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How to Capitalize on Data at the Edge

data at the edge Play

Ensuring data at the edge is seamlessly integrating with your legacy and core data will be key for the future of the technology function.

How do different industries and sectors define where the edge is, what challenges do they face implementing data at the edge and analytics into business models, and how does that affect the digital roadmap of the future?

In the last two years, the implementation of edge data has grown rapidly alongside the evolution of technologies. This change means that companies must consider how they implement data insights and use cases into their business model in an efficient manner. In addition to the blending of edge analytics, other data such as legacy data provides unique and complex challenges in an already opaque field. 

The roundtable debate between Maritza Curry, Data & Analytics Professional at RCS Group, Alison David, Chief Information Officer at GE Healthcare, and Nick Giannakakis, Chief Information Officer at Motor Oil moved from wider discussions of data at the edge to personalized examples, and comparisons and contrasts. Moderator Peter Stojanovic began by asking what data on the edge meant to them specifically. 

What does data at the edge look like in different industries?

Maritza Curry pointed out that edge analytics and data near the edge is an emerging architecture in data analytics and thus depends on the industry and branch that you enter. She stated that in central data banking and financial services the risk appetite is different depending on if you were considering fintech or more traditional banking services. “When defining dating strategy or architecture you have to think of the commercial value of including analytics at the edge in your strategy.” She explained. 

For Nick Giannakakis, edge analytics is an on-site tool in big industrial complexes, an example being natural gas pipelines. He explained that deploying edge analytics as well as the devices used by workers allow them to make more educated immediate decisions on site. Alison Davis referenced her previous work with the Natural History Museum and how important the implementation of edge analytics was for maintaining the collection. She gave the example of applying different data insights, on visitor data and weather data helped with pest management.

Extracting insights from edge data

One thing that was made very clear during this roundtable discussion was that while data at the edge was an incredibly useful tool, if there was no clear plan to action it then its value becomes void. Curry pointed out that you have to have a clear and compelling use case, if you’re going to invest in real time decision making, which will provide analytics that add value to the company. “Not real time analytics for the sake of it but by design.”

Davis added that it was important to understand if an organization was ready to use real time data. She stated that it was pointless to collect data “unless you are going to do something with it.” And went on to say that it was imperative to understand how you are transforming that data into a format that can be implemented into the business process 

Challenges with blending data at the edge

Giannakakis highlighted the issue of blending data at the edge with traditional historical data that they have from the other parts of their business. “To be able to further improve models with the blending of this data is a challenge.”

Curry and Davis were in agreement about this. Curry stated that data management has become a lot more complex than it was before whilst Davis explained that, particularly in the healthcare sector, there is graphical data and gene sequencing data to consider which is much more reactive and creates a unique set of challenges.

Digital Roadmaps

“Two years ago the edge was something exotic or something that we weren’t interested in.” Giannakakis explains, “We have seen a big shift driven by the industry itself and the evolution of tech. An example is 5G.” 

Curry stated that in the banking industry the focus of the future is on security and using AI to implement data privacy and security, which can alert them with data on fraud and other such security threats. 

Davis finished the discussion by highlighting the importance of the resiliency of supply chains in manufacturing and supply commercial businesses. She explains “With the emergence of world events that affect supply chains, we can use edge data to understand the impact and how to react to it and maintain the resiliency of supply. This means we can be there for the customers and that’s a valuable use of edge data.” 

This roundtable on data at the edge was created in partnership with HPE.

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