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Interview with Jennifer Roubaud, Dataiku, on the future of predictive analytics

We are proud to announce that Dataiku will be joining this year’s edition of Big Data Week as Influencer Sponsor.

More than 100 customers in industries such as e-commerce, industrial factories, finance, insurance, healthcare, or pharmaceuticals use Dataiku DSS daily to collaboratively build predictive dataflows to detect fraud, reduce churn, optimize internal logistics, predict future maintenance issues, and more.

We have used this opportunity and invited Jennifer Roubaud, Country Manager at Dataiku to get her views over the impact of big data, as well as her vision towards the future in a special interview made exclusively for our blog.

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  1. How have you come to the decision to sponsor BDW London 2017?

BDW London is about getting people around the world from all industries excited about the prospects of big data and making it an integral part of everyday business (not just an afterthought). And here at Dataiku, we have the same basic mission: to bring the power of predictive analytics and machine learning to all companies, helping them improve their products, operations, and everything in between. We enable businesses to leverage their data via a central, collaborative platform accessible to coders and non-coders alike so that data democratization becomes a reality. Given these parallel missions, we wanted to sponsor BDW London to take part in inspiring everyone to think about using big data to improve the way we live and work.

  1. How do you perceive the impact of big data in your field?

The concept of big data has been around for several years now, so most businesses we work with are already collecting tons of data, but the impact we see is that they aren’t able to use it or get value out of it even though it’s there. Where we do see companies successfully leveraging their data for insights, we see an immense impact – often it means allowing the business to scale a specific process or accomplish something automatically that used to be manual. And where we really see the impact of big data is businesses that are able to use real time data in their production environment for predictive analytics and machine learning – this is really where change happens and companies can innovate.

  1. How are your customers influenced by big data and how do you help them cope in this new technology age?

Lots of our customers are already using data in some ways, but they’re not able to keep up – they have trouble scaling their big data efforts. Even large enterprises that have 100-plus person data teams are not able to successfully deploy data products to production quickly enough to have the impact they want. We help our customers by providing a tool where data, and the projects created out of that data, are contained so that work is centralized (and therefore reproducible). We also make it possible to go from an experimental or sandbox state, playing with data and testing out different models, to production easily (yet in a controlled way) so that data teams are actually having a real-world impact and not constantly in a development state.

  1. What are your general projections for your industry in the coming years?

Big data will only continue to grow and get even bigger, and of course, the Internet of Things (IoT) will play a large role in that. The proliferation of IoT will make real-time analysis an essential component of businesses’ data strategies. And since the field is still relatively new, I’m sure there will be other major developments yet to come that will greatly impact the industry as well, which is why it’s important for businesses to stay agile and be able to adapt quickly.

  1. What is your main concern regarding the evolution of technology and why?

My biggest concern about the evolution of technology, specifically in the big data sector, is that it will grow and develop so quickly that businesses are unprepared and aren’t agile enough to keep pace. There are always new techniques, new languages, and oftentimes companies will adopt one tool. By the time it’s rolled out and everyone’s using it, the world’s on to the next thing, and the business isn’t able to adapt. It’s a concern for me because this really stifles data innovation in these companies in a time where sometimes a business’s only differentiator is their ability to creatively use data, either to develop new ideas or make current operations and processes more efficient and effective.

  1. Are there any solutions in sight to these matters?

The solution is for companies to always have this in mind and actively make an effort to stay agile. Businesses should be agile in two ways when it comes to big data; the first is that they should be able to quickly turn raw data into predictions so that if a business decision needs to be made fast, it can be almost immediately backed up with data and not have to be delayed for months. And the second is that they should not get locked into using one specific tool or language. Companies should always have the flexibility to allow their data teams to use the best tools out there in the data science world (often open source), but while also being able to maintain control with data governance policies.

  1. Which of the three main topic tracks of BDW London 2017 is your favourite and why?

I am most excited about the “AI, Real-Time and IoT Projects” track, partially for the reasons I mentioned above – it’s the future of predictive analytics, and it’s really not so far off, which is even more exciting. There are lots of amazing companies out there doing great things with AI, real-time, and IoT, so I think it will be a wonderful opportunity to learn about how any business can leverage these things in their own way and how they can (and will) become more accessible in the coming years.

Jennifer Roubaud, Country Manager at Dataiku

Jennifer

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