Our approach aligns scale and speed-to-market.

The strategy for getting a successful big data analytics initiative off the ground is complex. It is even harder to ensure that business value will continue to be generated from a project. From insight, to action, to value—your big data initiative must address all three of these aspects in order to deliver a true competitive advantage.

The core of our approach is focused on co-creation alongside our clients: working together to identify data opportunity within the organization, selecting strategic and operational rationale for big data analytics, and formulating a framework for structural exploration of data science that delivers fast-to-market results.

The Co-creation approach

Design Thinking

SteppeChange works as a seamless, multidisciplinary team of experienced engineers, data scientists, and marketing and industry experts. We rapidly develop sophisticated analytical capabilities centered on the unique needs of our clients.

We do not offer standard consulting templates or off-the-shelf technology solutions. Instead, we are geared to design and implement custom-made solutions specific to the needs of an individual enterprise.

We start every project by building a deep understanding of the client’s business model, competitive position, strategies, organization, and what our clients hope to accomplish through data analytics. Through a Joint Value Assessment (JVA) process, we aim to actively engage our clients in what their data will better permit them to do. SteppeChange’s client-centered process requires continuous engagement with clients in designing and deploying solutions, to ensure detailed alignment with their needs.

Agile Data Engineering

We go deep into the data wrangling and plumbing, both to understand the nature and quality of the data, and to devise the most efficient ways to extract and process it. Understanding the client operating environment and systems architecture also allows us to design solutions capable of scale implementation in each production environment.

We do not build data warehouses and, in fact, consider them a constraint on developing effective analytics. Contemporary technology allows data to be extracted from live operating systems at frequent intervals. New tools, “shovels,” allow the construction of analytical solutions from raw data dumps.

This is central to our goal of working quickly and cost efficiently, creating time and space for multiple iterations, with a degree of trial and error in the design of solutions.

Rapid Prototyping

In close collaboration with our clients, we design, prototype, and test potential data science solutions following in a thoughtful and flexible way what is now a relatively standard design thinking methodology. This begins with developing customer empathy and the ability to walk in the shoes of those who use the client’s product or services, including internal users.

Next, in collaboration with the client, we conduct structured workshops to identify the full range of potential improvements in processes, decisions, and end-to-end customer interactions and experiences, which might be empowered by data analytics. We then forge consensus on which solutions have the highest impact on business performance.

Finally, we move on to building and testing early prototypes. We are keen to build and implement real artifacts that the client can operate on an ongoing basis.

Fast-to-market Solutions

SteppeChange delivers in-market capability end-to-end. Our processes encompass designing data science solutions, embedding of models into the production environment, and the support of marketing programs and the customer treatments involved.

When we complete an assignment, our objective is to leave the client in the position to use, and continuously evolve and improve, the solutions we have assisted in developing as a part of their core business process. To the degree possible, our solutions are modular, allowing clients to select sets of modules that they can configure and customize to create different solutions and meet new needs, while keeping certain core modules constant.