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Insights & Analytics
Create new and meaningful insights using data at scale and Advanced Analytic techniques
We use Advanced Analytics to solve business challenges. These challenges involve identifying and fixing quality issues with client data, processing large sets of unstructured data, finding patterns to better understand complex situations, automating manual processes, or predicting future scenarios.
Our view on using Advanced Analytics
- You’re working in a challenging business environment. To be competitive and thrive you need to make informed decisions – based on a growing amount of company data. How to organize your company to make this happen? What kind of analysis provides innovative insight into solving a complex business challenge? Questions like these are not new. However, the answers benefit increasingly from Advanced Analytics methodologies.
- Successfully applying Advanced Analytics in a business environment is challenging; you need to be able to trust the data, use the right technology, and interpret and communicate results well. Furthermore, concepts such as Digital Ethics and Privacy need to be considered.
- Within Deloitte we have broad experience utilizing Advanced Analytics. Among others, we frequently assist our clients with the following topics using Advanced Analytics:
o Improve Data Quality
Poor data quality can lead to missed revenue, regulatory scrutiny, and impaired decision making, among others. How to identify and fix poor data quality? We have collected our experience in this area in the DQ Booster. The DQ Booster is a catalogue of different smart techniques based on the latest data-driven technologies, that identifies data quality issues and efficiently fixes them.
Examples of techniques included are Anomaly Detections, Knowledge Graphs, and Fuzzy Matching.
o Structure large sets of unstructured data
Key data might be unstructured, such as contract terms stored in scans of paper contracts, or client data on photos of passports. In these kind of situations techniques such as Optical Character Recognition and Natural Language Processing can structure the information and make it available for analysis. Other examples are techniques such as Topic Modelling and Similarity Search.
o Process optimization
The idea behind process optimization is to automate parts of a process to reduce manual effort. An example can be a financial institution that needs to identify which clients have an increased risk of taking part in financial crime. We develop and implement tailored algorithms to automate parts of such a pipeline.
- Deloitte supports many organizations to make better use of their data and deploy Advanced Analytics in a responsible way. From banks and insurance companies, to technology companies and governments. This gives us a strong outside-in perspective on how to use data and Advanced Analytics to its fullest potential.
- Within Deloitte we work in multidisciplinary teams combining the right competencies that best fit the challenge at hand. For example, we combine top business experts bringing industry knowledge and organizational skills with Machine Learning specialists and data engineers, to solve business challenges using state-of-the-art techniques as part of an innovative and effective solution.
- Deloitte’s experience, breadth, and size creates a significant advantage for companies seeking to implement Advanced Analytics. From developing Proof of Concepts and Minimal Viable Products to a high impact transformation with complex and specific needs.
- Furthermore, we are a diverse and inclusive team with a lot of enthusiasm in working with data and Advanced Analytics. We have a proven track record using cutting-edge techniques in areas where these make a lasting impact.
If you would like more information, please contact any of the persons below to discuss how Advanced Analytics can help your organization become more data driven.