My learning journey: Data Management Forum

Takeaways from Global Predictive BA & Data Management Conference (Milan, Feb. 2017)

I. Creating a data team is a custom process

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Image Credit: snegok13/Shutterstock
  • Integration problems: it is not so easy to understand where a data science team should seat. It is a good idea to have an external Center of Excellence (CoE), physically and operationally separated from the business. However, this could create internal integration problems for the organization, which can be weakened by hiring internals in a first place. If from one hand they may need extra training to be brought up to speed with big data tools and skills, they may also attenuate the communication problem with other departments and bring a solid knowledge of the business and the existing processes. Extra value point: look for different backgrounds, even internally;
  • Governance problems: data governance and organizational governance are essentials for a correct development and deployment of data science projects. If from one hand data governance is really relevant for security and privacy issues, company governance is the reason why DS team fails or succeeds. Clear policies and a single top executive to lead the effort of the team (CEO, CFO and CTO are the most common choices) are ways to reduce this class of problems.

‘Governance over data is paramount to empower business units and guarantee a fair and cost-efficient approach (Davide Cervellin, Head of EU Analytics, eBay)’

  • Cultural problems: data science team should adopt by definition a startup culture: they should be fine with failing, they should know how to deal with uncertainty, and they would need to act according to open and transparent processes. The team should follow an agile approach and work across teams and hierarchy layers. This creates a cultural clash within big organizations, and therefore the team leadership has the burden to smooth this issue setting clear goals for the team and establishing collaborative relationships with the rest of the company. They should also work hard in managing correctly the expectations as well as fighting the company resistance to change;
  • Data problems: I leave it as last because it is obvious: there are ALWAYS data issues, in any organization. They might be represented by sealed data silos, or by dirty/messy datasets, or simply by mismatches between technologies, data, and goals. Sorry, there is not an off-the-shelf solution to be implemented as it is, and this is why a DS team is so important.

II. Data science is a new paradigm shift

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Prof. Vercellis’ data strategy path.
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Image Credit: Natalino Busa’s slide.

‘Be fair with the data: let them ask you questions’ (Carlo Torniai, Head of DS and Analytics, Pirelli)

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Image Credit: Natalino Busa’s slide.
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Image Credit: Natalino Busa’s slide.
  • Initiative Optimization: this case leverages a ‘transformational program’ to fund big data analytics projects for organizational processes efficiency;
  • Business Impact: it leverages improvements in business processes without cannibalizing funding from special programs;
  • Corporate Asset: this case is more radical and fundamental because it uses big data to stay competitive or to gain competitive advantages within the industry.

III. There are sectors and areas that are hotter

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Image Credit: Peshkova/Shutterstock

‘The state-of-art of speech recognition today has raised a lot since 2012, with deep-q networks (DQNs), deep belief networks (DBN), long short-term memory RNN, Gated Recurrent Unit (GRU), Sequence-to-sequence Learning (Sutskever et al., 2014), and Tensor Product Representations (for a great overview on speech recognition, look at Deng and Li, 2013)’ — Full article HERE.

Insurance and banking (and finance overall), are likely two of the sectors most affected by big data and AI by definition. In the insurance industry specifically, we are observing applications to several use cases, as for instance claim processing, customer engagement, telematics and underwriting (for the full list of use cases and startups in the space, see here).

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Image Credit: Daniel Tapiador’s slide.

IV. Final Thoughts

There are few final general hints I would like to point out which I took away from the conference, which are misconceptions and mistakes that occur within data wannabe-organizations:

  • Experience is different than age: trust young generations more!
  • Communication is key: communicate and make visible (internally and externally) what the DS team is doing;
  • Focus on business: all these technologies may take you away from your final goal, which is doing business. So, remember to be a ‘business-driven tech-enabled’ company.

Written by

Research Lead @Balderton. Formerly @Anthemis @UCLA. All opinions are my own.

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