciated by employees. It is not too difficult to imagine how an outsider coming in and making recommendations to improve products and services, or to remove inefficiencies could be seen, in effect, as criticizing the current processes, let alone as potentially threatening employees’ livelihoods, causing a fair amount of friction and pushback.

  • If a consultant is brought in to implement solutions, the first question to come to mind should be: “why isn’t the company implementing the solution(s) themselves?” Is it because of a lack of resource? Are there political implications?

  • The same goes for breathing new life into a failing project: why is the project failing? Is it a failure of leadership or of planning? Is the project infeasible? Are they looking for a scapegoat?

  • In the training scenario, consultants need to recognize exactly how much can be done in the allotted time.67 Is the company hoping to offer the “illusion” of training? What kind of abilities the prospective trainees have? If they have the “right stuff”, why are they not training themselves? If they do not have the right skil sets and cannot be trained, what consequences might that have on success and/or reputation?

Generally speaking, consultants fall in one (or more) of the following types [50]:

Strategy Consultants

focus on corporate strategy, economic policy, government policy, and so on; the projects they typically conduct for for senior managers have more of an advisory nature than in implementation one;

Operations Consultants

focus on improving the performance of a company’s or a department’s operations; they typically work with both strategy and technology people (in sales, marketing, production, finance, HR, logistics, etc.), on projects that run the gamut from advisory to implementation;

Human Resources Consultants

focus on matters pertaining to human resources or on the workplace culture;

Management (Business) Consultants

focus on variety of organizational concerns (this is a catch-all term to describe strategic, operational, and HR consultants);

Financial and Analytical Advisory Consultants

focus on financial and/or analytical matters; for these consultants, subject matter expertise (tax law, risk analysis, statistics, etc.) is paramount;

Information Technology Consultants

focus on development and application of IT, data analytics, security, and so on; they typicall work on project, not on business-as-usual activities;

Specialized (Expert) Consultants

are usually brought in for a very specific task, which requires pointed expertise in a specific field.

For the purpose of this module, when we refer to quantitative consultants and/or data scientists, we usually mean someone who falls in one the last three categories, in short someone with expertise in a quantitative, analytical, technological, and/or technical field.

According to International Management Consulting, all consultants benefit from:

  • business understanding and external awareness (the so-called PESTLEE framework: political, economical, social, technological, legal, environment, ethics);

  • being able to manage client relationships;

  • implementing the EDDD consulting process (engage, develop, deliver, disengage),

  • and being familiar with various consulting tools and methods specific to their area(s) of expertise.

More specifically, good data scientists and quantitative consultants are expected to:

  • have business acumen;

  • learn how to manage projects from inception to completion, knowing that consultants are working with various people, on various projects, and that these people are also working on various projects;

  • be able to slot into various team roles, recognize when to take the lead and when to take a backseat, when to focus on building consensus and when to focus on getting the work done;

  • seek personal and professional development, which means that learning never stops;

  • always display professionalism (externally and internally), a standard a behaviour and skills that need to be adhered to – take ownership of failures, share the credit in successes, treat colleagues, clients, and stakeholders with respect, and demand respect for teammates, clients, and stakeholders as well;

  • act in accordance to their ethical system;

  • hone their analytical, predictive, and creative thinking skills;

  • rely on their emotional intelligence, as it is not sufficient to have a high IQ and recognize stated and tacit colleagues’ and clients’ needs, and

  • communicate effectively with clients, stakeholders, and colleagues, to manage projects and deliver results.

6.1.2 The “Multiple I’s” Approach to Quantitative Work

While technical and quantitative proficiency (or expertise) is of course necessary to do good quantitative work, it is not sufficient – optimal real-world solutions may not always be the optimal academic or analytical solutions. This can be a difficult pill to swallow for individuals that have spent their entire education on purely quantitative matters.68

The consultants’ and analysts’ focus should then shift to the delivery of useful analyses, obtained via the Multiple “I”s approach to data science:

  • intuition – understanding the data and the analysis context;

  • initiative – establishing an analysis plan;

  • innovation – searching for new ways to obtain results, if required;

  • insurance – trying more than one approach, even when the first approach worked;

  • interpretability – providing explainable results;

  • insights – providing actionable results;

  • integrity – staying true to the analysis objectives and results;

  • independence – developing self-learning and self-teaching skills;

  • interactions – building strong analyses through (often multi-disciplinary) teamwork;

  • interest – finding and reporting on interesting results;

  • intangibles – putting a bit of yourself in the results and deliverables, and thinking “outside the box”;

  • inquisitiveness – not simply asking the same questions over and over again.

Data scientists and consultants should not only heed the Multiple “I”s at the delivery stage of the process – they can inform every other stage leading up to it.

6.1.3 Roles and Responsibilities

A data analyst or a data scientist (in the singular) is unlikely to get meaningful results – there are simply too many moving parts to any data project.

Successful projects require teams of highly-skilled individuals who understand the data, the context, and the challenges faced by their teammates.69

Depending on the scope of the project, the team’s size could vary from a few to several dozens (or more!) – it is typically easier to manage small-ish teams (with 1-4 members, say).

A data science team in action.

Figure 6.1: A data science team in action, warts and all [Meko Deng, 2017].

Our experience as consultants and data scientists has allowed us to identify the following quantitative/data work roles.70

Project Managers / Team Leads

have to understand the process to the point of being able to recognize whether what is being done makes sense, and to provide realistic estimates of the time and effort required to complete tasks. Team leads act as interpreters between the team and the clients/stakeholders, and advocate for the team.71 They might not be involved with the day-to-day aspects of the projects but are responsible for the project deliverables.

Domain Experts / SMEs

are, quite simply, authorities in a particular area or topic. Not “authority” in the sense that their word is law, but rather, in the sense that they have a comprehensive understanding of the context of the project, either from the client/stakeholder side, or from past experience. SMEs can guide the data science team through the unexpected complications that arise from the disconnect between data science team and the people “on-the-ground”, so to speak.

Data Translators

have a good grasp on the data and the data dictionary, and help SMEs transmit the underlying context to the data science team.

Data Engineers / Database Specialists

work with clients and stakeholders to ensure that the data sources can be used down the line by the data science team. They may participate in the analyses, but do not necessarily specialize in esoteric methods and algorithms. Most data science activities require the transfer of some client data to the analysis team. In many instances, this can be as simple as sending a .csv file as an e-mail attachment. In other instances, there are numerous security and size issues that must be tackled before the team can gain access to the data.

Data Analysts

are team members who clean and process data and prepare the initial data visualizations. They have a decent understanding of quantitative methods. They typically have at most 1 area of expertise and can be relied upon to conduct preliminary analyses.

Data Scientists

are team members who work with the processed data to build sophisticated models that provide actionable insights. They have a sound understanding of algorithms and quantitative methods, and of how they can be applied to a variety of data scenarios. They typically have 2 or 3 areas of expertise and can be counted on to catch up on new material quickly.

Computer Engineers

design and build computer systems and other similar devices. They are also involved in software development, which is frequently used to deploy data science solutions.

Artificial Intelligence/Machine Learning Quality Assurance/Quality Control (AI/ML QA/QC) Specialists

design testing plans for solutions that implement AI/ML models; in particular, they should help the data science team determine whether the models are able to learn.

Communication Specialists

are team members who can communicate the actionable insights to managers, policy analysts, decision-makers and other stake holders. They participate in the analyses, but do not necessarily specialize in esoteric methods and algorithms. They should keep on top of popular accounts of quantitative results. They are often data translators, as well.

Another complication is provided by the fact that data science projects can be downright stressful. In an academic environment, the pace is significantly looser, but

  • deadlines still exist (exams, assignments, theses),

  • work can pile up (multiple courses, TAs, etc.)

In the workplace, there are two major differences:

  • a data science project can only really receive 1 of 3 “grades”: A+ (exceeded expectations), A- (met expectation), or F (didn’t meet expectations);

  • while project quality is crucial, so is timeliness – missing a deadline is just as damaging as turning in uninspired or flawed work; perfect work delivered late may cost the client a sizeable amount of money.

Sound project management and scheduling can help alleviate some of the stress related to these issues. These are the purview of project managers and team leads, as is the maintenance of the quality of team interactions, which can make or break a project:

  • treat colleagues/clients with respect AT ALL TIMES – that includes emails, Slack conversations, watercooler conversations, meetings, progress reports, etc.;

  • keep interactions cordial and friendly – you do not have to like your teammates, but you are all pulling in the same direction;

  • keep the team leader/team abreast of developments and hurdles – delays may affect the project management plan in a crucial manner (plus your colleagues might be able to offer suggestions), and

  • respond to requests and emails in a timely manner (within reason, of course).

6.1.4 Consulting/Analysis Cheatsheet

We will end this section with a 12-point TL;DR (too long; didn’t read) snippet that summarizes the profession. These were collected (sometimes rather painfully) throughout the years (see [51] for more details).

  1. Business solutions are not always academic solutions.

  2. The data and models don’t always support the stakeholder/client’s hopes, wants, and needs.

  3. Timely communication is key – with the client and stakeholders, and with your team.

  4. Data scientists need to be flexible (within reason), and willing and able to learn something new, quickly.

  5. Not every problem calls for data science methods.

  6. We should learn from both our good and our bad experiences.

  7. Manage projects and expectations.

  8. Maintain a healthy work-life balance.

  9. Respect the client, the project, the methods, and ther team.

  10. Data science is not about how smart we are; it is about how we can provide actionable insight.

  11. When what the client wants can’t be done, offer alternatives.

  12. “There ain’t no such thing as a free lunch.”

References

[50]
Consultancy UK, Types of Consultants.
[51]