What to expect when engaging with Neal Analytics

What to expect when engaging with Neal Analytics

I’m sure our marketing department would be thrilled if I waxed poetic about how impressive our talent is, or how we are leaders in innovation so expect to get your minds blown by our advanced capabilities. But I’m not going to talk about that. Nor am I going to talk about obvious project management items like how we hold a “Sprint 0” to populate user stories so we can hit the ground running on day one. You can expect those things, but I’d like to focus on explaining a bit more about Neal Analytics’ culture and approach… and where it comes from.

We are laser focused on driving business value

We believe that the best outcomes start by working back from the capabilities the business wants to build. We want to be engaged with the business as early as possible, formulating strategies and roadmaps from prioritized needs and use cases. These drive the design patterns for the data, technology, and operational improvements we implement. This feeds into one of the most differentiated aspects of Neal — our “just enough” management consulting approach. We rapidly build a path forward and, unlike pure management consulting outfits, we can execute what’s on that roadmap from an implementation perspective.

When we begin an engagement, be it strategy and design, a technical POC, or production development, we typically push to spend time with the business understanding their needs… in addition to the typical technical topics. Assessing architectures, evaluating data, benchmarking internal resource capability are all included, of course, but we work to unify IT and the business by aligning both teams to a common roadmap. This allows the Neal team to understand where we can add the most value — by addressing the biggest hinderances to growth and modernization.

We provide engagement flexibility

neal engagement models overview

We take a consultative approach to engaging with our customers. We work with large and small enterprises, so we offer a variety of low friction and more robust ways to engage. These include defined scope projects, managed capacity teams, and role-based staffing. Some customers have mature analytics organizations and simply need some of the specialized roles or capabilities we can offer, while others will gain substantial value from Neal Analytics’ thought leadership and design for adoption.

I often compare this to who is in the driver’s seat. Neal Analytics is at home in the driver’s seat, helping navigate the many technology and architecture options out there to find the right solution, but that doesn’t mean we don’t bring our customers along for the ride. Rather than make our customers dependent on us, we always seek to drive internal capability. We will support customers as long as needed, but we prefer to help them solve the next problem on the roadmap and drive even more value.

In the passenger seat, we can support you with the decades of industry experience and specialized knowledge members of our team provide, while listening to your subject matter experts who know your business better than an outsider ever could. One of my all-time favorite quotes from a customer touched on this, explaining “Never before have we encountered consultants who were so deeply knowledgeable, yet actually listened and built upon our needs. Normally they come in and profess to have all the answers, which is off-putting and never drove success the way you have.” That desire to create value and have an impact by sharing an outside perspective is very strong, but it should never be followed at the expense of collaboration.

Regardless of how we engage, you can be confident that Neal Analytics will provide first class talent alongside our industry leading playbooks and thought leadership.

We think beyond the solution

In the about seven years I’ve spent implementing ML and AI solutions with Neal, the technology has advanced at an incredible pace. However, what is more impressive to me is the number of things we’ve realized we (and everyone else) missed during the nascent years of the cloud and AI. Back in 2014, we would declare success as having trained a solid model and provided the customer an API to call it… along with a comprehensive deck summarizing our findings, of course. MLOps wasn’t a thing. Cloud Engineering was hardly a thing, or at least it wasn’t commonly recognized as distinct from Data Engineering. The data science revolution has identified key gaps in operating models and governance strategies.

Over many years, we’ve learned from the many hard-fought efforts to get our customers’ solutions into production and generating the value they are intended to make. It may not seem relevant to the immediate topic of how to build the forecasting model, but know that when we focus on how that forecast is going to change the workflow of various user groups, it has an important meaning. This extends to a variety of topics, such as needing to know what underlying datasets/tools need improvement, or whether the skills exist internally to maintain the ML model pipeline. Some of these topics may seem like they can be addressed later, but we know that it’s never too early to address some of these other keys to success.

Your success is our business

Neal Analytics is invested in your success. We have built our brand on succeeding in situations where other firms fail to deliver. We will do everything we can to ensure it happens — before, during, and after an engagement. If we don’t, find me, because I’ll make sure we make it right.


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