Data science is the promise of the digital age; that is, to gain—in a semi-automated way—truly insightful analysis of large and varied data sources which enable data-driven business decisions. Using data science to solve complex business challenges requires more than just having access to data, investing in analytic software, and hiring some data analysts or scientists.
There is no "easy button" in data science. Sustained success requires a complete cultural shift within a business to establish and maintain analytics-based decision management. Building an effective analytics culture can transform a business. But each business is different, with unique challenges that often require customized solutions to achieve the best results. Elder Research has decades of cross-industry analytics consulting experience in developing prototypes, production analytical models, custom software, and unique visualization tools that provide practical analytics solutions to real-world problems for a sustained competitive advantage.
At Elder Research, we approach data science consulting with your business’s needs foremost in mind. We begin every project by framing the most pressing business objectives your company faces and developing the most practical approach to achieve faster results. Early, measurable results build momentum and allies and ensure internal program support.
Using our Agile Data Science methodology, we develop and validate customized analytic solutions using Lean methods with regular client touch points. Making the results accessible to business decision makers is vital for adoption, so we work with you on data visualization solutions to be deployed in your production environment. As a result of this process, our clients enjoy the sustained benefits of advanced data applications that are customized and thoroughly tested to meet their needs.
Our Data Analytics Consulting Approach
Phase 1: Problem Definition and Data Discovery
We work with you to understand your unique business, analyze data, and recommend a strategic plan specific to your objectives, data sources, and systems.
Phase 2: Solution Development
We develop custom data science consulting solutions to tackle your specific problems, working closely with your teams to ensure our models are designed with your business in mind.
Phase 3: Deployment
We work with your teams to design the best way to implement our data science consulting solution into your business, ensuring it’s actionable and easy to understand.
Our expertise and experience in the world of practical data science consulting is always growing. Here is a brief overview of our most requested analytics consulting services:
Prototype models demonstrate whether predictive analytic techniques can answer an important business question or improve a current modeling effort. An analytic prototype helps an organization understand how analytics can be applied to more complex business problems. The result of this consultation is to confirm that a customized analytics solution will deliver value at a modest level of investment and risk.
The Model Validation consulting service provides expert judgment on the quality and robustness of a client’s existing model. Our judgment is provided in two forms; a concise official letter of judgment and a more detailed report of our findings including recommendations for model improvement. Elder Research works as a trusted third-party advisor to validate model performance and evaluate whether or not your model meets the necessary regulatory and compliance standards.
Customized Analytics Solutions
Unlike off-the-shelf products, the customized analytics solutions we develop at Elder Research combine the business domain expertise of our clients with our deep understanding of advanced analytics to produce best-in-class solutions that easily integrate with your business processes and systems.
Predictive modeling is an uncertain practice. It is difficult to know in advance which algorithms and variables will, when combined, reveal any secrets a data set may be concealing. Our goal in every project is to produce the model with the best out-of-sample error, that is, the model that performs the best when given new data. To achieve this goal, we are open-minded about the technique to use since it is often surprising which algorithms perform the best. We often use an ensemble method, a combination of different techniques, as this method almost always outperforms a single algorithm and can significantly improve results. Ensembling is now considered a best practice for predictive analytics, largely due to our work.
Data Products and Visualization
Providing easy-to-use, intuitive access to data adds tremendous value to analytic solutions. Our software tools for developing custom, web-based, interactive data visualizations provide flexible solutions that are able to match your needs by integrating with your processes—rather than making your process integrate with our tool. We work with you to create powerful visualization solutions that provide context and actionable insights to end users across different platforms.