Data Science Practice
15 Sep

Data Science Practice

Business Intelligence (BI) has given way toward something even more powerful: the practice of mining and analyzing raw data in a timely manner to make accurate predictions that can positively impact future outcomes. In a new whitepaper, we share Dovel’s data science process and methodology that examines the people, process, and technology we use to help customers derive value from their data.

People

Dovel’s data science team includes people with multiple perspectives and skillsets, including:

  • Data Engineers
  • Data Analysts
  • User Interface/User Experience (UI/UX) Designers
  • Data Scientists
  • Cloud Architect
  • DevSecOps Engineers

This combination of skillsets and expertise is what makes Dovel’s data science practice so powerful. Dovel combines the best aspects of all disciplines with top-tier technology to provide customers with specific and easy-to-understand yet powerful insights.

Technology

Dovel’s data science practice starts with Data Governance and follows an Enterprise Data Management (EDM) process, which includes:

  • Data Governance – The success of Dovel’s data science team is tied to the quality of its customers’ data. Dovel created a metadata repository and a flexible data model and data catalog so that the data assets are known, cataloged, understood, and shared.
  • Data Integration – Having a holistic collection of data from different sources is essential in achieving true value. Dovel brings significant expertise in analyzing, correlating, and integrating diverse datasets from other types of data stores. Dovel routinely works with various forms of unstructured (e.g., images, videos, PDFs, etc.) and structured data that have fundamentally different implications for processing and integration.
  • Data Visualization – When data is presented in a simplified visual format (such as a dashboard) it makes it easier to comprehend and delivers an immediate impact. Dovel’s data science team visually communicates its findings of a dataset during its initial exploration phase, as well as later in the process, when key findings of why a particular dataset is so valuable may be revealed.
  • Business Intelligence – BI capabilities coupled with skills visualizing data provides timely insights to business stakeholders by using that data. Dovel’s BI solutions cover various realms, including Enterprise Digital Modernization and Systems Integration and Cloud Adoption and Infrastructure Optimization.
  • Advanced Data Analytics – Dovel implements artificial intelligence (AI) and machine learning (ML) algorithms to create predictive analytics and natural language processing (NLP) solutions to help customers make better decisions based on predicted outcomes.
  • Decision Intelligence – With advanced analytics, organizations no longer need to make decisions based on “gut instincts”— they can make smarter and more informed decisions based on actual data.

Process

Dovel’s Data Analytics methodology incorporates a five-step process that goes from engagement and conceptualization through to production:

  • Engagement Conceptualization – Dovel profiles data asset content that shares a common context to infer a semantic framework for recognizing new assets as they become available.
  • Data Acquisition – Working closely with customer subject matter experts (SMEs) to identify data sources, storage requirements, and processing capabilities required for downstream advanced analytics and visualization, Dovel develops a data management strategy to procure, store, and report on the underlying data.
  • Data Transformation – Dovel refines raw data – in a repeatable way – into views that can be used for visualization and analytics.
  • Data Analytics – AI, ML, and NLP models are created, tested, and put into production (MLOps). The methodology provides an end-to-end process that aligns customer goals and objectives with disparate data sources to produce analytics, visualizations, and ultimately data products that can be used to create value for themselves or their organizations.
  • Data Productization – Dovel uses patent-pending scripts and software to create products that can be integrated into customer business processes and systems for improved decision-making.

A successful data science practice requires a mixture of skillsets, including various SMEs, and the right technology that allows organizations to experiment with data, learn from it, and put it to actionable use. Samples of how we’ve applied the methodology and created solutions to meet customer and industry-wide challenges are available on Dovel’s Discover Platform. For more details on how we combine this expertise and tools, check out the full whitepaper here.