Data and the structures that govern its uses, availability and quality, need constant investment if they are to serve businesses.
Rapidly advancing technological advancements have gifted business and organisations with a wealth of new methods for attaining and using data. However, this creates its own issues by complicating the process of structuring this data into something that is useful and high quality. While in some industries, unstructured data can be useful and is often necessary, for the vast majority of data analysts, visual representation and data activation is vital. This exponentially growing amount of data can make it difficult for data analysts to provide the same level of insight. Machine learning and A.I. are already proving these can be highly competent at restructuring data, however.
One of the main challenges faced by analysts is identifying exactly what their users are looking for and gathering that information as easily as possible. The process of restructuring the workspace to incorporate this emerging technology is often a long and arduous process. The upside? It is more than worth the effort as technology like machine learning and natural language processing can be used to achieve the desired results more efficiently.
With Mark Chillingworth moderating, the speakers of this roundtable debate include:
- Dan Kellet, CDO, Capital One
- Daniel Jeavons, Director Data Science, Bell
- Dr Frank Moser, Sales Director, Continental Europe, Wandisco
- Maritza Curry, Head of Data, BNP Paribas, Personal Finance SA
- Ross Simon, Global CDO, CDP
A.I and machine learning solutions
Machine learning and A.I is used to structure data activation most successfully when used to give users easy access to relevant information quickly. Dan Kellet, a data analyst From Capital One uses natural language processing to examine conversations between customers and their agents. This allowed then to identify recurring problems with their mobile app. They were then able to use this data to prioritise features in their mobile app, to creating a better customer experience in a relatively low-cost manner.
Data science is best served by technology when the method remains user centric, many people don’t like change and according to Martiza Curry of Personal Finance SA, 80 percent of users are not data analysts. Therefore, the rapidly transforming, already alien realm of data science may make it difficult to reach intended audiences. To overcome this, future businesses will need to ensure their methodology is both accessible to users with a low level of technological literacy, and interesting and useful to experts.
For this reason, Personal Finance SA built a custom data and analytics portal so users could easily access relevant information. This portal included a space for professional analytics to ask questions and make suggestions. In this way, they were able to address the needs of all their users, regardless of their familiarity with the technology.
The future of data activation and analytics
The role of A.I and machine learning in data activation is only going to grow as the technology progresses. In the future it may be difficult to distinguish between the quality of insights produced by a human analyst and an AI. However, it would be imprudent to entirely discard traditional methods of data analytics. These methods are still useful in a variety of ways, particularly for reaching users with a lower level of technological literacy. Instead, these new technologies should be used to augment traditional analytics
This roundtable was created in partnership with Wandisco.
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