The distinction between management consulting and more technical IT consulting services is becoming less and less evident, thanks to new business opportunities offered by the phenomena referred to as big data [2, 3, 13, 52–55] and data science [56–58]. Several drivers of innovation have been documented, some of which point to a potential disruption through modularization of a technology-assisted consul- tancy model [4, 49]. Whether sustaining or disruptive in nature, the threats and opportunities brought about by the democratization of information and big data technologies will impact the management consulting industry.
The literature points to at least five factors that may favor the integration of big data into management consultancy models, and at least three factors that may refrain its integration. Let us look at each of these factors.
2.2.1 Factors that Favor the Integration of Big Data in Management Consulting
1. Big data technology dramatically increases the speed and accuracy of market research
Today’s businesses continuously create new data, from sales and marketing to customer relations, production, logistics, and more [2, 13, 52, 53, 59]. In addition, there’s a huge amount of data generated by web sites, public databases, social media, etc. While traditional consulting methods may require weeks or months studying internal workflows, interviewing customers, or discussing with key per- sonnel, one can now search social media, purchase history, and draw amazingly
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precise customer profiles in a matter of hours [53]. This may benefit both the con- sultants 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 integrat- ing 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 con- trast, data science projects may be standardized, reproduced, and hence better con- trolled [56].
A management consultancy model assisted with advanced analytics technologies could become modular [4]. This model would be analogous to what software pack- ages 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 succes- sive 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 mod- els, acumen and inductive reasoning. Big data now offers the possibility to drive hypotheses and insights using real-time deductive reasoning [52], thanks to predic- tive 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 pro- cesses, markets and customer behaviors that consultants develop over time.
Predictive analytics may be used to identify risks and opportunities such as eco- nomic forecasts, cross-sell/up-sell targets and credit scoring. But the type of intu- ition 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.
2.2 Future of Big Data in Management Consulting
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 perfor- mance and ROI [68].
Start-ups and subsidiaries are emerging with the sole mission of assisting corpo- rate organizations leverage big data to optimize their businesses [53, 69]. This rep- resents 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 custom- ers 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
Table 2.1 Examples of data analytic providers offering business consulting services based on software capabilities
Type of Data Example of Providers General
Consulting
Narrative Science, OpenIDEO, BeyondCore, Rokk3rlabs General
Marketing
Bluenose, Markertforce, Salesforce, Experian, Marketo, Genesys, Medallia HR Management Zapoint, VoloMetrix, Sociometric, Cornerstone, Salesforce
Frauds Feedzai, Fico, Datameer, Lavastorm, Surveillance ADT, Frontpoint, Lifeshield, Monitronics
Driving Zendrive, FlightCar, Progressive’s PAYD, Metromile Fitness Discovery, Oscar, FitBit, Jawbone, Sleepcycle, Mealsnap
Health Watson, Ginger.io, Sentrian, Aviva, AllLife, Kaiser Permanente, Flatiron Emotions Motista, Luminoso, Lexalytics, Xox, Watson
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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 devel- oped its own “Business Services” too. According to S&P Capital, management con- sulting 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 manage- ment 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 inter- views 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 activi- ties. They are in charge of asking smart questions, navigating analytics tools, explor- ing 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…
2.2 Future of Big Data in Management Consulting
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, execu- tive 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.