Talent Management for Data Scientists

“SAS Institute’s data scientist program gave and reinforced the knowledge we lacked to conduct the necessary changes in order to become the analytical organization we want to be” Elis Rosén, Head of Analytics at Hi3G.

Getting the impact of analytics, big data and digitalization is not only about adopting new technologies. To get value, adjustments of all abilities must be made, and that includes those already familiar with analytics. But why it is highly critical to start recruiting, attracting, developing and retaining valuable talent and resources to handle big data, i.e. data scientists, as fast as possible?

Environments for Big Data Analytics are different from a traditional data environment in many ways. The human hand is crucial to the end result of big data analytics, and the importance of knowledge profiles for Data Scientists is clear. The combination of computer science, statistics, communications and business understanding makes Data Scientists’ ideal profiles broad and complex.

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Rita Sallam, VP of Research at Gartner
, has stated “Through 2017, the number of citizen data scientists will grow five times faster than the number of highly skilled data scientists.” It’s no secret that we have a skills gap in the data world, and as the expectations of business users grow, that shortage is only going to get worse. That’s why the new role of the citizen data scientist, or citizen data analyst, will become a critical role within companies”.

The biggest challenge in sourcing these skills is that very few (if any) candidates hold all these skills to an advanced level. Recruitment, trainings and career plans are challenging endeavors that will only intensify over the coming years. The most successful organizations in a data-driven world today are the ones that create, identify and develop their unique talents.

SAS Data Discovery Scientist program grew out of request from companies who approached us with this need.

To manage this challenge, the SAS Data Discovery Scientist program was established. It is accompanied by a detailed learning & development plan that extends high priority skills and introduces secondary priority skills. Based on the team’s higher intent, the subset of most- needed skills is prioritized, with the goal to deliver the positively contributing outcome of a specialized data scientist that supports their team. This also helps managers with designing their teams the right way, structuring them to maximize the differences in skillsets between teams while at the same time minimizing the difference between each team’s individual members.

Through a partnership with Bravura, the SAS Data Discovery Scientist program covers the full chain of Talent Management (find, recruit, train, develop, retain) for a Data Scientist and a Citizen Data Scientist professional.

“SAS Institute’s program for training data analysts is unique. There is no other program that combines analytical and business knowledge in this way,” says Mattias Andersson, Head of CRM Analytics at Scandinavian Airlines.

Hi3G and Scandinavian are two early adopters of a strategic talent acquisition approach that is in line with current business and human resource needs. Analytics proactively add value to the organizations if the right tools and knowledge are adopted by educated and competent teams. They have today the skills to support and to drive the change necessary for reaching the organization’s goals.

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Analytics vs supply chain uncertainty I - introduction

In my new series of posts I will outline typical business problems from manufacturing companies that originate in uncertainty and explain how Analytics is an enabler for improved supply chain transparency, improved stability and – at the end of the day – improved profitability.

The main objective for manufacturing companies is to ensure that the value chain runs as smoothly and profitable as possible. The core of their business is to transform raw-materials into a product and have this product made available to fulfil a customer demand through a series of supply chain processes (production, assembly, transportation etc.).

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Nonetheless, words like digitalization, IoT, big data are becoming a household in manufacturing companies, and focus is growing when it comes to utilizing data in a whole new way. The reason is not that traditional SCM theory is no longer valid, but that new technology can enhance supply chain efficiency by removing uncertainties.

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How will analytics-as-a-service impact business agility?

We kick off 2017 with excitement about big data and analytics still at fever pitch. Thankfully the discussion has now moved to business impact, and explores how good use of analytics is vital to make money, save money, and maintain competitive advantage. Data scientists now they have the sexiest job of the 21st century and are scarce.

Despite the hype, however, many companies are still struggling to work out how exactly to use analytics. It seems that moving from data to insights is still a big challenge. But there may be easier ways to get started with analytics than to rush out and recruit your own data scientists. I caught up with Colin Gray from the SAS Analytics On-Demand team to understand options available to organisations. Read more about our discussion and an invite to a Tweetchat on Friday 13th of January.

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The analytics road ahead: Formalising information value management

Data, it has been said, is the new oil. Indeed, according to Gartner, 90% of organisations apparently believe that data is an asset. But just having lots of data is not enough to ensure success: you also have to know how to use it to generate value. And in this area, younger, more agile companies are winning hands-down.

Why does data favor these ‘early movers’?

The business environment understands inherent advantages of early movers. As Maxwell Wesse, Aaron Levie and Robert Siegel argue in “The Problem with Legacy Ecosystems” this applies to data exploits too. 

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There are three characteristics of data that favour early movers. The first is that it is scalable. Unlike physical products, services and digital products are relatively easy to scale. Software can be used in any location, with minor adjustments for language. There is no incremental cost such as training new staff. This is a huge advantage.

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It's obvious, if you want to see - Look at your data in different ways

We sometimes talk about having trouble ‘seeing the wood for the trees’, meaning that you can get so wrapped up in the detail of what is around you (the trees) that is hard to see that they make a wood. The same goes for data. It is easy to get caught up in the detail, but sometimes you need to step back and look at the whole picture if you are to see it differently.

This may mean some work to set your data out in different ways. But once you can see an overview, new insights will automatically emerge. It’s a way to give your brain a boost. 

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All I want for Christmas…

The holiday season is upon us again, full of exciting events and related expenses. When important events happen, seasonal or in life in general, many of us tend to spend more money than at other times. From births to weddings and funerals, so many things happen in our lives that are marked by expenses, purchases and perhaps an altered economic status going forward.

jul_keldI firmly believe that the future of traditional banks relies on developing the ability to anticipate their customers’ needs and approach them with the right offer, at the right time. To do so, the banks need to gather customer insight from both physical and digital channels – and not just the banks’ own transactional data. As we reflect our lives on social media and search strings, banks need to be there to understand what we need and when we need it.

As the financial world is almost fully digitized, the potential for value chain disruption is enormous. However, I see no need to let fintech newcomers walk away with customers’ loyalty: Banks have the same access to using existing or develop the relevant tools. Perhaps more difficult, they need to develop a culture and organisational approach that allows them to act in time.

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Solvency II: Where do we go from here?

Solvency II went live almost a year ago. It should therefore have become part of the furniture by now, but it is clear from recent conversations that the journey is by no means over yet. What’s more, there are other challenges facing the insurance industry that will need to be addressed at the same time.

A few weeks ago, I attended the 6th annual EIOPA conference, in Frankfurt am Main, Germany. It was a big event, with over 300 attendees from insurance companies across Europe, and mostly at executive level. This is one of the few pan-European events where it is possible to hear direct from the authorities about the forces that are likely to be driving insurance regulation and risk management in the near future.

There were three major topics under discussion:Digitally generated server room with towers

  • The challenges of supervision under Solvency II;
  • A single market for pensions; and
  • The risks and opportunities presented by digital transformation in the insurance sector.

 

 

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The Market driven Journey IV: The demand and supply alignment process

Welcome back to the market driven journey in supply chain management! The three earlier articles of this short series  contained:

  1. The typical road to market driven forecasting – where companies go through three different levels of maturity before moving to being market driven
  2. Market driven forecasting – and the changes it requires from the organization and supporting technology
  3. Requirements to the supply chain organization in terms of responsiveness, monitoring of flexibility and fast & frequent re-planning.

This final article will outline conclusions from the first three articles, re-visit the key characteristics of being market driven and explain the demand and supply alignment process.

If you missed the previous ones, you find links to them at the end of this post.

Market driven

As stated in the second article – being demand driven deals with three things: Knowing true demand, being able to respond to true demand and having a process in place to align demand and supply.

 

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The market driven journey III – the market driven supply chain

Intuitively being demand driven indicates having a pull-based manufacturing system customer orders trigger sourcing and production activities. However – it is rarely the case, that this is possible – due to the long lead time this would cause. This article will point out the requirements to a market driven supply chain.

From the previous article in series “The market driven journey” on market driven forecasting, two things stood out regarding the role of the supply chain

  • The supply chain should be responsive to true demand keeping a high customer service level.
  • Possible demand scenarios must be evaluated and consequences to the supply chain identified – and the time to do so is very short.

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Are your big data skills disruption-ready?

Disruptive innovation—the big changes in an industry or sector that occur when someone or something turns the whole business model on its head—have huge implications. Big data is likely to be one of those disruptors. So are your big data skills disruption-ready? These following aspects will guide you to verify your readiness.

Are your big data skills disruption-ready

Digital security expertise is the biggest issue for businesses right now. This is, however, expected to be overtaken by analytics and big data skills within the next three years, according to an analysis by the Economist Intelligence Unit. In total, 43% of executives in the US and Europe said that these skills would be the most important within three years, compared with the current level of 38%.

 

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