Data drives digital transformation.

Create sustained competitive advantage with a data-driven strategy that leverages predictive analytics, machine learning, business automation, and AI.

“Information is the oil of the 21st century, and analytics is the combustion engine.”

Peter Sondergaard, Gartner Research

Technology is advancing at light speed. Each innovation presents exciting new business opportunities, but also the potential for disruption, perhaps by a competitor you never saw coming. A new competitor that better leverages technology to provide your customers with an enhanced value proposition, potentially rendering your offering as “out-of-touch.”

Smart businesses stay ahead of the competition, and potential disruption, by constantly reinventing themselves to better serve customers. They’re always learning – scanning for new information that enhances their understanding of customers’ needs and provides insights into future trends.

At Emylla, we know that a plan must deliver results – quickly. We’re focused on generating short-term wins, while developing and implementing a long-term plan for digital transformation that is highly customized to your business, industry, and customer value proposition. We follow a data-focused approach with five key stages:

1 > Customer Experience

It starts here. Any meaningful strategy must enhance the customer value proposition. How do you increase a customer’s willingness to pay? How do you help customers achieve their goals, fulfill their vision, and make them successful? We need to understand customers at a granular level. And, moving forward, what is the framework for continuously learning, testing, evaluating, and evolving that customer experience to make sure that you are always one step ahead?

2 > Data System

For digital transformation, it’s truly a data system that is required … not just collections of data points, sets, reports, or platforms. In this stage, we integrate existing data sources and identify new data streams for additional insight. What data do you have? What shape is it in? How is it organized? What additional data do you need? We answer these questions to set the foundation for developing and scaling machine learning models and predictive analytics.

3 > Analysis and Model Development

Model development is pursued in two parallel paths. First, we identify and prioritize specific problems to solve. Once defined, machine learning models are developed, trained, tuned, and validated. Second, a comprehensive “exploratory data analysis” is used to uncover new insights. Are there trends, inefficiencies, customer needs, and opportunities that we didn’t previously see? A framework is also established to monitor model drift, biases, data governance, and privacy concerns.

4 > Insights and Sharing

With data organized, integrated, and flowing, now let’s unleash it to develop a deeper understanding of your customers, your operations, and your market. These new capabilities and insights, however, don’t provide any value unless they’re easy to consume and available to decision makers when and where needed. A comprehensive and customized “data visualization strategy” is crucial to extending the value of these new insights to all levels of the organization.

5 > Organizational Alignment

Stakeholder buy-in is critical to the success of your digital transformation strategy. Are you ready at all levels of the organization? Is it supported? Are you attracting the right talent? We’ll help identify critical training needs and develop a system of continuous employee development. Also, let’s go back to stage 1 and see what has changed. Are there new data sources available? Let’s keep exploring, experimenting, measuring, and reinventing! The process never stops.

“It’s not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change.”

Charles Darwin