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2 Future of Big Data in Management Consulting

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2.2  Future of Big Data in Management Consulting



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precise customer profiles in a matter of hours [53]. This may benefit both the consultants and the clients.

2. Technology-assisted consultancy models enable modular and standardized fee-­

for-­service offerings, providing clients with better control, faster delivery, and

lower cost

From the perspective of the clients, maybe the most attractive aspect of integrating big data into management consulting is the potential to solve a popular dilemma

with consultants: that their traditional judgment-based interventions are difficult to

control and cannot be reproduced because there is no definite standard [4]. In contrast, data science projects may be standardized, reproduced, and hence better controlled [56].

A management consultancy model assisted with advanced analytics technologies

could become modular [4]. This model would be analogous to what software packages represent in the computer science industry: they offer a finite set of clearly

defined and repeatable “sub-routines”. Instead of paying for integrated solutions,

including features that clients might not want, clients could supervise the problem-­

solving process, prevent consultants from reinventing the wheel with each successive assignment, and instead contract them for a specific analytics module, a specific

link in the value chain [4]. For each intervention, the time for delivery and cost

would represent only a fraction of expenditures associated with fully integrated

projects. This would effectively transform the value proposition of management

consultants from a fee-for-service model to a more flexible pay-for-output model.

This wave of commoditization would not benefit the management consultants.

Even from the perspective of the clients, given the need to strike a balance between

“reinventing the wheel” and “one size fits all”, it remains to be seen whether and

how much clients would benefit from such a disruptive innovation.

3. Big data does not eliminate the need for traditional consultants. Even with big

data, traditional business consultants are needed to ask the right questions

Management consultants are used to drive their hypotheses using business models, acumen and inductive reasoning. Big data now offers the possibility to drive

hypotheses and insights using real-time deductive reasoning [52], thanks to predictive analytics algorithms that may exploit patterns in big data within a few seconds.

Combining these methods brings undeniable value to corporate organizations, but

in many contexts nothing may fully replace the deep knowledge of business processes, markets and customer behaviors that consultants develop over time.

Predictive analytics may be used to identify risks and opportunities such as economic forecasts, cross-sell/up-sell targets and credit scoring. But the type of intuition that consultants develop to ask questions, pose hypotheses and drive executive

decisions is still the realm of science fiction, not existing computer programs [60,

61]. Hence, the arrival of data scientists and big data analytics does not eliminate the

need for traditional business professionals.



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2  Future of Big Data in Management Consulting



Table 2.1  Examples of data analytic providers offering business consulting services based on

software capabilities

Type of Data

General

Consulting

General

Marketing

HR Management

Frauds

Surveillance

Driving

Fitness

Health

Emotions



Example of Providers

Narrative Science, OpenIDEO, BeyondCore, Rokk3rlabs

Bluenose, Markertforce, Salesforce, Experian, Marketo, Genesys, Medallia

Zapoint, VoloMetrix, Sociometric, Cornerstone, Salesforce

Feedzai, Fico, Datameer, Lavastorm,

ADT, Frontpoint, Lifeshield, Monitronics

Zendrive, FlightCar, Progressive’s PAYD, Metromile

Discovery, Oscar, FitBit, Jawbone, Sleepcycle, Mealsnap

Watson, Ginger.io, Sentrian, Aviva, AllLife, Kaiser Permanente, Flatiron

Motista, Luminoso, Lexalytics, Xox, Watson



4. New entrants embrace the new technology because it reduces brand-barrier to

growth

In contrast to the largest generalist management consulting firms, smaller firms

(so-called boutiques) and new entrants must specialize in niche markets. But with

standardized analytics softwares joining the toolset of management consultants,

competition based on brand reputation is becoming less pervasive [4]. Factors such

as product portfolio, technical capabilities, speed of delivery and convenience are

becoming more relevant success factors.

5. New data analytics technologies are already leveraged in many industries

The big data innovation is already underway in many industries [13, 62–66], so

management consultants will have to tag along. Potential clients process big data

either in-house or through outsourced analytics providers, which sometimes works

as a decent substitute to management consulting [66, 67]. For example, big data

softwares have been developed to increase transparency between marketing performance and ROI [68].

Start-ups and subsidiaries are emerging with the sole mission of assisting corporate organizations leverage big data to optimize their businesses [53, 69]. This represents a threat to the role of Analyst in management consulting. Examples include

assistance with structured data (e.g. how long a target market goes jogging every

week?) and assistance with unstructured data (e.g. how much a product induces

positive emotions?). Table  2.1 samples emerging organizations that recently met

success with a computer-based data analytics business model to assist their customers with gathering the type of insights that used to be delivered by management

consultants.

Large IT companies (eg. IBM, Accenture) are aspiring to become total service

providers [57, 58]. This represents a threat to management consulting companies that



2.2  Future of Big Data in Management Consulting



21



do not internalize the new technologies. IBM is increasing investments in its “Global

Business Services” and IBM Watson Cognitive System (launching “Watson-Health”

in 2015), HP developed its “Business Service Management”, and Accenture developed its own “Business Services” too. According to S&P Capital, management consulting in the IT industry will grow at an average CAGR of 7% [58].

The mirror phenomenon is taking place in management consulting firms: they

are revisiting their service portfolio to assist clients with software development.

McKinsey developed its “Solutions”, Booz Allen Hamilton its “NextGen Analytics”

and BCG is rapidly expanding “BCG Gamma”. This is a potential threat to management consulting firms that do not internalize the new capabilities.



2.2.2 Factors that Refrain the Transition

1. In some projects the nature of the data does not benefit from computer

capabilities

Management consultants often get essential insights based on just a few interviews inside/outside the organization, by looking at easy-to-digest financial records,

etc. In these frequent cases, the data is not large and “big data” does not apply in the

first place [60, 61, 70].

2. Traditional management consulting revolves around executive decisions also

when it pertains to data analysis. It prioritizes low volume, high quality, easy-to-­

digest data

Management consultants drive executive decisions which are directional in

nature. That includes all the steps before, during and after the data analytics activities. They are in charge of asking smart questions, navigating analytics tools, exploring data, interpreting data, building action plans, and increasingly helping implement

these plans. As long as the consultant is involved in driving executive decisions, he/

she will continue to follow the 80/20 rule and prioritize low volume, high quality,

easy-to-digest data. Thus even when big data is available, the consultant might defer

its use whenever a faster route to deliver insights is available.

In 10  years, the management consulting industry might have transitioned to a

place where many clients redirect the consultant toward specific analytic tools. In

this scenario the client would be doing a job currently held by the consultant. This

would indicate that a disruption has taken place in the form of modularization and

even commoditization. In contrast, if in 10 years data science has matured but using

its software still requires a highly technical expertise that most clients cannot

insource, then the currently emerging business models that blend core judgment

based capabilities with technical capabilities such as McKinsey’s Solutions and

IBM’s Global Business Services will have effectively disrupted the industry.

Regardless, a disruption is underway…



2  Future of Big Data in Management Consulting



22



3. Internalizing highly technical tools that cannibalize traditional market research

can create cultural dissonance

Large global and generalist management consulting firms leverage their premier

brand reputation (cachet) for contracting large clients on their most strategic, executive cases [71]. In contrast to smaller players and new entrants, larger organizations

cannot fully embrace big data integration because it commoditizes their business,

dilutes their focus, and weakens their brand.



2.3



So What: A Scenario Analysis



In this section, the future of technology-assisted management consulting is articulated using a scenario planning analysis. The scenario planning approach is concisely introduced in Chap. 3 (Sect. 3.1). More details can be found in Refs.

[72, 73].

The Key Focal Issue

How will new data analytics and artificial intelligence technologies impact the business model of management consultants?

Driving Forces

In Table 2.2, some key factors and environmental forces are brainstormed and listed.

This list does not aim to be exhaustive but rather to represent pointers toward forces

that have potential to impact the key focal issue. This is standard procedure with the

scenario planning framework of Garvin and Levesque [73].

Critical Uncertainties and Scenarios Creation

The driving forces listed in Table 2.2 may be condensed in two broad variables that

I will refer to as External Push (from clients or consumers) and the future of

Partnerships:



Modular Solutions

Outsourcing



External Push

Partnerships



Integrated Solutions

Insourcing



A 2 × 2 matrix may now be constructed with four possible scenarios:



23



2.3  So What: A Scenario Analysis

Table 2.2  Key factors and

environmental forces

(un-ranked). This list of 13

forces results from simple

brainstorming and is thus

non-exhaustive, but the goal

of scenario planning at this

stage is not to be exhaustive

nor precise, it is to list a

broad set of pointers toward

forces that may potentially

impact the key focal issue



Driving Force

1. Client and end-customer push

2. Improvement in analytics software

capabilities

3. Level of technical expertise required

4. Evolution of big data-enabled

businesses

5. Modularization of consulting

interventions

6. Budget reductions

7. Skepticism for traditional

judgment-based analytics

8. Skepticism for commoditization

and automation

9. Profitability of the McKinsey

Solutions platform

10. Future of the IBM’s Watson AI

platform

11. Future of niche providers such as

Kaiser Permanente

12. Future of portable sensor devices

13. Regulations (confidentiality,

anti-discrimination...)



Level of Certainty

Uncertain

Predetermined

Uncertain

Uncertain

Uncertain

Uncertain

Predetermined

Predetermined

Uncertain

Uncertain

Uncertain

Uncertain

Uncertain



Modular Solutions



A-La-Carte



The Social

Network



Insourcing



Outsourcing



High-Tech



2017 High-End



Revolution



Model



Integrated Solutions



Narratives and Implications

For each of the four scenarios above, let us weave in defining characteristics and

implications based on the insights gathered in Sect. 2.2 and the driving forces listed

in Table 2.2.



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