EY Big Data and Analytics vs KPMG Data & Analytics comparison

Used the right way, data and augmented intelligence can create competitive advantage, re-engineer processes and enhance risk controls.

Technology-savvy organizations, as well as “digital non-natives,” can benefit from analytics and augmented intelligence across all disciplines by using an infusion strategy.

Infusion means that by embedding analytics and artificial intelligence (AI) into the very core of your business processes, we can help you drive capital allocation strategies and investment decisions, create an end-to-end digital audit, generate new revenue opportunities, manage risk, conduct investigations, measure financial and nonfinancial performance, capture tax big data to inform decisions, increase customer satisfaction, and improve the customer experience.

Take a look at our insights on how combining issues-led and technology-enabled approaches helps you get to the heart of how organizations can thrive in the digital age.

Terms such as Machine Learning, Analytics and Big Data have become a part of the commonly used language of the Advanced Data Analysis field, or Analytics in short. This field has received attention and acknowledgment in recent years and become recognized as a valuable asset for every organization in any sector.

Analytics have changed the way we approach data in organizations. In the past, organizational information was replicated and transformed to match a format of the organization’s database. In order to access and analyze that information users created queries, however, not all organizational data could be processed that way since the database only worked with structured data.

Nowadays, we know that the numbers and types of data sources available to us are enormous, and we must take the internal and external information into consideration in order to get the full picture.

The Data & Analytics practice provides holistic solutions by collecting ALL available data and directing it towards interesting issues by raising anomalies and trends in the data.