The data you collect should be speeding you up, not slowing you down.
The only way to make sure this is happening is to leverage the power of the cloud, but how do you keep up with all of the recent trends in this space?
Tom Mack is the RVP of Sales, EMEA at Qubole where he joined four years ago to build out a sales team in the Western United States. Qubole provides big data as a service, so they understand this landscape well. They focus on allowing automation to handle the life cycle of data clusters so organizations can get insights and yield out of their data as opposed to managing the infrastructure associated with big data technology.
In the time since he joined the team, Tom and his family have moved to London and opened up Qubole’s European sales operation where the team is on track to keep expanding throughout Europe. Tom’s job is to drive sales and create new business opportunities.
Tom joined us for this episode of B2B Revenue Acceleration to talk about the Big Data landscape, different verticals and industries that are benefiting the most from this technology, and differences between the North American and European markets.
Big Data Landscape
It’s no secret that companies are collecting more and more complex data, and are developing complex systems to go along with these data sets. There’s a major trend today of businesses migrating their big data workloads to the public cloud; any enterprise company that isn’t already in the cloud certainly has made plans to migrate there soon.
Every company’s goal is to enable operating markets or bring decisions closer to consumers.
This trend is emerging for three reasons:
1) The resources for big data are elastic. Your use of the cloud can be optimized based on your needs. This is a cost effective way to store large amounts of data while utilizing the flexibility of cloud technology to process data on an as-needed basis.
2) Businesses are more and more interested in streaming analytics. Rather than keeping complicated infrastructure on their premises, companies can use the cloud for anomaly detection purposes, price optimization, risk/fraud analytics, and more. All of the decisions surrounding these areas can be made much more quickly and efficiently using cloud technology.
3) Cloud access is broadening. No longer is this type of service out of the question for many businesses. This latest cloud technology enables many organizations to use self-service analytics.
People are more and more interested in streaming analytics.
Verticals and Industries Who Benefit From Big Data
Organizations are trying to provide larger datasets at a much larger scale so their people can make strategic and data-informed decisions. As a result, the amount of verticals and industries that are benefiting by big data is expanding quite rapidly. The practice of leveraging large amounts of customer data was once limited to IT companies, but has now made its way into everything from marketing agencies to the Internet of Things space.
One example of a vertical in particular that needs to utilize big data is mobile applications. As more people are making purchases through these devices there is a need for A/B testing the user journey. Tom and his team help these types of companies execute strategies on price optimization and competitive analysis by leveraging big data.
Differences Between American and European Markets
Tom has seen the data market completely change in last four years. There’s a lot more competition and confusing marketing messaging, so he’s seen the need for spending time educating customers.
He’s seen that customers are more conservative in London. They’re more likely to really do their homework and make sure they’re vetting companies on their end before making a purchase decision. It’s just a slower process with more to weed through in the market itself; there are a lot of different players in the cloud space now. Tom and his team are still winning business, but it just takes longer to educate these prospects in Europe in comparison to the US, where the technologies are more established.
Tom knows that the pace at which open source tech is evolving is substantial. Even in the evolution from Machine Learning to Deep Learning, it’s challenging to keep up. There is a specific skill set needed to develop this new technology that is difficult to find. But Tom has found that as long as he set expectations with clients and explains that there is a learning curve to utilizing big data, customers are on board for the journey.
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