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Jacques Platieau, Country General Manager IBM Belgium, Luxembourg

Jacques Platieau, Country General Manager IBM Belgium, Luxembourg

A recent McKinsey (2014) showed that in Europe about 7M youngsters are unemployed. Surprisingly, the same study stated that about 27 % of the European employers have jobs, but don’t find the right candidates to fill the vacancies. It’s a strange paradox that raises question on how we prepare our youth for employment.

At the same time our business and society are in transformation, a digital transformation. We’re entering the cognitive era, a new era for technology, business and thinking. Technology will access data in all its forms, will understand it, learn from it and technology will reason through it. It is an essential transformation for business but also for a sustainable and inclusive society. It implies the creation of thousands of new jobs. These jobs require new skills. And here lies Europe’s challenge. We don’t have these skills yet. Europe does not have a pool of talent anymore to address this challenge.

In the ICT-industry, we estimate that by 2020 we have 900.000 open vacancies. This is an acute problem for our industry. But with the world digitalizing at rapid speed, this will become soon a very acute problem for all industries.

Yesterday, in the presence of King Filip of Belgium, European commissioner Marianne Thyssen and European Parliament President Martin Shulz together with big enterprises like IBM, Nestlé, Solvay and many others, launched the Pact for Youth. The pact is about achieving a new level of partnership. It involves CSR/business, education, youth and the European institutions to deliver the required skills and employability to support growth of new jobs in Europe.

We need this partnership also to ensure that the European Youth is equipped with the right skills needed for these new jobs. We need to work to close the skills gaps by increasing the level of partnership between education and industry and we need also to bring innovation on the way to succeed this challenge.

Many new jobs are created in new areas. We did not even imagine them a few years ago. For example, explosion of data and its key role for business and society creates a huge need for data analytics skills. Another example is the complete reinvention of the way to engage with companies, business, people through mobile & social technology explosion, think to the APPS developed every day or to the social networks, it creates a need of new skills. Many other examples, I could mention the biotechnology, high value, high skills area, renewable forms of energy the same. All these areas require new skills, many in the STEM topics and also new mindset, more entrepreneurial. We can all be innovators.

To succeed this challenge, we need more collaboration between industry and education. This is the objective of the STEM alliance, to bring together multiple industries with the Ministries of Education to ensure students & teachers are learning the skills and knowledge to equip them for the new jobs.


IBM is a driving force, both in the STEM-alliance as in the Pact for Youth. And we do what we preach. IBM is helping to increase student engagement in STEM and align the worlds of education and employment through a portfolio of education offerings and skills-based volunteering. We organize workshops for more then 22.000 students through more than 1.000 sessions, or 1-to-1 mentoring sessions with about 1500 teachers. We organize STEM camps, etc. I n total 2.700 IBM employees are involved in STEM initiatives.

In the UK, France and Turkey, we invite teachers to spend 1 or 2 weeks at industry. They can take a lot of new insight back to the classroom. To make this sustainable, we offer the teacher an industry mentor for continued connection to business.

In Belgium, we successfully implemented Extreme Blue, that gives the opportunity to students to get exposed to real client projects for a period of 6 to 8 weeks under the mentoring of IBM employees.

Finally, we need to innovate across the education and training systems. A good illustration of that is about the vocation education. We need to find new ways to deliver the skills for the “middle jobs” that require high competence but not necessarily a university degree. One way to do this is to eliminate the silos between school and vocation education. The dual system in Germany and other countries is an example.

In IBM, we have 2 successful initiatives:

  • In the US, we have introduced a radical new approach that integrates school and vocational college in one 6 year system. It has the support of President Obama and next year, there will be 100 schools with such an initiative.
  • In the UK, 4 years ago, we introduced a new apprenticeship programme. Before that we had 100% graduate hire. This innovation has been very successful, the students mostly get jobs at IBM on completion.

We invite more business to join in all these partnerships, the pact for youth, the STEM alliances to help our young people to get equipped with the right skills for all the new job opportunities we see coming and at the end, to serve and contribute to a sustainable and inclusive society.

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Blog by: Bart De Kempe

Big data analytics: deeper insights-driven financial performance

CFOs all used to rely on excel spreadsheets and past budgets in order to plan, budget and forecast. Answering questions such as: will customers pay on time; have we got too much stock; will production costs increase?

Today, CFOs face challenges that no spreadsheet can overcome. In a fast-paced, global and heavily competitive market place, they need to make more decisions, faster. A warm winter can break a clothing retailer’s profits. An increase in unemployment rates in one country can significantly impact the profits of a company in another country. CFOs need to anticipate changing market trends in order to guarantee profitability. For that reason, they require deeper insights into financial performance than ever. Analytics can offer real competitive advantage in this regard by transforming a company’s planning, budgeting, forecasting, reporting and analysis processes into more dynamic, efficient and connected experiences. The excel spreadsheet has become obsolete.

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Gut feeling vs. predictive analytics

And yet IBM’s most recent CFO study showed that 52% of CFOs still use spreadsheets to forecast future billings. 14% even base their financial planning on gut feeling. It is time for a sweeping change. Apart from being vulnerable to errors and inaccuracies such as broken formulas, cut-and-paste errors and unstable macros, the major disadvantage of spreadsheets lies in the fact that it they can take 80% of a CFO’s time to juggle them. Time that could be better spent analyzing complex yet accurate business data to predict and shape future business success.

So how can analytics aid the CFO? Predictive analytics tools can help today’s finance professional anticipate and predict an enterprise’s
financial future based on real-time data from a variety of sources, such as internal databases, employment rates, weather forecasts and competitive analyses. Meanwhile, adding collaboration and deep, automated financial analysis to strategic planning and corporate budget development enables organizations to adapt quicker to change with more frequent, rolling forecasts.

In addition, analytics lets organizations examine profitability in order find the most viable products, customers and sales channels. With powerful what-if scenario modeling, they can evaluate strategies, test assumptions and compare current performance with historical actuals and external benchmarks. End result? Forward-looking financial analysis helps guide sales, marketing and supply chain functions to optimally deploy resources towards the most profitable opportunities.

The CFO’s holy grail!

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November 9th, 2015

Blog by: Bart De Kempe

Big data analytics: acquire, grow & retain customers

The CMO’s role has never been so exciting. Nor so complex. Companies are operating in a more competitive, global market place, in which the customer has almost endless choice. Meanwhile, that same customer’s means of interacting with a company has changed exponentially. Touch-points have multiplied. In addition to the good old-fashioned store, customers are purchasing, dealing with customer service or otherwise engaging with companies across social media, mobile apps and websites.

Clearly, heightened competition and complexity represent a risk. A company that fails to provide the ever more demanding customer what they want, when they want it, risks oblivion. Or in real terms, reduced customer satisfaction and retention, churn and ultimately a hit to the bottom-line. Likewise, it represents an opportunity to the company that grasps and harnesses big data. Across the board: to improve customer satisfaction and retention, reduce churn, diminish customer service costs, and to maximize revenue.


Enter analytics. We now have tools at our disposal that can help the CMO attain a 360-degree understanding of the customer by combining data from all the aforementioned touch-points, both on and offline. Whether the customer is dissatisfied, gearing up to purchase or just exploring options, we can target them via the right channel, with the right message, at the right time. The long-term prospects are immense. From the basic customer segmentation of yesteryear, we are now looking at highly sophisticated segmentation that allows us to classify customers based on lifetime value to a company, not just basic (and often meaningless) demographic data.

How does it work?

Specifically, predictive intelligence can provide insight into each single customer based on buying behavior, web activity, customer service and social media interaction. This level of detail on each unique customer allows companies to predict and personalize marketing and communications efforts for each customer. Are you a bank? Know whether a customer is ready to accept a mortgage offer or cancel a policy and act accordingly. In FMCG? Understand if a loyal customer is ready to switch to a competitor and why, and respond with a special offer.

Mass marketing, as we have known it is no more.

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November 9th, 2015

Blog by: Cedric Mullier

Big data analytics: optimizing operations

Global operations means global supply chains. Meanwhile, the integration of complex technology into almost every professional task means the risk of downtime and the threat of fraud are greater than ever. These areas all fall under the jurisdiction of the COO. As a result, the intricacy and importance of the COO’s role, and as a result, their influence, is at an all-time high.

How do leading COOs use analytics to drive superior operations within their organizations?

Awesome opportunities lie in real-time supply planning. Picture for instance an airline. If one of their engines has a minor issue which requires repair, analytics tools are available that help visualize the fastest delivery route for the required engine part based on stock inventory and weather forecasts. With this kind of real-time intelligence aiding decision-making, the plane will not need to stay on the ground for long, and will not create overall delays at an airport. All in all resulting in substantial savings all around, not to mention avoiding at least some customer inconvenience and annoyance!

Beyond one-off crises, analytics is being utilized for more long-term supply chain integration and optimization. 360-degree tracking of all customer and supplier data in a single space allows for improvements across the board, from inventory management through to forecasting.
At production plants, predictive maintenance tools are helping prevent technology failures. By tracking a combination of how a machine is faring, how it has held up before and when it was last repaired, operations is able to schedule maintenance in the most optimal manner possible and significantly reduce asset downtime – and perhaps even eradicate it. The days of illegible maintenance scheduling on white boards are long-gone.

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And our favorite example: picture a zoo! The number of visitors relies very much on the weather. By combining datasets of weather forecasts, historical visitors and consumption of food and beverages, the number of visitors can be predicted, and operations can adapt opening times and features accordingly to maximize efficiency and profitability.

Last but by no means least, analytics can assist in preventing fraud, also the domain of the COO. It can help uncover fraud and allow companies to take action before damages and losses are incurred, by analyzing data across multiple sources in order to detect fraudulent activity, be it welfare payments, financials services, healthcare, insurance, retail, credit cards, taxes, identity theft, insider trading and money laundering.

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Door Peter D’Haeyer, Watson Analytics expert bij IBM

Sidder en beef, het is Halloween vandaag. Ik hou van het vallend blad en de donkerrode kleur van de herfst. Pompoenen uitsnijden, spookhuizen, verkleedpartijen en gratis snoep zijn al lang geen exclusief Amerikaans gegeven meer. Halloween is overal. Mijn absolute nummer één? Horrorfilms.
Voor het plezier heb ik een lijst samengesteld van horrorfilms die de afgelopen decennia in de bioscoopzalen zijn vertoond. Ik maakte gebruik van data als de lanceerdatum, de distributeur, de bruto-opbrengst, de opbrengst na inflatie, het productiebudget en de reviews van fans. Mijn overzicht bevatte vrijwel alle klassiekers in het genre en populaire recente films.

En dan nu het leuke werk. Welke inzichten kan Watson Analytics halen uit deze reeks griezelfilms?

  • De meeste horrorfilms verschijnen in oktober. Een niet onverwachte conclusie. De meeste horrorfilms verschijnen in de Halloweenmaand. In juli en december lanceren filmbonzen de minste griezelprenten. Opvallend: filmfanaten beoordelen horrorfilms die in de decembermaand verschijnen significant hoger in vergelijking met andere maanden.
  • Horrorfilms uit de jaren ’70 krijgen een hogere beoordeling dan recent werk. Herinner je filmklassiekers als The Exorcist, Jaws, Halloween en The Omen nog? Ze stammen allen uit het tijdperk van de jaren ’70.
  • Hoe hoger de beoordeling, hoe hoger de opbrengst (in de VS). Ook na inflatie brengen films met een hoge beoordeling voor Hollywood ook het meeste geld in het laatje. Aangezien horrorfilms in de decembermaand gemiddeld genomen de meest positieve reviews opleveren, is die maand ook een stuk lucratiever.
  • Universal is de filmmaatschappij die de meeste horrorfilms heeft uitgebracht, met een totaal van vijftig films. Toch mocht Warner Bros het meeste geld binnenrijven. Gemiddeld brengt een horrorfilm tussen de 5 en 20 miljoen dollar op.
  • Hoe hoger het productiebudget, hoe hoger de winst. Watson Analytics gaat automatisch op zoek naar correlaties en associaties in de dataset. Zonder een specifieke variabele te selecteren is het met de tool zeer eenvoudig aan te tonen dat hoe meer geld in een film wordt gestopt, hoe groter de kans dat de kassa rinkelt.

Wat leert deze case ons nu?

Naast het feit dat Halloween beter in december plaats zou vinden, toont bovenstaand voorbeeld aan dat data-analyse eenvoudiger en intelligenter dan ooit is geworden. Tools zoals Watson Analytics stellen bedrijven in staat om eenvoudige vragen te stellen, net zoals je die aan een collega zou stellen. Door een dergelijke oefening te maken stoot je op inzichten waar je initieel niet eens naar op zoek was, maar die wel uiterst relevant zijn voor je business. Je kunt bepaalde veronderstellingen bevestigen of ontkrachten.

In bovenstaande case gebruikten we een dataset over horrorfilms, maar je kunt gebruik maken van eender welke dataset, gestructureerd of ongestructureerd. Wat zeggen klanten over een bedrijf op sociale media? Hoe gestresseerd is het personeel van een bepaalde afdeling? Wat is het kostenverloop voor een product? In welke regio’s koopt een bedrijf te veel voorraad aan? Het zijn vragen waar klassieke analysetools moeite mee hebben, maar waar Watson Analytics in een oogopslag een antwoord op geeft, uiteraard onder de voorwaarde dat het bedrijf in kwestie over relevante datasets beschikt.

CxO’s nemen nog te vaak beslissingen op basis van hun buikgevoel. Door (realtime) data te analyseren, afkomstig uit tal van bronnen zoals interne databases, werkgelegenheidsstatistieken, weersvoorspellingen en concurrentieanalyses kunnen bedrijven anticiperen op toekomstige opportuniteiten of issues.

Meer info:

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