Don´t forget to change, it might be the last thing you did not do

We know that strategy is a long term thing that changes rarely. For example, a company who produces an item, let say a car, has decided early on to do just this. Their strategy might be to manufacture exclusive cars, just to keep it simple.

If we then look at the “how”, the tactics, we can for example see different ways to distribute, manufacture, and advertise. This is where organisations need agility and understanding. You might ask yourself why. Because change is inevitable. Scary? Yes! However, the results of not changing is even scarier.

However, if the strategy is well defined and understood by everyone, it gets easier to adjust and develop your tactics. Simply because everyone knows where they are going, even though the way there alters. The competitive landscape is ever-evolving. Continuously adapting to others slow organisations down. To me it sounds better to drive the change, instead of waiting for others to take the lead.

If we go back to the car industry, Tesla is revolutionizing it. Electric cars are taking over. Soon they will also be self-driving. It’s a fact. How come there is only a couple of other brands who build competing electric and/or self-driving cars at the moment? Tesla can provide a complete eco-system including free charging stations to their electric vehicles who are able to cover much longer distance then the rest.

Sorry, this is not a Tesla-blog providing you with the perks of being a Tesla-owner. However, it’s an understandable example most people recognise. In this case, competition need to adapt or perhaps come up with another offering that is even better than what Tesla is offering. The slow ones will take a huge risk and might not be able to close the gap.

This is where agility and understanding comes into the picture. Tactics. What do we need to do now? How do we build a better offering? How do we beat the competition?

How do you decide tactic and how do you change tactics?

Most of the information is sitting there in front of you. Data. Through your data you can understand, decide and create. The list goes on. Your data gives you unlimited possibilities.

There is a term called Data Innovation (Whitepaper: From Data to Action). Take your data – play with it – find out what you can do. Change. Innovate. Be better.

Some of my colleagues have been working with the Swedish Ice-Hockey Team in their preparations towards the upcoming Canada Cup (reflective blog post will come soon). Exploiting the underlying data and deciding on tactics according to the values they found will improve the chances of a medal. Impressive? Absolutely!

Until next time, I recommend you to read this article (in Swedish) by my colleague Christer Bodell and get some insight into what is happening in Sports Analytics. He is discussing how we need to allow data to make decisions, and leave our feelings to the side. Staying rational during decision making while being angry, happy, sad etc. is very difficult. Perhaps we should let the emotions flow when we have won, not before.

I believe that sports/athletes can benefit hugely from Analytics, and it is already proven in many ways. Due to the unmatched dedication to winning, it is only a matter of time until analytics has become a cornerstone of sports.

Dare to change. It is difficult, nonetheless beautiful.

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Why benchmarking is necessary for marketers on the data-driven journey

Artificial intelligence has been around for quite a while—research on it started in the 1950s, in fact. But it is only now that it is really moving beyond the pages of science fiction, and into the realms of not just reality, but usefulness. In part, this is because of recent improvements in analytics, computing capacity, and algorithm development. These together make machine learning and cognitive computing, the two main components of artificial intelligence, much more accessible and useful.

There has been a lot of excitement in particular around how artificial intelligence is likely to improve customer experience. Cognitive computing, for example, uses analytics to answer questions. It is already being used to manage fast responses to customer queries to service centres. Computers create an initial response and direct the query appropriately. It’s fast and effective. Machine learning is enabling improved planning of marketing campaigns, based on customer segmentation and preferences.


Perhaps most excitingly, the emergence of deep learning, the third component of artificial intelligence, is starting to benefit marketing, and in particular, more complex tasks. It is early days yet, but if a computer can be ‘taught’ to drive a car, there is little reason why it can’t learn to carry out marketing tasks like mapping customer journeys.



Data-driven marketing?

The idea of artificial intelligence being used in marketing, and especially to improve customer experience, is very exciting. But in reality, artificial intelligence is totally dependent on data. If we’re honest, we know that for most marketing teams, including here in the Nordics, the journey towards data-driven marketing is only just beginning.

Marketing today is both easier and more difficult than it has ever been before. We have access to huge amounts of data, but how should it be used most effectively? Marketing is not, perhaps, seen as a natural partner for mathematical modelling and analytics. But to succeed in a world that is becoming increasingly data-driven whether we like it or not, marketers need to understand and use data to answer questions about their customers’ preferences and problems. Fortunately, analytics tools are becoming increasingly user-friendly. What is required now is more a willingness to try things out, to see what value can be created, rather than a degree in mathematics or computing.

More complexity, increased reward

The Internet of Things (IoT) is one of the key drivers of the huge increase in data volume in recent years. With estimates suggesting that the number of connected items could increase exponentially over the next few years, the data volume is likely to get ever-larger, and much of it requiring real-time analysis.

But if the IoT will add complexity and data volume, it also has potential to increase the rewards. It is already, for example, being used to improve customer retail experience, through the use of improved personalisation and tailored interactions and offers. The question, as we have noted before, is not so much can value be generated, but how many of those who could benefit are even aware of the potential.

Benchmarking to assess progress

With so many aspects of marketing being influenced by digital customer experiences, how does a marketing team assess progress and seek guidance on aligning priorities? SAS believes benchmarking can help. If you're ready to take stock of your digital marketing approach, we are ready to help you develop a game plan to strengthen your marketing confidence. Take the assessment and get your score.

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Fraud and Fado: mournful tunes but no sentiment of resignation

In May this year a great number of telecom operators gathered in Lisbon, the city of Fado, to discuss telecom fraud. Organized by the Communications Fraud Control Association (CFCA) and the Forum for International Irregular Network Access (FIINA), operators ranging from AT&T, Vodafone, Korea Telecom to Orange and Deutsche Telekom shed light on how old and newer forms of fraud are detected and combatted.  As the Fado lyrics often deal with the life of the heart-broken and days past, telecom fraud nowadays deals with the whereabouts of criminals, increasingly organized crime. And as telecom fraud becomes more organized and sophisticated, so need the tools and methods for detecting and combatting current and future practices.

Too often, telecom organizations are working in a reactive approach where responsible fraud managers act as a plumber: they look out for anomalies in traffic, literally the ‘odd ones out’ - traffic to national and international suspect destinations and number ranges and traffic coming from new, potentially ‘risky’ applicants – before they try to repair the leaks. This is typically done by patching the firewall, maintaining static “black lists” and looking for illegal simboxes.

But looking at the individual cases, do they see the bigger picture? Aren’t there too many ‘false positives’ clogging up too many scarce resources. Are relations between fraud cases and fraudsters visible?  And what about the fraud schemes that are not solely or not at all call related? The possible connections between external fraudsters and internal employees? Supplier fraud? Subscription fraud? And the hidden cases? The false negatives, the cases that stay below the radar?

Fraud is big business in telecoms

The communications industry ranks in the top 5 industries most threatened by economic crime according to a recent PWC survey (Global Economic Crime Survey 2016).  It’s ahead of sectors such as insurance, manufacturing, energies, utilities and construction, and ranks straight after banking, public, media and transport/logistics.  Interestingly a great number of operators are eyeing up banking and media/entertainment companies and broaden their activities, thereby increasing their exposure to economic crime, fraud, money laundering etcetera substantially. Currently operators lose a whopping 38 billion US dollar to fraud, according to last year’s CFCA survey. Top methods are typically (IP)PBX hacking, subscription application fraud, dealer fraud and subscription identity fraud. Not only traditional telecom is affected: also cable/satellite/IP TV providers are targeted by fraudsters with unlawful card sharing and illegal streaming. Even in a small country as the Netherlands with 7.4 million households roughly EUR 14 million is lost annually due to illegal TV viewing, according to the Dutch cable association.

And as cybercrime has climbed up the ladder of fraud methods according to the PWC research, we can expect this to become a new category soon in telecoms as well. An example in case is a hack in the dealer system of a European carrier where prepaid cards were switched to postpaid, thereby enabling the fraudsters to run up high bills without paying for them. Calls were made to so-called International Revenue Share Fraud numbers, nowadays openly available and traded on the internet. Kick-back and revenue share models enable all criminals involved, home and abroad, to take their unfair share from these scams, leaving customers and often operators to foot the bill. Sometimes internal resources can be implicated: employees committing unauthorized conversions from prepaid to postpaid in the HLR or suppressing billing Call Detail Records or even call agents implicated in unduly complaints. A fresh KPMG research on fraudster profiles found that groups of fraudsters very often comprise people both inside and outside the company. Sixty-one percent of colluders are either not employees of the company, or are employees who work with people who aren’t. Some of them are former employees. This highlights the need for better third-party due diligence of such persons as vendors and customers. Another fraud form, related to vendors (and customers) are VAT fraud, or so-called Missing Trader Intra-community (MTIC) fraud which is also prevalent in telecoms, especially regarding handsets and CPE. Also malware, enabled via unsolicited messages and installed on a mobile phone, can do the trick. With Voice over LTE (VoLTE) mobiles are even more exposed to fraud because signaling is implemented in the mobile OS instead of mobile-based broadband, as for 2G/3G telephony. Many of these vulnerabilities can then actually be exploited remotely through mobile malware.

Moreover, as the number of new, connected networks grow and the number of devices explode with the advent of the ‘Internet of Things’, new forms of economic crime will emerge. We already know of hacking connected ‘things’, whereby running up phone bills is only one part of the damage done. What about criminals hacking into your smart home, as your customer forgot to change the default password settings? Reputational and brand damage can be often much bigger. Maybe operators, at least some, want to exude ‘security’ and ‘trust’ as part of their brand image and core values – just like banks do. And increasingly critical private and business customers expect their telecom provider to protect them from harm done.

Analytics coming to the rescue

Traditional, often rule-based investigations can only detect so-much of fraud. As KPMG concludes in its fraudster profiles report: “The key anti-fraud technology is data analytics, a tool that can sift through millions of transactions, looking for suspicious items. But only 3 percent used pro-active anti-fraud data analytics in detection of the fraudsters surveyed.”

Data analytics can go much deeper, look beyond the obvious. Analytics is an encompassing and multidimensional field that uses a combination of mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns and knowledge in recorded data. This can be internal data, but also external data sources, such as Chamber of Commerce information, social media, or (IRFS) websites. Adding powerful and cheap processing and storage methods, such as Hadoop, for analyzing increasing amounts of data and running sophisticated software algorithms – producing the fast insights needed to make fact-based decisions. By putting the science of numbers, data and analytical discovery to detect and combat fraud, we can find out if what we often assume or believe is really true. See hidden patterns. More specifically analytics can help with:

  • Increase efficiency by reducing wasted time spend on investigating false-positive alerts. A sophisticated fraud scoring engine applies risk- and value-based scoring models to prioritize events before they go to investigators. With the time saved, investigators can work many times the number of cases and focus on higher-value networks. Moreover, the outcome of the investigation is sent back to the system in order to continuously increase the detection accuracy.
  • Spot more suspicious activity by processing all data (not just a sample) through analytical models in batch or if needed in near-real time as data streams in. Also bringing in new, external data sources with structured and unstructured data (even text-analytics).
  • Detect also first and third party fraud networks and crime rings that would otherwise be missed. Also automatically identify suspicious networked behavior in the data. Identify early risky applicants and transactions.
  • Increase the collaboration between the fraud & marketing and finance/credit risk departments thanks to efficient customer screening and scoring. Without interfering with the customer journey, the analytical models can prevent losing money and high-end devices to fraudsters who should not become customers. Or use different offerings to different customers depending on their risk profile.

An encompassing fraud framework

One of the challenges for combatting telecom fraud is dealing with its many forms - TM Forum has made a classification which, ideally, is tackled from one framework, instead of using different tools for different forms.

Picture 1

Most telecom operators have a classical Fraud Management System that mainly focusses on call related frauds such as IRSF, PBX and other frauds.  When new fraud modus operandi become more important, the fraud investigators are often not able to detect these in time.

Therefore, it is important to have a Fraud Framework that enables you to cover all types of fraud in a flexible way and works as an Enterprise Platform.  The usage of analytics is certainly a key factor in order to fire the alerts but there are also other layers within the framework that are equally important:

Picture 2

  1. Integration, quality and management of data: The telecom operators are facing a high volume of data internally (CDR, customer data, etc.) but should also leverage external data, structured (lists, demography statistics, etc.) and unstructured (social media information, web crawls, etc.). All this data should be brought together which will require a system that can access all these sources transparently and which can also handle the data cleansing in order to make sure that the data quality is optimal.
  2. Detection: Using a hybrid analytical approach (combination of business rules, anomaly detection, predictive modeling and social network analysis) in a white-boxed approach will enable not only to detect all types of fraud but will also highly decrease the false positives whilst increasing the hit rate.
  3. Alerts qualification and investigation: Alerts should be shown in an understandable manner to the fraud investigators, giving them the opportunity to visualize and prioritize the alerts following their internal processes and needs. For each of the alerts, the investigator needs to clearly understand the reasons of the alerts in order to ease and shorten the investigation time and process. After investigation, the outcome will be sent back to the system in order to increase the detection logic (feedback loop).
  4. Monitoring and ad hoc analysis: the follow-up and pilotage of the fraud information can be used by the general management to determine the future fraud prevention approach but also to fine-tune long term strategies in different domains (fraud, marketing, risk, etc.).
  5. High performance and real-time technology: In order to avoid that fraud losses explode quickly, the fraud framework has to work (near) real time. This will also prevent that the fraud detection interferes in the customer journey of new and existing customers.

As a conclusion, it seems obvious that the telecom business is still very lucrative for fraudsters. Fraud losses are still not under control and the fraudsters continuously find new ways to increase their profit.

Nevertheless, the new technologies and the usage of advanced analytics can help the operators to stop this phenomenon.  Other sectors such as the banking sector have already embraced these methodologies with success and although not obvious, there are a lot of best practices that could be copied and applied similarly to the telecom sector. But that is another story…


Matthieu Joosten is Telecom Industry Lead at SAS South West Europe

Frédéric Hennequin is Senior Solution Specialist Fraud at SAS Belux

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Telecom Fraud: What Operators can learn from Banks

By Matthieu Joosten & Frédéric Hennequin

Telecom operators loose annually up to a staggering $40 billion on fraud. And with the advent of the Internet of (Insecure) Things (IoT), extending connectivity far beyond smartphones and tablets, our homes, cars, clothes and lightbulbs can become little Trojan horses, vulnerable to security and fraud exploits. So how should the telecom industry tackle current and future scams? One learning is broadening the scope of fraud management, going beyond traditional blacklisting and reactive traffic anomalies. Using an encompassing, enterprise-wide Fraud Framework based on advanced and predictive analytics, detecting risk and fraud before it happens, as we argued in our previous fraud blog.

Now we concentrate on what telecoms can learn from other sectors which are hit equally hard, or even harder, by fraud and have progressed in detecting and combatting it. Banking is one such industry.

Financial industry hardest hit

Following PwC’s 2016 Global Economic Crime Survey, the Financial Services industry is most at risk regarding fraud, slightly more than Government and Retail but way ahead of Communications & Insurance. The Financial Services industry is not only at risk, it’s also been heavily affected and has faced material losses, certainly since the era of online banking (remarkably, a number of telecom operators are moving themselves into the banking industry).
Together with the facilitation of payments and increased use of credit cards, fraudsters have found new and easy ways to steal money and many banks have already suffered vast losses within the last 10 years. When you look at which banks have been targeted, the fraudsters, increasingly organized criminals, started with the big banks in big countries. After that they targeted big banks in smaller countries before targeting smaller banks everywhere. Why?  Because fraudsters are smart people (often with university degrees) and they adapt their strategy to the market.  When one builds a defense wall, they find another victim without defense instead of losing time finding a way to bypass the enhanced security.
What are the main fraud modus operandi the financial sector has been suffering the last years?

  • eFraud Transaction Fraud via mule accounts using phishing/vishing/malware/etc. techniques: online bank accounts from customers are hacked and money is transferred to mule accounts – similar to what happens in telecoms with ID fraud, malware etc.
  • Application Fraud (again, similar to telecoms): customers asking for a credit/loan (phones, set-top boxes) and disappear with the money without reimbursing the bank (or telecom provider).

How to combat banking fraud in the digital age?

Initially, banks mainly worked with simple rules where thresholds were defined above which an alert was triggered – similar to what telecom operators are doing nowadays. As fraudsters became more sophisticated, it never took long to know how to avoid these thresholds and stay under the radar. In addition, fraudsters often know that bank fraud systems rarely monitored customer behavior across multiple accounts, channels and systems. That vulnerability paved the way for cross-channel fraud,  which enables criminals to gain access to customer information in one channel, then use it to commit fraud in another channel.
Putting harm to injury, banks also had to move into the digital age. Digitally demanding, fast-paced consumers expect to be able to make easy and fast payments in real time, at any time and from any place and device. Digital channels, however, are innately more vulnerable to fraud, and, while the speed and openness of the approach made banking faster and more convenient for customers, it also made it easier for fraudsters to access money and transfer it quickly without being detected until after the crime.

Analytics coming to the rescue

Clearly banks had to take a more sophisticated approach to fraud detection and many banks moved to a state of constant readiness. Careful data monitoring and management is critical from the outset, and banks are now often, where necessary, enhancing their data quality and collating and linking a wide range of different data types ingested into an organization, including finan­cial and non-financial transactions, customer information, bank account details, computer IP addresses, and information about devices and their usage patterns.
Using these different data sources and types, many banks have started applying more advanced analytics and machine learning. And in order to be really efficient and effective, they combine multiple techniques consisting of anomaly detection, peer group analysis, text mining, (social) network analysis & predictive modeling to get answers to questions like:

  • Is the beneficiary living in a country at risk?
  • Is this beneficiary living in a country to which the customer already sent money?
  • What is the usual timing at which the customer does his transactions?
  • What is the usual device that’s used for those transactions?
  • Have other customers with similar characteristics paid off their loans in time?
  • Does this customer who is applying for a loan have contacts in his network who are already known for application fraud?

By combining all these analytical approaches, banks are increasingly capable to:

  • Avoid future fraud losses for existing and new modus operandi
  • Maintain the manual workload of the fraud investigators at a manageable level
  • Increase customer satisfaction and avoid customer annoyance during the customer journey

Moreover, banks, like telecom operators, often took (and some still take) a siloed approach to addressing fraud – check fraud is handled by one group, credit card fraud by another. So over the past years many banks have taken a single, holistic platform approach independent of product, channel or geography to address all financial crime, including money laundering. Cost savings and efficiency improvements are also drivers: reducing data storage costs, enabling reuse across departments and increased flexibility to add new products, services and channels to the enterprise platform at a far lower incremental cost than installing another customized fraud detection system.

But what about telecom operators?

All telecom operators are suffering fraud losses in various modes (IRSF, application fraud for high-end devices, etc.). Most have static and reactive detection systems in place where leaks are plumbed as they are detected (see our previous blog post on telecom fraud).

So it’s time for telecoms to “get inspired” by the advanced fraud detection models that exist within the banking industry (certainly as some of them are becoming banks!) and start getting answers to questions like:

  • Is the destination number located in a country at risk?
  • Is the destination number in a country to which the customer has already called in the past?
  • What’s the usual device the customer uses and what’s the usual time at which the customer performs his calls/messages/data usage?
  • Have other customers with similar profiles asked for a similar high-end device?
  • Is a customer, applicant, dealer, distributor, call-agent or even supplier connected to fraud, and if so, how and does that constitute a risk?
  • How can I decrease the number of ‘false positives’ and increase efficiency?

If telecom operators invest time and effort in more advanced fraud detection techniques they will avoid losing money to customers and companies they shouldn’t have accepted in the first place and will be able to reward customers who should get their full attention.
Want to read more? Read about the SAS Fraud Framework or download our whitepapers: ‘Using Analytics to Proactively Detect Insider Threats’ or ‘Addressing Fraudulent Payment Activity
Another good read is: ‘Protecting the Enterprise’
For any questions, don’t hesitate to reach out to us.

Matthieu Joosten is Telecom Industry Lead at SAS South West Europe & Frédéric Hennequin is Senior Solution Specialist Fraud at SAS Belux

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How a simple blog performance dashboard motivates authors

As SAS, we are fortunate to work with some of the brightest analytics consultants in the world. Most of them are happy to share their ideas with the extended community, to inspire and encourage development of new use cases and improve analytics performance. Presentations at SAS conferences, ideas in SAS insights and blog articles are just some examples of how these ideas are being shared.

As digital marketers, my colleagues and I are able to leverage these ideas in our campaigns. Like so many companies these days, we want to be a ‘digital organization’, and also be data-driven. To become a fully-fledged data-driven digital marketing organization, we need to be active online and the entire organization needs to be involved. To be truly data-driven, we also need to be able to make sense of our activity and its outcomes.

One way to do this is via a dashboard, which is why we are in the process of developing one now.

 Blogs dashboard article header image

Impact and motivation

One particular section of our new dashboard will show the impact of our bloggers. Bloggers are spread through the organization: across countries, geos, and departments. Everyone here is skilled and knowledgeable, so anyone can blog, and share their knowledge about their area of expertise. We are always on the lookout for more company bloggers.

We run programs to encourage staff to become more social and digital, and develop into online thought leaders, sharing their ideas and vision. But only by tracking and measuring, and being data-driven, can we assess the impact of this activity. It is vital that we know what works so we can improve our outreach, build on good practice, and get closer to becoming a truly digital marketing organization.

A dashboard will also encourage competition among bloggers, and motivate them to improve. The dashboard will show bloggers their own performance compared to others, with a list of the top 10 posts and authors. We hope that the challenge of working out how to feature in the top 10 will encourage bloggers to ask digital/social leads in the company what they can do to perform better and to write better – in other words, to improve their impact.

Once we add the number of clicks, engagement levels, and leads generated from each blog post, the competition will really heat up. Being able to show the business impact of our digital activities makes their importance very obvious, and encourages bloggers to make even more effort to improve.

Top 10 Authors



With SAS being the acknowledged leader in analytics, we have used SAS Visual Analytics for the dashboard. The great thing is that there is a lot of blog data available and we do not have to wait for IT to prepare data sets. Instead, we can create our own reports, so that business intelligence is fully integrated into the business.

What the digital marketers and bloggers see is an interactive dashboard that is easy to use. They can set filters to see subsets of the results and enable them to drill down further. In the future, we hope to enable the spearheads to get involved in data exploration, so that they can analyze their own activity and increase their impact. They may even want to explore data from other bloggers and do some benchmarking to improve their own performance.

Views per month


Connect with customers and prospects

Our blogs help us to connect with customers and prospects, and anyone else interested in analytics in the broadest sense of the word. Their responses to our blogs provide even more, and smarter, data. By adding this to the dashboard, digital marketers can analyze which topics are resonating in the market, which blog posts get the most engagement, and much more. This in turn helps us to design more relevant campaigns, and generate more engagement, and closer relationships with customers. This, in turn, of course, results in still more data.

Truly digital and data-driven

The new dashboard will enable us to make better decisions, faster, and become more relevant for our customers and prospects. Using data to improve our (online) marketing campaigns moves us closer to becoming not just a digital, but a truly data-driven organization.

Do you have a similar dashboard in place or are you setting one up? I would love to hear from you – just send me a tweet or direct message!

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Diverse use cases drive booming transport IoT investments

The travel, transportation and hospitality sector is a big spender on the Internet of Things (IoT), spending an estimated $130 million on it last year. So what exactly are the use cases driving these investments?


Self-driving trucks: Improving fuel efficiency and safety via ‘platooning' - In April 2016, over ten ‘self-driving’ trucks arrived in Rotterdam as part of an initiative to demonstrate ‘platooning’: The movement of semi-automated trucks in a convoy. This system enables trucks to drive closer together, and respond to each other and the lead truck automatically. Manufacturers including Scania, Iveco, DAF, Daimler and Volvo claim that it will improve fuel efficiency and safety.

Connected rail systems to improve efficiency and staff safety - Cisco is working with rail operators to provide train and track monitoring systems to improve safety of both staff and passengers. Cisco has a contract with the UK’s Network Rail Telecoms to update its telecoms infrastructure and support full remote monitoring and maintenance. It also offers a rail collision avoidance system which has been commercially available for several years and is being piloted in several different countries.

Connected aircraft: Improving fuel efficiency and maintenance - Large numbers of parts of the Boeing 787 and Airbus 380 are highly connected. Individual parts send data about their state and condition, alerting ground staff to potential problems while the plane is in flight. The new part can be waiting on the ground when the plane lands for immediate replacement. This will both reduce maintenance time and make flying safer.

Improving fleet operations with real-time monitoring of fuel levels - Marzam Corporation provides services for ship owners and operators. A recent investment in two large fuel tanks has been enhanced by the addition of IoT technology allowing it to monitor fuel levels in real time and ensure that fuel is always available. Monitoring has also reduced expensive emergency refilling, and allowed it review consumption patterns.

Improving logistics at transport hubs: smart ports - The Port of Hamburg uses IoT technology to improve both management of cargo and ships through the port, and also traffic flow to and around the port. Connected systems advise drivers of likely bridge and road closures related to ship traffic and highlight alternative routes to reduce congestion. It has been able to handle larger volumes of cargo as a result.

Improving supply chain monitoring  via RFID and IoT technology - RFID and IoT technology make the movement of goods more visible and enable companies to identify when perishable goods are being kept in unsuitable conditions in time to rectify the problem. Kuehne and Nagel, for example, use IoT technology particularly for their pharma business, where temperature control is crucial to product viability.

Revolutionising hotel check-in, check-out and room keys - Hilton Group now allows its customers to check in and select their room from a digital floor plan, then customise their stay by requesting upgrades, using a mobile app. Guests can also check out using the app. Hilton also expects customers to be able to use their smartphone as a hotel key in the majority of its hotel rooms by the end of this year.

Smart luggage: making lost and damaged luggage a thing of the past - BlueSmart, crowdfunded via Indiegogo, launched its new ‘connected suitcase’ at the end of 2015. It is a carry-on bag that will check its own weight and allow you to charge your phone using an integral port. You can also locate it via an inbuilt GPS function. Delsey is due to launch a similar range later this year, called Pluggage. It will have inbuilt fingerprint-linked locking and unlocking, and ability to check whether the bag is on board, and has been tampered with in transit via a smartphone app.

Concierge service for connected travellersPlanet Traveller’s Space Case 1, currently in development, will have all the same features as other smart luggage, and one extra: a personal concierge service to check flight information and hotel reservations.

Luggage trackers
- A simpler solution to the lost luggage issue is a baggage tracker inside your suitcase, such as Lugloc or Trakdot. Lugloc uses a GSM network connection, to avoid poor GPS/mobile connectivity underground or in flight, and Trakdot uses GPS technology connected to a mobile app. Trakdot cannot tell you whether it is on board the plane, because the wireless technology switches off under those circumstances, but at least you can find out where your suitcase is in the world once you have landed.

Data management and analytics play a critical role in transforming IoT streams into business value. If you are part of the travel and transport community, and have a neat use case, I’d like to hear from you.
In the meantime, watch a real-world example on safer transportation and see how trucking companies use IoT to make operations safer, more efficient and economical. I can also recommend the TDWI report, Four Use Cases Show Real-World Impact of IoT, to learn how intelligent transport solutions speed up traffic flows, reduce fuel consumption, prioritize vehicle repair schedules and save lives.
Our experts will be discussing this topic further at the #saschat on Twitter on 12th August from 15hrc CEST - we look forward to seeing you there!

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Data science skills for your IoT programme success

Over the past 10 weeks, our experts have been in conversation with Internet of Things (IoT) deployment leads to understand critical success factors and challenges. One major finding stands out. Over and over again, we heard the same message: projects are at risk because of a shortage of data science skills.

Organisations simply cannot find the data management and analytics skills. They need to take advantage of their IoT deployments. Analytics is a key part of exploiting their new resource, and there is a worldwide shortage of data scientists. But it doesn’t have to be this way.

In our view, there are three ways that organisations can acquire the skills that they need to seize the moment: they can build, borrow or buy.

Data Science

Building competence

This is perhaps the most obvious: customers can develop their own staff, to ensure that they have the skills that the company needs. But while most of those interviewed could articulate the skills that they needed, very few were able to suggest how they could support their staff to develop those skills. Data science is not something that is widely taught as yet.

It is precisely to address this need that back in April this year, we launched the Academy for Data Science. It provides certified training for data scientists. Our customers and others can use this academy to create an in-house development programme for their staff, to provide skills tailored to the company’s need. The six-week training modules include theory plus case studies or team projects, coaching and an exam to achieve certification. Although the course focuses on SAS packages, trainees emerge with a general certificate in data science.

Borrowing skills

The second option is to borrow the skills from elsewhere. We found that a number of companies surveyed had gone down this route, forming partnerships with firms that had the necessary skills in developing and handling IoT technology. Professional services firms like SAS have access to experts in analytics and data handling. What’s more, these firms ensure that their experts’ knowledge is kept up-to-date by regular training and professional development. It is a time-effective way of ensuring access to the latest skills and technology.

Of course the big drawback of ‘borrowing’ skills is that eventually you have to pay back the loan, as it were. The borrowed experts have to leave and move on to somewhere else. But a combination of borrowing and building can pay off in the longer term. Borrowing fills the immediate skills gap, and building then starts to take over. What’s more, ‘home-grown’ staff developing their skills through training and certification programmes can work alongside borrowed consultants for a while, shadowing them and learn ‘on-the-job’ as well as during their training sessions.

Buying into capabilities

The final option is to buy. This could be in the form of recruiting skilled data scientists who will be ready immediately. The problem with that, though, is that they are few of them. There are not enough to go round, and anyone wanting to recruit has to consider what they have to offer to attract and retain this rare breed.

Fortunately, there are other options: to buy pre-configured solutions that reduce the need for manual intervention by skilled professionals.  There are several ways to do this:

  • a plug and play module
  • a more dynamic data lab
  • an online ‘cloud’ analytics-as-a-service capability.

For example, the Analytics Fast Track™ for SAS® (AFT) is a‘plug-and-play’ analytics module designed to enable businesses to get value out of it fast. The idea is that business simply turn on, add data, and start to benefit immediately. Built in partnership with Intel, this suitcase styled configuration comes pre-configured and ready to go.

For teams who need to support many and as yet unknown projects, a big data lab might be a better option.  This is an environment designed for experimentation and ‘fast failure’, and to generate value from a very early stage. It comes with requisite data science support elements so teams are not stuck at any point.

The ultimate in the ‘buy’ spectrum is a fully functional cloud capability. SAS Viya™ for example provides high-performance cloud-based visualization package designed to be accessible and scalable for individual business users. It is suitable for any analytic challenge, large or small, and also helps with technology integration.

The hybrid model will be the most sustainable for Data Science

Immediate skills shortages do not need to hold anyone back from exploiting IoT opportunities. Whether you decide to build, buy or borrow, or some combination, there are options out there. What will you choose? We can help you in that: join us on Friday 5th August for a discussion on analytics skills evolution. This is an open discussion on Twitter, and no registration is requires. Just follow the #saschat hashtag which will be most active between 15 and 16 hrs CEST.

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Better Analytics Today, Better AI Tomorrow, Starts With Summer Reading

‘We are entering a third era of automation, in which machines encroach on decision-making. But there is still a role for wetware.’ - Tom Davenport, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (2016)

As we work day in and day out using analytics to drive innovation in our organizations and in society, I think it’s essential that we realize our data science today is very much shaping the way we will live and work with machines in the future. That’s why I was very happy to see how Only Humans Need Apply: Winners and Losers in the Age of Smart Machines emphasizes how we need to be conscious of the current artificial intelligence revolution and really work to make sure human intelligence remains complementary.

“Oh fantastic,” I hear you sigh, “Another ‘must-read’ to add to the list. Because I’ve just got so much free time to read these days.” Well, that being the case, I still recommend you make some time for this one during whatever summer holiday you manage to carve out and….watch out…I’m going to add a few more essential whitepapers to your list a bit further on.

From Stephen Hawking to Bill Gates through to Elon Musk, big brains have highlighted both the potential, as well as the risk of Artificial Intelligence. Logical then that Tom Davenport contributes a ruthlessly pragmatic, but still fun to read, account of what we as knowledge workers can do now, not only to keep up (and keep our jobs), but also to ensure that ‘augmentation’ (the idea that human intelligence plus artificial intelligence needs to be more than the sum of the individual parts) actually occurs.

This isn’t the first time Davenport has treated a hot tech topic to make it digestible. Davenport’s milestone book, Competing on Analytics is now ten years old! When the book came out in 2006, data mining was still very much happening in dusty corners of the organization, sometimes leading to strategic insights to influence strategic decisions, but mostly becoming watered down when it came to influencing actual operational decisions. With a mission to bring analytics into the corporate mainstream and help make data science a boardroom concern, SAS even organized some very successful events with Tom here in Belgium.

The new book made me reflect on where things stand today. While analytics HAVE become an essential part of doing business, there is still a need to continually innovate as a competitive weapon. Today, there is more of everything (data, computing power, business questions, risks and most importantly, analytics consumers). The ability to scale your organizational analytical power is more essential than ever for staying ahead of your competition. And at the same time, in order to balance both the potential and risks of artificial intelligence, we all need to step up our game. Computers can and will do more intelligent work, but we also need long-term thinking about what and how we teach intelligent machines.

So with equal attention for what’s good for organizations today, as well as for planning the artificial intelligence augmentation of tomorrow, I wanted to bring attention to a few recent SAS whitepapers which address some key topics for meeting the growing demand for analytics.

Reading these whitepapers will give concrete suggestions about making your analytics scalable to drive more efficient decision making. While reading them though, I suggest trying to also think about how these analytic process lifecycles need to be enriched with human insights.

  • How do we develop models not only in line with organizational strategy, but with the organizational conscience?
  • When establishing data labs for building predictive models, how do we think in terms of correct model behaviors, instead of correct model outcomes?
  • When engaging machine learning, how do we ensure that such learning is holistic, not just maximizing expected utility from a logical perspective, but fully humanist?

Heady topics, but I’m convinced they are essential considerations if we as data scientists are to make positive contributions to artificial intelligence evolution. I’d be interested in hearing anyone’s views on this.

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Analytics in action – the world (domination) is not enough

I’m sure you’ve heard of Blofeld, Goldfinger and Dr No, right? Well these evil villains and adversaries of Her Majesty’s most famous secret agent had one thing in common: their devious plans for world domination, complete with all the facts and figures, always featured on oversized screens.

Analytics in action - image

Even if world domination doesn’t feature in your business model, visualising your plans can still be helpful when on the hunt for new ideas. In fact, this approach works equally well in meetings with colleagues and management alike.

Visualisation is the common language that unites the different roles within a company. Aside from the silver screen, data visualisation is no longer restricted to just the big global players; it requires a strong level of analysis, which even medium-sized companies can use to find trends in their data. In doing so, they are able to uncover new revenue potential, improve their services, anticipate issues and thereby gain a competitive edge.

The advantages of having an edge afforded by digitisation and globalisation include close customer relationships and loyalty. But needless to say, having information available so quickly and readily has made customers more demanding than ever.

Speed and quality are of paramount importance to customer satisfaction, which has been demonstrated in a number of studies. This means that 80% of unsatisfied customers will permanently walk away from a company that doesn’t respond quickly enough.

Information is key to improving customer relationships. It can be provided through a variety of different sources and sectors, including emails, phone calls, social networks and even surveys.

Structured and unstructured text data offers valuable insights if it is focused on a particular outlook or existing activity (such as resignation) in the right context.

Companies are often lacking not only in the analytical tools required to evaluate the available data, but also in the employees capable of using them. Data scientists are expensive and hard to find. With this in mind, an alternative option of providing end users within specialist departments with the necessary power requires an analytical approach. In most cases, they quickly come to the relevant conclusions on their own, and can then work side by side with the existing data scientists.

This is where SAS offers a smart introduction to analysing text data (without any prior changes being made), which can be performed securely and directly in the office environment without the need for statisticians or data scientists.

Not everyone can rely on Q for technical and analytical support, but plenty of people have the intelligence and strong power of deduction to handle this.

Here’s an example of how text data in Excel can be transformed into a word cloud at the click of a button, highlighting initial trends across the files.

Highlight the text data in Excel …

excel sheet - blogpost

… and generate an initial overview of the content at the touch of a button.

VA report - blogpost

More wonderful and inspiring insights and best practices of data visualization can be found in a new data visualization e-book.

I’d love to hear from you on this blog, so please feel free to leave comments.

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Long live the IoT hype!

Long live the IoT hype!

Why Gartner is "wrong" and the Internet of things hype won’t drop.

Internet of things, IoT, connected cars, Industry 4.0, Insurance 4.0, smart factories, smart homes, smart cities, smart police, smart banking, smart grid, smart … where will it end? If there’s one thing the Internet of things isn’t short of, it’s names. A sure-fire way to tell that marketing bods the world over have most definitely jumped on the bandwagon of this hot topic. Expectations have reached epic proportions. Technologies and infrastructures are still in their early phases of development. Processes and companies are not yet ready. And if dreams and reality get too far apart, this kind of disillusionment can only set us up for a fall. This is what Gartner describes in its Emerging Technology Hype Cycles, and exactly what the 2015 Hype Cycle predicts for the Internet of things.

This is where Gartner is wrong – and yet right on the money.

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