Author Archives: TCSET

Standard Chartered: Creating a Golden Source of Financial Data to Continue Being, “Here for Good”

March 20, 2018

This story is all about numbers! And why wouldn’t it be? It’s the financial transformation of a global bank with revenues of nearly $20B annually. (That’s just the first number.)  Standard Chartered is more than 150 years old, operating in over 70 countries with 1700 branches.  Now that you have the lay of the land, check out what Standard Chartered has accomplished.  The bank can perform up to 1B open trades a day with 80M transactional processing points. They are now integrating those 600 source systems with financial data to create a ‘golden source’ that allows them to reconcile in just 60 minutes.

Screen Shot 2018-03-15 at 10.47.48 AMThe team from Standard Chartered embarked on “Project Rubicon,” a direct reference to the ancient river where Julius Caesar passed the ‘point of no return.’  With the results Standard Chartered has achieved, they don’t want to go back!  Specifically, “Project Rubicon” is an accounting hub that enables a bank-wide “golden source” of financial data. The accounting hub generates summarized accounting postings based on centralized business accounting rules and posts it to the GL, retaining detail to trace back to every transaction. This provides a much stronger foundation for daily reporting than traditional data warehousing where transaction data and accounting data are both sourced independently but unable to be fully integrated because the linkage detail is often not retained by the Transaction Processing (TP) systems and the ledger only has the summary postings. When you add adjustments, allocations of revenue & expenses, along with changing organization structures to the mix to then drill-down and drill-across, functionality is often impossible using just a traditional data warehouse, but with an accounting hub-driven data warehouse on the Teradata IntelliFlex™, it is both feasible and straight-forward to implement.

What does this do? The ability to reconcile and report quickly in every country Standard Chartered operates is truly game-changing. Not just in productivity, but also in compliance.

“We can comply with all of the regulatory requirements. We have the data available at our fingertips if the regulators ask for it. We can aggregate client Screen Shot 2018-03-15 at 10.46.28 AMdata. We can do straight through processing. The power of the analytics of having all of these transactions in one place at the same time, that’s reliable. It gives me the power. Having that transaction data set that is reconciled, you know, the limitations are endless. There’s no limitation because the first thing you could probably do is put some extended intelligence over that data set.” – Wayne Mendham, Global Head, Strategic Initiatives & Client Investor Banking

All of this give more insight into risk and potential fraud to make more informed decisions.

“We’re a bank. We take risk. It doesn’t reduce risk. You understand the risk. And then by understanding the risk, you can take a decision what you do with that risk. You can actually keep it or mitigate it. So that’s a different concept from a corporate perspective. Banks take risk because we actually take the pot of money, then we lend that money. We actually have in the middle, a risk which is called credit risk. Therefore, we do take risk, but it’s actually better understanding that risk is what you do in a bank, not mitigate it. The decision is making on what risk you do have. That’s the difference. “ Wayne Mendham, Global Head, Strategic Initiatives & Client Investor Banking

Alongside Standard Chartered data scientists was Think Big Analytics to design and implement, architect and advise throughout the entire project, because no one wants to cross the Rubicon by themselves.

“It’s such a big implementation and there were challenges. A lot of the challenges had to do with the vision as opposed to the outcome. We’ve come a long way and obviously they’re partners with this.” Wayne Mendham, Global Head, Strategic Initiatives & Client Investor Banking

Congratulations to Standard Chartered for “crossing the Rubicon” and finding success!

 

STC: Putting the Customer at the Center of Their Analytics & Data Strategy to Earn Customer Trust

March 2, 2018


How does a digital, self-service, automated experience increase CSAT, boost revenue, and decrease churn in the highly competitive, historically fickle telco industry? Great question!  For its answer, look no further.  STC is using sophisticated analytics and data to improve their customer’s experience and understand the customer’s journey.  And they’ve done such a good job! STC is dominating with over 54M subscribers that’s more than 60% market share, despite being the legacy player in Saudi Arabia.  And STC’s success also supports the Kingdom of Saudi Arabia fulfill its vision for all citizens to have access to the internet by 2030.

“At STC, we link customer experience to the digital transformation.  By improving customer experience, based on research, if you go more digital, more self-service, automated activities, customers are happier and more satisfied.  By reaching this digitalization, this is where we improve the customer experience, improve the satisfaction, and at the same time we reach the goal of being more digital, more innovative in our offerings for our customers.” Raafat Mounla, Director of Customer Experience, Program Design and PerformanceScreen Shot 2018-03-02 at 1.32.54 PM

Spanning 9 countries on 3 continents, STC segments their customers based on behaviors and then optimizes experiences for each group and, using a lifecycle management strategy, identifies customers at risk of churn. STC is laser focused to serve each segment as one team and gives everyone who needs it access to the data and analytics.

Meeting customer needs and mitigating churn requires world-class analytics and a unified data architecture. STC is utilizing the Teradata Unified Data Architecture ™, specifically the Teradata® Database, open source Hadoop and R, with access to a variety of structured, unstructured and multi-structured sources and customer touch points. This includes:

  • Billing
  • Complaints
  • Network experience
  • Pricing and tariff plans
  • Competitive offers
  • Sentiment analysis

What are the results so far?  Revenue per day has grown from $267,000 to $400,000 (USD).  And that’s not all:

  • Call Center Service Level went from 30% to 90%
  • Customer Satisfaction Scores (CSAT) increased from 55% to 80%
  • “My STC” Mobile App CSAT increased from 75% to 85%
  • Incoming Call Center calls were reduced by 56%
  • Paperless bills are now above 98%

Automation has been critical to their success.  When the Teradata analytical platform detects a trend in sentiment, negative network performance, customer billing complaints or troubleshooting a new feature, STC quickly responds via text and ultimately automates the remedy and the communication to its customers reducing operational costs for call centers and decreased calls by 70% in four years.

“We have bots answering the queries, so knowing what the customer is going to ask.  We have already automated all the support and the answers, so that you don’t have agents sitting there waiting for customers to call in order to support them.  Automating improves time to service these customers, and at the same time, you will not have the human factor which might affect the customer experience.  From one side, you have the customer happier, they are served in a faster way, and at the same time, as a company, you have your costs being reduced.” Raafat Mounla, Director of Customer Experience, Program Design and Performance

Giving access to the analytics and data with the assistance of Think Big Analytics consultants has been a game-changer for the organization.  Think Big Analytics and STC agree: everyone in the organization who needs access, has access.

“The good thing is that when you give access for top management to the good analytics, to the good quality of data, they will trust your advice and your recommendations.  From the top management all the way to the agents that are working on designing offers or on assessing the performance of a channel or the experience of a customer, everyone has access to the data in a proper way, in a way that they can understand, in a way that they can take the decision upon it, and basically there’s no decision, no service being designed, or no action being taken if not backed up by proper data analysis and understanding of the context and this decision, what could it lead, and where it will improve, and so on.  It’s all around data.  Any decision that we take has to be based on facts, on data.” – Raafat Mounla, Director of Customer Experience, Program Design and Performance

STC is delivering on its vision to be recognized as the information and communication technology (ICT) leader.  They are putting the customer at the center of their analytics and data strategy to earn the customer’s trust and enrich society.  Congrats to STC!

Larry H. Miller Sports & Entertainment: Enriching Lives and Making the World Better with Teradata IntelliCloud™

January 25, 2018


What do NBA basketball and minor league baseball teams, Megaplex theatres, a bicycle race, sports apparel stores and a sports arena have in common? Answer: Larry H. Miller Sports & Entertainment, a diverse group of companies that have now integrated their data to support analytics that are producing business outcomes all over!  How do they benefit each other? The question is really, how well do they benefit each other?

THE PROJECT

Starting with customer loyalty and sales data, LHM Sports & Entertainment leveraged Teradata IntelliCloud™, enabling them to expand retail opportunities between businesses, mitigate risk in the supply chain and enhance fan experiences across venues.  And that is only the beginning!  Results were fast and profitable.

Dustin Spangler VP Data & Analytics

“We started with the retail data model, which if you think about all of the different businesses, they actually do fit in there because it’s a transaction with a transactional line and it’s very similar across the entity. That works really well for us so that we can provide reporting both at a company level and at a corporate level.” – Dustin Spangler, VP of Data & Analytics, Larry H. Miller Sports & Entertainment

One of the challenges for the fan apparel stores is predicting what jersey will be hot and what jersey will not.  Whether it’s teams or players, the demand typically depends on the ‘play’ of those teams or players.

“We’re doing a good job on the forecasts, but the reality is, how do you plan for a player getting traded? How do you plan for merchandise to carry year after year and plan for Kevin Durant to go and sign with the Warriors instead of staying with the Thunder? There’s part of that in the free agency and trades that you’re just going to miss.  And so, how do you mitigate that risk and how do you minimize the order quantities?  In the past, it was really challenging to know how much to buy, and often it meant that you were overstocked on inventory and/or you had a lot of obsolete inventory that you were carrying on the books. That’s where we’ve seen the biggest improvement.  It’s on the limited amount of inventory that we’re having to write off for discount.” – Dustin Spangler, VP of Data & Analytics, Larry H. Miller Sports & Entertainment

LHM Sports & Entertainment are running analytics on real-time sales to determine which player’s athletic gear is selling best before, during and after a game to potentially do dynamic pricing. Wow!

Leveraging the integrated environment is really making a difference for concessions between the different business groups.  Using loyalty program data gives LHM Sports and Entertainment the opportunity to know the consumer and what type of offers will entice the consumer to respond.

“If you’re a frequent moviegoer and you only go on a discount day and you only buy concessions when they’re discounted, then….when you come to the arena to go to a Jazz game, I’m going to assume that you bought a discounted ticket there. We know that you’re going to respond better to an offer that we can push to you in the app to buy concessions and to engage with food and to go buy your drink and your dog during the middle of the game. It also enables us to push you to the right products that are in the team store that’s there at the arena so that you can have a more integrated experience and a more personalized one.” – Dustin Spangler, VP of Data & Analytics, Larry H. Miller Sports & Entertainment

Screen Shot 2018-01-24 at 1.41.28 PMAnd has it paid off, fast!  Business units experienced significant additional revenue every week. And more are coming online allowing for more cross-pollination questions between business units, such as:

 

 

  • Does a customer, living near Megaplex Theatre A, drive to Megaplex Theatre B, 15 miles from his/her home, because they prefer IMAX?
  • Did the opening weekend of a major movie drive down a Jazz game attendance?
  • Did a player’s performance on Saturday night drive more apparel sales through the Fanzz store?

Why move to the cloud? IntelliCloud™ offered exactly what they needed and allowed LHM Sports and Entertainment to get out of the data management business and securely manage all of their business units.  Implementing in only nine months, time to value was quick and the team learned valuable lessons along the way.

“For me, one lesson learned is to not underestimate the networking component of making sure that as you’re talking cloud to cloud, cloud to on premise, that you’ve done a thorough evaluation of your network topology. The other thing, I would say, is on the small and incremental projects.  And last, I would say is focus on business capabilities. Don’t try to deliver the cool technical solution. Try to deliver something that adds value to the business.” – Dustin Spangler, VP of Data & Analytics, Larry H. Miller Sports & Entertainment

Adding value and revenue to the business! Congratulations to Larry H. Miller Sports and Entertainment on all your success in the cloud!

 

 

Qualcomm: Using the Power of the Cloud to Enable Mobile Ecosystems Worldwide

January 10, 2018

How indispensable is your smartphone in your everyday life? Be honest.  Could you go more than a day or two without it?  How about an hour or two?  Whom do you thank for that? Probably a name you know, Qualcomm. Qualcomm connected the smartphone to the internet and that changed everything.

It’s impossible to imagine a smartphone without features Qualcomm made possible. Every time you snap, shop, navigate, stream, download, store something or even just talk, you’ve got the power of Qualcomm technology to thank.  Examples include backlit selfies that showcase image stabilization technology, finding the hottest restaurant on a crowded street even if you have no sense of direction, gorgeous graphics, as well as lightning-fast video streaming and immersive 3D experience.
Qualcomm works closely with the world’s leading network operators – such as China Mobile, Vodafone, Telefónica, AT&T, and Verizon –  to help connect new industries, new services and experiences that are changing every day.   They also work with the manufacturers who design and sell smartphones with Qualcomm chips to better understand how their chips can be designed more efficiently.  This, in turn, allows the service providers to add more phones with newer features!  A win-win-win for Qualcomm, the network operators and smartphone manufacturers.  We could add a fourth ‘win’ here and that is we, the consumer too!

Qualcomm is in partnership with the network operators refining those networks with a renewed culture that relies on;

  1. Sharing and allowing full access to analytics and data;
  2. Sophisticated analytics; and
  3. An innovative ecosystem that relies on data NOT moving.CraigQualcomm

“I have data scientists in our group, and a lot of people with machine learning expertise and everything else, but the biggest impact we’ve had is just sharing information.  Changing from a need-to-know company where people would share data because, ‘I’ll give it to you if you need to know it’, to almost like a need-not-to-know, like, ‘Why can’t I share it?’  And so that was really the driver…to share data across the company. Then the more advanced insights you can get from the machine learning, the data scientists, and everything.  But number one, get people access to as much as possible.” Craig Brown, Senior Director of Technology

 The key to number two above is actually number three.  Okay, we’ll clarify. For Qualcomm, sophisticated analytics depend on the simplicity of the ecosystem.  Eliminating movement and leaving data in its place ensures efficiency.

“The Teradata system used by Qualcomm is currently on AWS. It gave us the benefit of instant access to it. We can just turn it on, right? It gives us the thing where, in our development systems, we can use it in various environments, in various places. It’s not just in our Las Vegas data center…Building a great end-to-end ecosystem that best meets our needs, best meets our existing momentum, or best suits our existing momentum as well as giving us the best results in the future. And it, again, comes down to simplicity. Don’t move data around. And a much bigger thing is that Teradata is more than a Teradata box. Teradata is huge.” – Craig Brown, Senior Director of Technology

But we have buried the lead.  The goal state is really innovation in the chips and the networks that our smartphones rely on and that Qualcomm gets when they add it all together.

Screen Shot 2018-01-10 at 11.09.25 AMPartnering with network operators and manufacturers around the globe, Qualcomm strives for innovation.  Together, they test networks in existing and emerging markets to understand “total network profile.”  There is software running on the phones to determine how they are operating.  With geo-location knowledge, Qualcomm and the network operator collect data off cell phone towers to analyze multi-structured data about handoffs from cell tower to tower.  For example, if you have 100M phones/subscribers with 2B phone calls in a month, you can expect to collect 30 – 40 different types of data including cell phone year, manufacturer, format, service, etc to reveal: ​​​​​​​

  • Where does the network misbehave?
  • Is there an area of the network running much better than expected?
  • Where is the switching station?
  • Do certain phones and technologies perform better with certain cell towers and technologies?
  • How old is the cell tower?  What parts are being used in the cell tower?
  • Where are the anomalies?
  • Can we exploit those cases?

Working alongside consultants from Think Big Analytics, Qualcomm data scientists discovered the key lesson from this engagement was that simple things often work the best.  They found that a manually created decision tree or a simple probability model would solve many cases.  When more advanced techniques were needed, they tended to stick with supervised machine learning approaches such as support vector machines (SVMs) or random forests where the model will indicate the criteria it used to make a decision.  In this particular engagement, deep learning was a last resort for situations where it’s often difficult to show why a decision was made, and thus, more difficult to get adoption.  For example, Qualcomm previously had the goal of clustering cell towers into groups, and this lent itself to an unsupervised approach as well as the use of k-means clustering.

“Now what we can do is we can say, ‘Ah, let’s go and measure what we found here, and let’s go and measure the entire network and see.’ One of the machine learning techniques is where you do unsupervised learning, where you cluster things. And so I’m not teaching the machine what to do; it’s just looking for patterns, and it says, ‘Those things are acting the same way.’ And so the thing I just found, the thing that was playing up, if I can now, across an entire country, say, ‘Where else is it playing up the same way? Go out and try and fix them the same way.’” – Craig Brown, Senior Director of Technology

Congratulations to Qualcomm! Your equation of culture + analytics + ecosystem = business outcomes is your success!

 

Monsanto: Looking to the Cloud to Help Farmers Globally Grow Crops More Efficiently

August 1, 2017

Cloud, cloud, cloud.  If you’re in the analytics and data space, that’s probably been on your radar (or right in your face) for the past few years. Is cloud the real thing or just the CIO’s bright and shiny new object? Global agriculture giant Monsanto knows it is the real thing. With a broader strategy to move more applications to the cloud, they started with disaster recovery and will now start to move engineering and product development initiatives.

“For a lot of our research scientists, they see the Cloud as an opportunity to move data into a place where they can use different tools and different capabilities very quickly.  The administration time is very short.  They can spin up a new data platform and they can start getting right to the analytics very quickly without a lot of administration in the way.  At the same time our finance team wanted to redo and prove the way that we consolidate and close our financials on a monthly basis (see previous video on their finance transformation.) So this larger capital effort was already leveraging the enterprise data warehouse for collecting and consuming all of that data and centralizing it in one place.  They wanted to connect the closed reporting system on top of that warehouse, but we couldn’t do it unless we were able to offer SOX compliance and disaster recovery.” Troy Crites, Global BI Architecture Lead

With a DR data center that was running out of tile space, combined with the desire to exit the DR business altogether and concentrate on their business, the elastic and scalable architecture in the cloud was the perfect option. And, it’s just the start.

The ultimate goal is to take advantage of the high-powered analytics no matter where the data is – meaning anywhere and everywhere.

“Teradata Everywhere™, I think that gives us new opportunities to look at Teradata as not just an enterprise data warehouse platform, but as a vendor and a partner that can help us in a variety of different areas… the digitization of our business, and how we can enable real time solutions……” – Troy Crites, Global BI Architecture Lead

With portable licensing, Monsanto can implement Teradata across flexible deployment options. That is critical as Monsanto moves to Teradata IntelliCloud™ on AWS in the near future, co-locating Teradata in the same cloud as product development and engineering.

One of the biggest concerns for anyone deploying in the cloud is security.  Monsanto is no different and security was paramount. Multiple teams (network, firewall, and routing) worked together with Teradata teams to produce all of the certifications and security protocols.

“Teradata was top notch.  They were able to produce all of the certifications and the security protocols that they follow.  There are a particular number of reports that our security team was looking for, along with the audits.  And Teradata had just recently gone through a facility audit around their cloud facility.  Our team and their team worked hand in hand to feel comfortable.” – Troy Crites, Global BI Architecture Lead

All of this gives Monsanto the opportunity to concentrate on the business they are truly in.

“Monsanto’s mission is to build sustainable agricultural products in order to help our farmers get as much yield as possible while conserving energy and water. In the future as we move into more of a data science space, we’re starting to look at how we can digitize the farmer’s experience and be able to leverage that information in order to increase that yield and offer more sustainable opportunities.” – Troy Crites, Global BI Architecture Lead

Congratulations to Monsanto who is looking to Teradata Everywhere™ to help farmers across the globe grow crops more efficiently, working together for a brighter future.

 

 

Danske Bank: Innovating in Artificial Intelligence and Deep Learning to Detect Sophisticated Fraud

July 25, 2017

What is it about Artificial Intelligence (AI) that excites us data geeks? Is it the delivery of a promise given to us decades ago? Or endless possibilities that AI can give us now?  Probably a little of both. For Danske Bank, it’s giving business outcomes that deliver exciting results and AI is inspiring everyone on the team!NadeemGulzar

“Hands down, this is a high point in my career!  I’m a science guy, so I love these methods and talking about some of the more complex stuff.  It’s definitely worth it;  hands down.” – Nadeem Gulzar, Head of Global Analytics

Danske Bank is using Artificial Intelligence and Deep Learning to detect and then prevent sophisticated fraud in multiple areas and it’s working better and better every day because the models are learning.

Danske Bank distinguishes between two types of fraud – customer fraud and “fraudsters.” The customer is at the center of the fraud.  For example, the customer receives an email from a citizen in a remote country asking the customer to send money to help alleviate hardships or arrange a visit as there is a courtship in the making. And then there is true professional fraud done when a “fraudster” tracks the perfect time to do serious damage. This can include malware that is infected into a bank or where personal ID’s are taken and malware is added to devices.

“Sometimes in some scam cases the fraudsters attack us for ten minutes and then they never return. Their goal is actually to get the maximum value of those 10 – 15 minutes of fame as we call it and then stop up.” Nadeem Gulzar, Head of Global Analytics

Danske Bank is using LIME (Locally Interpretable, Model Explanation.)  LIME is a method used to explain the Deep Learning and what features matter. It is an open piece of software that basically helps the team using the model to explain the factors that make them believe that the model is solid. During this step and in one of the test projects, the team had to explain why they wanted to block a credit card transaction. In one example, bank customers buy from eBay, and the payment goes to China. But, today, the customer is using Alibaba. Is that fraud? Don’t know. And in this case, what do we tell the model to do?

In another example, the customer lives in Brazil, but today they are having lunch at a restaurant in Copenhagen. Is that credit card transaction fraudulent or not? This is where behavior data is important. Most customers have a preference, and when that preference isn’t chosen, what is occurring? Shall we execute the credit card transaction or not?

Is it all AI? No, human interaction is required to help train the models. For example, investigative officers were brought in to better understand anomaly detection. Human knowledge was needed to better understand the scene alongside the fraud models. Is this a sophisticated ring of “fraudsters”? Is this only one “fraudster”? Is this a group of 10-15 minute “fraudsters” creating a cluster? Is this a trend?

championchallengerOne of our favorite parts of this story is the ‘champion/challenger’ model strategy. Both champion and challenger models are always being tested using production data. And because there are billions of transactions happening every day, they can constantly improve the models. Danske Bank sets thresholds for the models, and when they go below a threshold, they determine if they are feeding it enough data. For example, do they need to add in geo-location data? Add in ATM data? Model comparison is done live!  And when appropriate, challenger models become champion models. That’s so cool!

All of this takes a diverse team.  Danske Bank employs platform, technical & data engineers, data scientists, the business and even highly trained criminal investigators – all of them work with experts in AI and Deep Learning to innovate.  And they will even hire from local universities!

The results for the business are more than impressive.  Before applying  AI and Deep Learning, Danske Bank had 1200 false positives a day.  Those were cases that had to be analyzed by Danske Bank investigators, sometimes even external agencies like Interpol.  Now that number has been reduced by 60%, saving bank investigators significant time and allowing them to investigate real cases of fraud. And that’s not all.  Detecting true positives has increased to 50%.  Teams at Danske Bank believe this is just the beginning.

Congratulations to Danske Bank for all the success in this renaissance of Artificial Intelligence!

Sanofi: Forwarding Medical Advances and Breakthroughs to Help People Have Better Health

June 12, 2017

Faster time to market in any industry is vital, but in bio-pharmaceuticals, it is even more than that – faster time to market can improve patient care or even change patient outcomes.  For the French multi-national bio-pharmaceutical Sanofi, analytics and data are the key factor in accelerating time to market. Operating in 100 countries around the globe and providing healthcare solutions to more than 170 countries, Sanofi produces critical pharma products for oncology, diabetes, cardiovascular, central nervous system and vaccines. Screen Shot 2017-06-12 at 5.57.19 PM

“Our goal is to help people have better health.  We produce vaccines to prevent disease in some of the poorest countries in the world.  We are researching solutions to meet unmet needs. We are also in consumer health, helping people feel better and live better.  We provide this to people all around the world.” – Martin Longpré, Solution Architect

It is well recognized that clinical data is one of the most sensitive industry assets and a competitive advantage that is required for critical evidence of a drug therapy’s efficacy, safety, as well as potential health and economic impact. Managing one of the largest R&D organizations in the bio-pharmaceutical industry, with a diverse pipeline, Sanofi R&D focuses on maximizing the efficiency of its clinical development organization as well as improving visibility into the progress of its global trials. Aiming toward the business outcomes of product innovation and risk mitigation, Sanofi R&D created MAESTRO.  Like a distinguished musician, MAESTRO is an agile, integrated warehouse designed to scale and address the clinical trial challenges that materialize from the high variability of the data captured.  Clinical trial data is as varied as it gets; everything must be entered – patient data, medical test results, protocol data, and medical side effects (even a headache must be captured).

Screen Shot 2017-06-12 at 5.57.08 PMMAESTRO, from the beginning was designed to scale.  Today, the solution is able to address the clinical trial challenges materialized by the high variability of the data captured and the continuous increase in volume introduced by an extensive portfolio of prescription drugs, vaccines, generics, and consumer healthcare products.  Sanofi R&D is able to manage dozens of studies at once (70-75) with the ability to scale to 200.  Researchers are able to refresh the study every ten minutes rather than every forty in the previous environment, allowing them faster access to new information.  And, the lockout time on studies has been reduced from eight hours to just under an hour!

“If we think about this system, it allows us to recognize and rapidly address any change in the status of a patient. If a patient experiences an adverse event during a clinical study, especially a serious adverse event, it is important that we get notified immediately so we can respond accordingly. The safety of our patients always comes first, in all the studies we do.” – Martin Longpré, Solution Architect

Teradata’s Enterprise Data Consulting organization helped Sanofi architect, integrate and migrate the data from Oracle to Teradata in addition to being instrumental in implementing important features such as JSON, User Defined Functions and Temporal. Project MAESTRO is impacting business outcomes, including;

  • Product Innovation and the ability of the R&D organization to potentially create new products or designs that are safer, more efficient, and meet the market needs sought by doctors and patients.
  • Risk identification, mitigation and evaluation should any agency (internal or external) want to understand a study’s traceability.

“The major advantage is that we have a history of all the interactions with the patient.  This means from the beginning of the clinical study, let’s say four years ago, to the end of the study, we will be able to provide every action that has taken place with that subject for all the different elements of the study.” – Martin Longpré, Solution Architect

All of which propels Sanofi towards their mission to “shape tomorrow’s health.”

“Félicitations” to Sanofi on project MAESTRO and all of your success!

 

Lufthansa Group: Connecting Europe to the World While Keeping the Customer at the Center of Business

June 5, 2017

“Big data costs money. Big analytics earns money.” Have you ever heard a more true statement?  That profound little nugget came from Heiko Merten, Head of Global Sales Business Intelligence Applications at Lufthansa Group.  Heiko knows what he is talking about – last year Lufthansa Group maintained critical profit margins and used that “big data and big analytics” to achieve three corporate KPI’s – maximize revenue, minimize costs, and maintain customer satisfaction. No easy feat operating multiple airlines and more than 18 companies that provide services to those airlines (think food, cleaning, and maintenance just to start.)

“That’s our KPI set which is supported by performance drivers. It means those factors that Screen Shot 2017-06-05 at 2.38.58 PMinfluence and affect those KPIs.  From a point of view, our top management should steer the company just via the top level KPIs and only if there are questions, uncertainties or need for clarification, will they drill down to have a look on the performance drivers together with middle management.” – Heiko Merten, Head of Global Sales Business Intelligence Applications

So, what are they doing and how are they doing it?

In order to measure those KPI’s, Lufthansa had to create a common data language from the multiple airline acquisitions and then integrate the data, from many internal and external sources, including revenue, reservation, marketing information, schedule, and market share while also bringing in competitive information to remain in tune with the competition. Lufthansa integrated their data with the Teradata Unified Data Architecture™ to break down the many different brand silos for crew scheduling, destinations, airplane schedules, jet fuel, and crew efficiency.  They have the ability to understand cross-functional performances of the different Lufthansa Group airlines, establishing a monetary evaluation of the carriers’ bookings.

Screen Shot 2017-06-05 at 2.30.46 PMWithin this highly competitive market, Lufthansa uses analytics to measure ‘fare share.’  Using pricing, route, airplane model and even customer segment data to determine if travel agents are giving Lufthansa group their fair share within a growing or shrinking market.

“How did the Lufthansa Group’s market share develop in comparison to the overall market development? Or who gained new share of the market? And to be more concrete, if Lufthansa grows by three percent in the market that overall grows thirty percent, that’s not a good sign. And vice versa. If a market shrinks by ten percent and Lufthansa shrinks just by five, that’s a good sign for Lufthansa…We are currently modeling or introducing a KPI called ‘fair share’ to evaluate the performance of agents or corporate customers reflecting exactly the fair market share of an agent.  That Lufthansa share that is sought thru an agent.”- Heiko Merten, Head of Global Sales Business Intelligence Applications

The integration of that data quickly led to another outcome or purpose for the analytical ecosystem which was to enable customer-specific offers and better service, establishing an overarching analysis among the multiple brands/carriers and business units.  That same integrated data also;

  1. Helps better steer sales and sales performances within Lufthansa Group airlines;
  2. Gets the full picture of their customer’s performance and rewards loyalty; and
  3. Fulfills the sales strategy and commercial targets on customer levels with optimal attribution of incentives.

With the right analytics, Lufthansa can overcome economic, competitive, and quality challenges to achieve unprecedented levels of excellence leading.  All coming back to…

“…Big data costs money.  Big analytics earns money. And that means, in my eyes, that additional revenue cannot be generated by data, it must be generated from the analytics that are based on the data.  This is an important point.  Have a good data set available and have powerful, high-performance reporting on top.”  – Heiko Merten, Global Sales BI Applications

Congratulations to Lufthansa Group for all your success!

DHL Express: Gaining Insights From a Global Finance Transformation

March 10, 2017

What DHL wants is simple….be “The logistics company for the world.” That fulfills a greater vision to improve the lives of people everywhere, enabling international commerce to boost economic growth with safer deliveries of medical goods and the ability to reach the most remote areas of the world. Revolutionizing the world of logistics takes innovation and the ability to recognize key opportunities and then transform. Key opportunities in this case meant finding actionable insights from the massive amounts of data DHL collects every day. That’s exactly what DHL Express (a division of DHL) did in 2013, embarking on a global finance transformation they appropriately named “Project INSIGHT.”

“The data that we get from INSIGHT allows us to be very specific when it comes to pricing, profitability, and costing. We can adjust our prices where we have very Screen Shot 2017-03-10 at 10.02.05 AMlittle capacity.  We can lower our prices where we have more capacity.  We can offer customers a much more balanced pricing proposition.”  Graeme Aitken, Vice President of Business Controlling

 Starting in one country and then replicating to others around the world, DHL Express integrated data from finance, operations, their customers and more, creating a sophisticated state-of-the-art costing system that takes advantage of the goldmine of data that DHL Express users have access to.

“What we’ve got is we understand very granular costing and profitability of every shipment, and because it’s at the level of the shipment, we can aggregate it up to trade lanes, countries, products, customers, and then we can start to take action on pricing, revenue management, capacity management, and so on.  So we can be very surgical about how we approach pricing, costing and profitability.  If we have a problem with profit, if we have a problem with cost, we can really be very specific about how we fix it.” – Graeme Aitken

This allows DHL Express full visibility into cost management, yield management and gives them the ability to predict and act on their customer’s economic cycles.

Screen Shot 2017-03-10 at 6.01.53 PMCost management allows DHL Express to take into account the hard costs of doing business no matter the customers (think planes, trains, automobiles, fuel, personnel etc.) Knowing those fixed costs allows DHL Express to better manage their customer pricing, ensuring it’s low enough for customer retention and yet high enough to be profitable for DHL Express.

With the capability of yield management, DHL Express can adjust for decreased or increased capacity.  They can adjust prices where they have little capacity and offer lower prices when there is more, resulting in a balanced pricing proposition. They can then pass insights and cost savings to their customers to save them money, improve their service, and increase their customer’s profitability.

“If, for example, we have an issue with a customer with failed delivery– so if we keep trying to deliver a shipment, our customer is not home — it’s bad for us and it’s bad for the original shipper because they’re getting lower customer satisfaction if a shipment’s not delivered. In many cases we’ve actually shared information with our customer. ‘We have an issue delivering shipments. We have an issue with bad address,’ and they get an insight into their own logistics data, and they can make their own improvements.” Graeme Aitken, VP of Business Controlling

Perhaps one of the most impressive results from the finance transformation is DHL Express’ ability to manage customer’s economic cycles with micro and macro 3rd party data. Whether the economy is up or down, DHL Express is now predicting and forecasting with better accuracy. They can look at historical data and add in economic indicators and strategic priorities to better manage their budget and revenue/profit plan.

“We have a brilliant view of the past. So if you extrapolate the past to the future, we should get a pretty accurate indication of where we’re going. So if you build in variables like inflation rates, Brexit, whatever else is happening in the global economy, we should be able to forecast more accurately using the very detailed costing data that we have currently.” Graeme Aitken, VP of Business Controlling

 DHL Express’ team is proud of the progress they have made in a few short years, knowing that 85% of digital transformation projects fail.  Graeme Aitken credits Teradata Consultants with consistent, innovative and critical work on Project INSIGHT.

“Teradata consultancy helped us build the system. One or two of them are still working on INSIGHT. If we ever have an issue, if we have a change request, if we want to make improvements, we always get the same people back because we had such a strong relationship, and over the years they understand almost as much of the business as we do and they can help us— ‘This is practical, this is not practical.’”  Graeme Aitken, VP of Business Controlling

Congratulations to the DHL Express team on the success of Project INSIGHT!

Lloyds Banking Group: One Ecosystem Serving Multiple Brands Delivering Business Outcomes to Help Britain Prosper

February 7, 2017

Committed to “helping Britain prosper,” Lloyds Banking Group serves 25% of Britain’s first time home buyers and 20% of business start-ups through their multiple brands. Data and analytics are at the core of the bank and are an integral part of Lloyds’ mission for the people of Britain.

 

Simon Howarth

Simon Howarth

“It’s across both operational and analytical data. It’s very much understanding the customer, providing a single view of the customer and their interactions with the bank, then being able to provide them with the best offers around products and their finances. It’s also to protect their data with support, risk systems, fraud systems and fraud identification. We provide that deep understanding of the customer in order to be able to set strategy, pricing, and organize the bank in the best way to be able to support both retail and commercial customers.” Simon Howarth, Chief Subject Matter Expert, Information Management.

The ‘mantra’ within IT at Lloyds is, “service is number one priority,” bridging architecture and the practical implementation of strategy. IT designs and manages the eco-system while different groups ‘crowd fund’ initiatives that benefit all of Lloyds in the areas of income generation, new product strategy, meeting regulatory requirements, customer experience and cost management.

When it comes to analytics and creating those business outcomes, Lloyds follows a winning process starting with a small ‘proof of value’ using minimal data and a known ‘kit.’ Then the team will bring in IT and different business areas together to identify common goals.  That’s when the ultra-unique ‘crowdfunding’ comes in to support different analytics initiatives and they deliver quickly with short sprints, share the findings and build more support.

In just a short time Lloyds Banking Group got quick wins with three separate Teradata Aster™ projects using the same 200 datasets

  • Transactions at the branch, internet banking, or ATM,
  • Digital (web, mobile and tablet platforms)
  • Telephone interactions with call center agents initiated by the customer or the bank
  • Updates to account/customer profiles
  • Outbound and offline marketing
  • Feedback (NPS surveys or complaints)

Use Case #1: Distribution Strategy

This included an investigation into what drives activity into bank branches, a significant and strategic impact to their cost infrastructure.  Key questions were asked during this use case including where should they invest bank branches over the next X years, number of ATM’s needed and where, along with the mix of skills and staff needed at the branches.

Use Case #2: Call Center Volume Investigation

lloydsFollowing the results of the distribution strategy use case above, our consultants were asked to analyze unexpected charges in customer call centers.  Customer calls had been declining steadily and naturally the bank was staffing toward that decline.  Yet, in November 2015, the decline turned upward and the bank worried that call volumes would continue to trend upward with service levels and customer satisfaction being impacted.  Call center staffing is a significant investment and the bank wanted to control and mitigate risk associated with cost of potential dissatisfaction.  And customer satisfaction is important!

The team gathered banking transactions and marketing interactions to sessionize the data and place it in a chronological order to understand when customers called (4 months of call center data, 4 months of subsequent banking transactions and 4B rows in Aster.)  And the triangulation began.

  • If you are an active account, 20% of that base called into the call center
  • If you were a payment customer, 26% of them would call into the call center
  • If you were a credit card customer, 33% would call the call center.

Upon further investigation, they realized that government tightened the rules for credit card which may have triggered their calling.  And for fraud alert transactions, there appeared to be a strong relationship between experiencing an alert and calling a call center.  There was strong evidence of rapid responses to fraud alerts with a pronounced spike into the call center.  Think about it.  If you received a text message that there was fraudulent activity on your credit card, how would you respond?

Use Case #3: Investigate Contactless Payments

There was strong evidence that contactless payment events often preceded call events, but there appeared to be a significant lag between payments and calls, that supporting the view that there is a “sudden shock” for customers when they realize that something isn’t right with their statement or transactions,Screen Shot 2017-02-07 at 10.01.15 AM

“Paths aren’t linear for somebody to actually get a product. Traditionally what we’ve done is look at a product’s completion; therefore the person has gone through step one, two, three. Actually, people go through steps one, two, back to one, then to three, back to two, back to one again, off out somewhere else. You can see a lot of looping going on, and that’s quite inefficient while it’s happening. So, a lot of the changes, or some of the changes that came off the back of that, aren’t actually to do with IT systems and what we’re actually going to do in the IT process. They’re to do with business process and how that then will result in success.” – Simon Howarth, Chief Subject Matter Expert for Information Management

The Group has used Teradata Customer Interaction Manager (CIM) for 8 years.  CIM helps to consolidate and organize data so that Lloyds Banking Group can

  • Segment target audiences into meaningful groups;
  • Visualize the impact segmentation strategies will have on engagement;
  • Select the right message for the right customer;
  • Predict customer responses to strategic offers based on historical data;
  • Send personalized, relevant and timely messages based on specific attributes and behaviors; and
  • Create and manage custom offers that align with a holistic brand messaging strategy.

By knowing the customer’s age and their geo-location on their mobile phone, they can send very targeted campaigns.  One-quarter (1/4) of sales come from the campaign management tool.    One of the biggest benefits of the Teradata CIM tool is the ability to tune a campaign, leading to an uplift in revenue.

Congratulations to Lloyds Banking Group for all of your success!