Choosing the right engagement model for your data initiative
- Data-driven business transformation initiatives, i.e., digital transformation ones, require specific skill sets that are hard to find
- These skill sets, and therefore the associated profiles, evolve throughout implementation; and they may not always be necessary long term
- Leveraging external resources is often the fastest, most cost-effective, and less risky way to go
- How these resources are managed and integrated, temporarily or permanently, in your existing team is also an essential element
In 2011, Neal Analytics developed a leading innovative analytics delivery approach backed by highly skilled experts that successfully helps companies in various industries convert data and servers into visible and usable business results. Neal Analytics calls this flagship engagement model approach: the Managed Capacity model. It enables Neal Analytics to meet its customers’ needs with higher speed and efficiency.
How to organize the perfect team for a data analytics project?
A data analytics initiative requires a broad set of skills that evolves through the project and with a team size that can vary quite significantly depending on the project.
The final piece to fit these components together is an organizational model to support the transformation goals associated with analytics.
Some organizations wish to create internal centers of excellence. Others want to only engage in fixed-term projects, while others want to outsource the capabilities completely.
To help execute the complex needs of data-driven initiatives, Neal Analytics offers a portfolio of engagement models. These engagement models have their own unique set of variables and benefits that allow Neal Analytics to fit its services best into its customer environments. These models will adapt to any customer’s needs and business philosophy. They can also be mixed and matched depending on where a customer is on their data journey.
For more mature customers scaling an existing team through staff augmentation is often the quickest way to engage. For customers who do not want to engage directly with or manage resources within this software development lifecycle, project-based work is often the best fit. For customers who wish to outsource or want a capable team able to quickly and seamlessly plug into a broad analytics initiative, a managed capacity approach is often best.
Staff Augmentation or Role-Based Staffing
Neal Analytics offers the traditional staff augmentation model. Our expert consultants can seamlessly integrate into existing teams to complement or augment their skill set on a temporary or permanent basis.
For instance, a company may need data science help to complement its existing team during a temporary heavy workload. It could then decide to add to its team one or more data scientists. After a few months, it can go back to its business-as-usual mode by reducing or even entirely stopping using these external resources.
With teams in the United States in our Bellevue, WA headquarters and our Pune, India office, Neal Analytics can offer a range of staff augmentation options: onsite, offsite, or a blend of both.
This engagement model tends to work best with mature customers who have a defined organizational model into which Neal Analytics adds complementary resources and well-defined business analyst initiatives where its resources will add value.
Neal Analytics also offers project-based work. Defined through a statement of work (SoW), which encapsulates deliverables and timelines, a team expertly staffed for a project will then be responsible for the project’s delivery.
Project-based work is often part of a functional portfolio management initiative for a customer’s digital transformation efforts. Customers interested in building their team but unsure of how to start choose this option to see an already functional team in action.
Unfortunately, the disadvantage is that it’s the costliest one on a per-resource basis. The upside is that the vendor signs up to fixed deliverables over an estimate.
In our experience, as customers mature, they usually opt for staff augmentation or managed capacity as a follow-up to initial project-based work.
Neal Analytics also offers its Managed Capacity model. It provides customers access to the optimal mix of skillsets throughout the engagement duration.
It quickly allows the continuous and iterative definition of customers’ priorities, creating a regularly updated living document that describes work in progress, targets for a given initiative, and the appropriate team scaling as the project evolves.
This model brings the best of traditional staffing and project approaches into a predictable, reliable, flexible, and scalable framework.
This model also leverages a modern engineering approach and a flexible team composition. It is often the preferred engagement model for large or complex projects.
Three core principles are the bases for this type of engagement:
- A fixed but adaptable monthly retainer
- Blended engagement teams which composition adapts to the project needs
- An Agile (aka “modern”) software engineering approach using two-week sprints to deliver predictable results
These engagements can be designed to be permanent, ongoing, outsourced arrangements where the Neal Analytics team will operate a data-driven business process on behalf of the customer.
The client will either define an appropriate monthly project retainer based on scope and timelines constraints or calibrate its requirement (both timeline and scope) to fit a pre-defined but adaptable monthly retainer. By choosing this approach, the client will be able to keep its expenses predictable and, if for whatever reason the project scope increases or decreases, it will adapt this retainer on a month-to-month basis.
Adaptive Team Composition
Within this monthly budget, Neal Analytics will put together the optimal project team, based on the goals and project stage (as defined in the steps 1 to 6 in this blog post).
This model is very flexible as the client doesn’t need to staff full-time specialists. Depending on the project, as little as 10% of a specialist’s time can be dedicated to it.
Conversely, clients can add as many full-time specialists as necessary on their project. In total, a managed capacity team can be as small as three people equivalent and as large as tens of specialists.
However, for large projects and to abide by Agile best practices, these teams may be split into semi-independent scrum-level teams of more manageable sizes, i.e., about 7 people, aka “the two pizzas team.”
Modern software engineering – Agile methodology and best practice reuse
A common best practice, the Agile methodology fits well with the complexity, ever-evolving goals, and speed of typical data analytics, AI, or ML projects.
Focusing on short development “sprints” will allow the managed service team to focus on the most critical and urgent tasks at hand across the six steps depicted above. For each sprint, the customer and Neal Analytics project team will align on the next sprint deliveries, whether they are new features, bug fixes, or code improvements of all sorts, such as speed, size, or reliability.
After each sprint, the team will revisit the deliverables, plan for future team composition modifications if needed, and plan the next sprint. This ongoing cadence lets the Neal Analytics team plan for future work and build a practical roadmap with clients to build towards mutual success.
As the project reaches completion, an ongoing project retainer, also using two-week sprints, can ensure the project is maintained, improved, and adapted as data and business conditions change.
Throughout these engagements, the Managed Capacity team will operate using standard best-practice patterns, templates, processes, and solutions Neal Analytics has already implemented multiple times for other customers. It will help ensure both output quality and engagement efficiency.
The managed service model is the classical outsourcing model. It enables a customer to outsource an entire business process and rely on a supplier, such as Neal Analytics, to operate the process without the need to invest internal resources. It can scale as needed and is often more cost-effective for well-defined processes not core to the business.
These engagements can also be combined, as required, in a Build-Operate-Transfer model. With this mixed model, the Neal Analytics team will morph its engagement based on the project’s stage and customer needs.
First, a skilled Neal Analytics team will build the initial solution end-to-end (project-based or managed capacity). Then, Neal Analytics will operate the solution during its initial production stages (managed service).
Finally, the team that built and operated the solution will train the customer’s organization and transition the day-to-day operations to its in-house team, supplying it with staffing resources as needed (staff augmentation), ensuring a smooth and seamless transition.
Analytics projects are seldom as straightforward as initially expected, and internally staffing the appropriate team to support them can be challenging when no existing large scale is present.
Several options are possible depending on the goals, timeline, and budgetary and available skill set constraints.
Neal Analytics offers a flexible approach to these challenges, building on years of experience in delivering projects from business analysis to data estate modernization, analytics, machine learning, and AI. Customers can purchase these engagements via Microsoft Appsource, or by contacting Neal Analytics.
For more on how to staff a data analytics project check out our free ebook: “How to staff your data analytics team.”
This article has been updated and was originally published on 12/5/2019.