How we work

Strategy Projects

These are typical consulting projects that can last for 2-6 months more depending on the complexity and nature of the business. We embark in an in-depth review with the leadership, identify areas of growth, value creation and most importantly, help them make sure they are getting the strategy decisions right. To the traditional analysis, special attention is paid to understanding how the strategy is leveraging digital and emerging technologies whether for Marketing or Operations.

Supporting the Journey

Once the strategy is in place, some clients request some form of ongoing support during the initial phases of implementation. A small team accompanies the leader in making initial decisions and build momentum.

In Data Science engagements, clients request that we do the development, follow up and updates, while they deploy and apply the analytical tools on a day-to-day basis. If necessary, we support our clients in the hiring and training of their talent, embedding them in our team so they can later lead the analytical effort at the firm.

Data Science competencies and applications share similarities with software development and implementation in the sense that they require updates, upgrades, new developments, redesigning processes and training. A two-lane or speed approach is often followed so that day-to-day needs are taken care of. A cost-benefit analysis will determine whether the scale, complexity and need make it worthwhile to perform these tasks in house.

Scoping and brainstorming workshops

In these settings, we aim for a more open-ended discussion with the leadership, typically off-site. The purpose of these workshops is often more narrowly defined, tends to be exploratory in nature or we facilitate an in-depth discussion, build consensus around a critical decision with the leadership team.

We often apply this format with clients that already have a strategy in place and need a quick review, explore how to launch their analytics journey or understand how analytics can boost their strategy and competitiveness. In many cases organizations that have already made significant investments in AI but are seeking to explore ways of accelerating their development or address organizational design issues that emerge as they progress in their analytics journey.