What is digital consulting?

What is digital consulting?

In one sentence, digital consulting is a focused form of management consulting designed to enable firms to better leverage their data and technology and create business value. Digital consulting seeks to inform and enable decision makers so they can navigate the rapidly evolving technology landscape and create a digital strategy that generates immediate and long-term value.

Whether you are a leader evaluating potential engagements, or a consultant seeking to specialize your skillset, there are a few key aspects of digital consulting that are worth understanding better:

Table of contents:

    1. Digital consulting vs. Management consulting 
    2. Rapid Envisioning Workshops 
    3. Data strategy 
    4. Decision-making frameworks 
    5. Architecture design 
    6. Business problem solution design 
    7. Persona/Persona journey creation 
    8. Maintaining Agility and ongoing management 

Digital consulting vs. Management consulting

Digital consulting can be considered a specialized subset of general management consulting where tech and data are the focus of strategy development, but that’s an oversimplification. Management consulting always requires some level of domain expertise, which is no different when it comes to data and technology, but there are some more foundational differences in the approach to make it more lean and agile- necessitated by the rapid pace of the space. For these reasons, the pace of traditional management consulting can often allow organizations to fall behind the innovation curve, rendering many strategies moot.

Rapid Envisioning Workshops 

The first step in building a digital (data, technology, and operations) strategy is understanding the business’s needs and goals. To facilitate this quickly, it is best to get the many stakeholders and decision makers across the business to get in a room with the IT and data teams so they can align on a shared understanding of current challenges and opportunities. (Far too many organizations only support limited collaboration between these two “sides of the house” but that is a topic for another day.) These workshops can be anywhere from two days to a week, depending on the size and complexity of the organization, but all share the same tenets.

What does a rapid envisioning workshop include?

    • Review of the business’s current state
    • Understand the state of the industry and leading innovations
    • Inventory of the current data/technology capabilities
    • Identification of top challenges/opportunities (SWOT)
    • Refining the challenges/opportunities into business scenarios/use case definitions
    • Scenario evaluation and prioritization

Digital envisioning workshop

Following the workshop, there are a number of activities that build on the initial direction from the workshop, such as persona journey development for the priority use cases and designing a technology/data foundation that meets the requirements. This is typically a 4 to12-week effort at most.

Again, digital consulting is in effect a “lightweight” strategy consulting effort that runs faster than the common 1+ year traditional approaches where things are thought through at an extremely deep level. If you are familiar with project management terminology, think of it like operating in Agile vs Waterfall — you may not have a perfect definition of what is required and what the outcomes will be, but you have enough to get started and things rapidly evolve so flexibility is key.

Data strategy

A data strategy is the core outcome of digital consulting work. The strategy is often best defined as a series of roadmaps that are built upon many individual analyses where the consultants evaluate various aspects of the business, technology, and operational needs.

Example: A roadmap to create more personalized customer experiences

Here is an example: The top priority business scenario for a retailer is more personalized customer experiences, and ideally the business wants it slotted early on the roadmap. However, that need must be reconciled against the reality within the data and technology roadmap. Despite prioritizing supporting this scenario, IT needs to build a unified Customer Data Platform that brings together the data from the many solutions the marketing and sales operations teams have purchased over the years. Further, simply having finer grain customer segmentation (or “segment of one” true personalization) data does not mean that the marketing team itself can immediately scale their activities to properly leverage it. As a result, any immediate effort to build a personalization analytics solution will likely stumble over limited data or ability to operationalize.

This is where the “strategy” comes in. There may be other lower ranking scenarios that can generate value in the interim as they only require a subset of the data, or the personalization use case can be broken down into more achievable capability milestones so the business can get something usable in their hands sooner. Reconciling each of these needs and roadmaps creates a cohesive plan that the many stakeholders across different lines of business are more likely to accept and ensures that the IT and analytics departments have a reasonable path to nirvana.

Decision-making frameworks

There are many decision-making frameworks leveraged in Digital Consulting that come from “vanilla” Management Consulting:

SWOT analysis

The SWOT analysis is still useful in digital consulting if done in a rapid/lightweight manner- top of mind answers are more than sufficient.

SWOT analysis - digital consulting

Priority map

The 2×2 matrix serves as a great way to prioritize scenarios:

Priority map using value and feasibility

We use two simple dimensions: Value, and Feasibility. The top right quadrant delivers the “low hanging fruit” scenarios that often become the top priorities to develop.

Additional dimensions when working with data, AI, and IoT

The matrices above are common knowledge, but what about frameworks unique to digital consulting? These are often tailor-made for each case, so instead of sharing examples that won’t generalize, I’ll speak to some of the more unique dimensions encountered when working with data, AI, and IoT.

    1. Scope of Impact: Number of end users impacted, as well as their influence(seniority)  
    2. Tech Debt: The cost of rework that a solution will have on future development effort 
    3. Operational Load: Governance, monitoring, management, and support requirements 
    4. Personnel Capability: The innovative nature of digital transformation often highlights significant skill gaps 
    5. Predictive Confidence: The use of advanced analytics to solve business problems often results in conversations around whether performance is “good enough” or meets a minimum standard for robustness and quality. 

These are then assigned ratings from 1-5 and can be leveraged to further differentiate options within more nuanced decisioning frameworks. Example: Governing dozens of analytics models/solutions to ensure that the business is still able to trust the numbers being presented to them

Generally, if there’s a decision to be made, it’s best to put together some form of framework so it can be done consistently, whether it’s simple or complex. If there needs to be a 30-item checklist to certify something for production, great, let’s make sure the process around it works well enough that it gets followed! Implementing frameworks accelerates decisioning by making it repeatable and scalable, which is essential at the pace digital consulting runs at.

Architecture design

It bears saying that when you’re working to help guide a business through their digital transformation efforts, there must always be a few architectures involved. The specifics will always change so it’s better to focus on what purposes they serve.

    1. Logical/Conceptual architecture: Inform stakeholders on how the data will flow and be organized 
    2. Current/Transition/Future state architectures: Technical diagrams where the actual tools/services/products are assigned to each stage outlined in the conceptual architecture. 

Building these is an essential part of generating the technical roadmap, since the many foundational engineering tasks (migration, unification, etc.) that don’t generate a direct business capability or solution will require significant efforts to complete. Technical roadmaps span years, not months, so it is important to identify transition solutions that can provide enough capability for development to begin on some of the business solutions.

Business problem solution design

Scenario canvas example - Demand forecasting

Business scenarios need a high-level definition that answers “What, Why, and How?” The solution will deliver the capabilities the business is seeking to build. By receiving sign-off on a charter such as this, the IT and analytics teams can ensure they have a clear path forward.

Creating these isn’t trivial and requires research into the problem space, the data, and working iteratively with the business to ensure their needs are met as much as possible. As such, these should be reserved for the top scenarios earmarked for development in the next 3-6 months.

Persona/Persona journey creation

Persona overview chart

Following up on the scenario definition effort, far too many development efforts focus on what to build without considering who is going to use them and how it is going to fit in to or change their process. Collaborating with the end user groups (personas) to ensure what is being built meets their expectations is a prerequisite for success. Focusing development efforts around this will reduce wasted cycles later and is worth the bit of up-front effort. For each persona, it is worth building a view of their journey from the current state to the desired state to articulate back to the stakeholder how their experience is likely to evolve.  

Maintaining agility and ongoing management

Digital consulting efforts are frontloaded to the initial stages of a modernization journey but persist at a light level to ensure that the momentum that it generates does not veer off from the targets set on the roadmap. These efforts are the glue that keeps the business appraised of the modernization status and helps stakeholders observe the value that they bring even before capabilities and solutions begin production deployment.

I will close with a point that may not be obvious: A data strategy needs to be dynamic and regularly updated to stay valuable. This can have a reasonable cadence — annual, semiannual, or quarterly are the most common depending on the industry. Once the initial workshop efforts are complete, the same stakeholders should meet to review their rankings and make additions and/or changes as the business’s priorities shift. If something urgent comes up, the roadmaps can pivot to address, but the point is to have a forward leaning stance and communicate the direction out, rather than be reactive.


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