While historically management consulting firms have viewed a highly talented workforce as their key asset, the emergence of data technologies has prompted them to turn to the productization of their offerings. According to “Killing Strategy: The Disruption Of Management Consulting” report by CB Insights, one of the main reasons for the disruption of the management consulting industry is the increasing pace of digitalization, and in particular, the expansion of Artificial Intelligence and Big Data capabilities. Incumbents in the consulting world are recognizing competitive pressure coming from smaller industry players, which leverage modern data analytics and visualization technologies to deliver value faster. At the same time, clients of major consulting companies are investing in software systems to collect and analyze data, aiming to empower their managers with data-driven decision-making tools.
While there’s an ongoing debate about whether these trends truly represent a disruption for the management consulting industry, it is nonetheless clear that technology will change the approaches and tools that consultants use to serve their clients. Over the last decade, major consultancies, such as McKinsey, Bain, and BCG, have invested in developing their own data engineering, analytics, and BI capabilities, in some cases productizing their IP into stand-alone software assets.
On the operation level, many consulting firms are moving away from traditional data manipulation and presentation tools (Microsoft Excel and PowerPoint) in favor of technology-enabled processes and modern data platforms—Domo, Tableau, Qlik, Microsoft Power BI, etc. While modernization of tooling, combined with growing efforts to build reusable assets, allows consulting teams to improve margins and deliver higher quality client service, it also requires vigilance and orchestrated efforts across the organization.
In this article, I will discuss the most common challenges that arise when consulting teams partner with product engineering teams (in-house or outsourced) to build data analytics and visualization tools.
Building data-centric consulting offerings – how to avoid common pitfalls
Start with the end-user. First and foremost, in productizing their data offerings, consulting teams must understand the fundamental difference between data dashboards (self-service) and data stories (curated narratives). Dashboards visualize specific metrics, allowing the users to explore the datasets and discover valuable insights. Dashboards can be built for internal users (e.g. analysts who work for the consulting company) or external users (clients of the consulting companies), assuming that those users are properly trained to interact with data.
Conversely, the purpose of data storytelling tools is to explain the data insights using annotations and narrative structure. In the context of a consulting company, a partner or engagement manager will use a data story to explain selected insights to their client, leading to actionable recommendations. According to a Forbes article by Brent Dyke, an expert on data visualization, a dashboard will either frame the information so potential insights can emerge or tell a story to explain a specific insight, but it can hardly serve both masters.
One of the fundamental questions the teams should discuss early on is the user persona—the engagement leader or the client himself. In determining the right framing of the problem, they can choose to build a data dashboard that clients can directly access for exploratory purposes, or build a tool to facilitate the creation of linear data stories for partners.
Avoid replicating charts. In most cases, consulting teams have legacy solutions, such as PowerPoint templates, which they’ve been using for their analysis for many years. Therefore, when they approach software developers to discuss technology enablement of the data offering, they expect to be able to replicate their existing visualizations as part of “replatforming”. This approach has two major disadvantages. First, it may not be feasible to mirror the legacy PowerPoint charts with one-to-one fidelity in the new BI environment (for example, Tableau or Domo); even if the resulting visual is close enough, it can become brittle due to excessive customization. Second, it leads to a missed opportunity to fully leverage the capabilities that the new environment offers (e.g. interactive drill downs). I recommend that product teams work with their consulting counterparts to expand their thinking and explore the “art of possible” so that both sides truly embrace the opportunity to build compelling visualizations.
Redesign the data model. Similar problems can arise when development teams assume that they can replicate the data structures and dictionaries from their legacy tools (e.g. Excel sheets). In reality, consulting teams often consist of generalists without extensive technical backgrounds, which makes their data models highly prone to workarounds (e.g. hardcoded parameters). Furthermore, consultants may not be privy to the data modeling best practices, for example, reducing redundancy and enforcing naming consistency. Therefore, it is highly recommended to use a clean slate approach in data modeling by taking the time to understand the key entities and relationships between them, and then redesigning the schema based on that understanding.
Consider the human factor. With the adoption of modern data visualization platforms, it’s interesting to understand the importance of the traditional “hardcopy” presentation medium. In the consulting world, presentations or “decks” are the default form of output, and most clients expect to receive a PowerPoint file, seeing it almost as an embodiment of the services they paid for. Product teams tasked with technology enablement need to be aware of this phenomenon when aiming to move consultants and clients away from static presentations towards interactive on-screen client experiences. While it may seem to be a no-brainer for a technology professional to discard PowerPoint if all charts can be accessed through a web-based application, organizational inertia that can hinder this transition should not be underestimated.
Align on the development process. Finally, consulting teams are not always aware of modern software development and design practices, which may lead to tensions. Management consulting teams traditionally operate in a more “waterfall” fashion—they define what’s called a “Day 1 Hypothesis”, collect evidence and develop insights, and then present final recommendations to their client. In the technology world, product development is much less linear—tech teams iterate in short cycles, as they learn new information. Therefore, product teams need to align early on with consulting teams on their development approach. In some cases, technologists may even need to educate their counterparts on the benefits of working in the Agile fashion, iterating and embracing learning.
Data assets can help consulting companies survive digital disruption
The management consulting industry has primarily relied on human capital and traditional data tools to develop high-value recommendations. By productizing their IP and embracing modern data technologies, consultancies can create new differentiators for their client service, while also reducing costs and improving margins. In order for consulting companies to successfully transform their operating models in the face of digital disruptions, they need to invest in building their own product innovation and development. Part of this digital transformation is the modernization of the tooling—while manual effort using PowerPoint and Excel has been at the core of the consultancy model for decades, it’s unlikely that it will win the battle with modern interactive data solutions. The leadership of consulting firms should be prepared to conquer organizational inertia and introduce a mindset shift, for empowering and educating consulting teams will be one of the critical success factors.
Kirill Osipenko is a Product Leader at Google. He has 10 years of experience in Product Management, specializing in data analytics and visualization software. Previously, he has built successful enterprise data products for global consulting companies like Accenture and McKinsey & Company. Kirill is also a passionate career coach and mentor – he offers his expertise to other product managers to help them navigate their careers in the technology industry.