Monthly Archives: April 2017

Optimize the End-to-End Customer Experience with Business Analytics Solutions

April 27, 2017

burning leafIn Dr. Bokareva’s previous blog, she described how the Art of Analytics allows viewers to understand customer spend through a work of art. Businesses can use these types of visualizations to unlock new sales opportunities.

By now, companies understand the importance of using data analytics to better understand the customer, process, or product performance. However, many still struggle to get the full value from the vast amounts of available data. Every company is in a different stage of analytic maturity, but in my experience, most analytics are done in silos and are usually one-offs or domain- or problem-specific.

Departments within organizations do not share their data scientists, analysts, or data, so they don’t know what other departments are doing. The missing piece to analytics is the holistic approach, which is the clear view of capabilities, data, and a production road map across the whole business. Many companies are also limited by their current technologies and skill sets that prevent the business from moving forward through analytics. I hear many variations of, “We built this really great customer solution for target marketing, and then we end up marketing to everyone anyway because we don’t know how to put it in production, we don’t have the skills to use it, etc.”

Many factors contribute to this disconnect, ranging from financial to politics. However, a driving force to analytic success is a team that is independent, technology agnostic, and provides a full range of services and analytic solutions, including analytics business consulting and data science.

Structured Engagement Accelerates Time to Value

How can a structured, well-coordinated, and multi-disciplinary team help a company progress to the next stage of maturity and leverage the full benefits that analytics and data can offer? One way is to use a Business Value Framework that prioritizes and aligns business analytic use cases with the company’s strategic objectives.

One example we use at Teradata is a technology-agnostic methodology called the Rapid Analytic Consulting Engagement (RACE). I’ve seen many variations of it: scrum, experimentation, etc., but the RACE methodology offers several unique advantages. It is a highly structured, strategic way to identify business processes with the highest ROI, data sources, and analytics to solve a problem. The entire engagement happens rapidly—unlike consultancies that require several months or more to review processes, make recommendations, and implement solutions—RACE delivers results in just six weeks.

RACE also gives companies the flexibility to pivot priorities if a more urgent problem comes up. In fact, in many cases we start with one problem and then the client requests another use case or asks us to expand the scope of the current one. The success of such short engagements also comes from working closely with stakeholders who understand their business as well as our expertise in technology and analytics.

Effective Customer Journey

What are examples of RACE engagements? Most customer-centric companies emphasize building and analyzing “Customer Journeys.” One-off RACE engagements will never deliver the full benefits of the customer journey, but having a clear purpose for creating the journey and a methodical plan for building and analyzing it will.

Great customer journey maps are rooted in data-driven research and represent the different life phases of customers, their experiences, and touch points. The full value of the journey is achieved only if it is based on a variety of dimensions such as sentiment, goals, touch points, and more. The sources of these dimensions come from a variety of data:

  • Digital: The most obvious is website analytics, which provides a lot of information on users and what they are trying to achieve. This also helps companies identify points in the process where customers stopped shopping or otherwise abandoned their journey.
  • Front-line staff: Recording and analyzing feedback from front-line staff who interact with customers daily, such as those in support and sales, helps companies understand customer needs.
  • Transactional data: This can show spending habits, budgets, deposits, outgoing payments, etc.
  • Social media: Businesses can identify sentiment from customers talking about products and brands, and determine customer connections and their influence in social networks.
  • Customer history: Companies can find out the number of accounts a customer holds, tenure, relative income, Net Promoter Scores (NPS), etc.

Each dimension requires different techniques, analytic approaches, and technologies. Ultimately, every customer journey is built upon life events and customer touch points. The importance of a particular event or touch point varies between industries and from company to company.

Repeatable Methodology Across Businesses and Industries

A RACE approach is repeatable. The same steps can be used to analyze the entire end-to-end customer lifecycle to benefit any customer-centric business. It can be applied to telecom, retail, airline, and insurance companies that need a much better customer view.

For some businesses, like telecoms, that journey can be very short, and extending that journey and increasing customer value is of paramount importance. A great opportunity for new sales is in the insurance industry. If insurance companies know a customer is expecting a child or recently gave birth, then they know it is the best time to offer life insurance.

For others, such as banks and airlines, the journey can span decades. Because customers’ needs change—their buying habits at 20 years of age are not the same when they’re 50—companies must offer what a customer wants at a specific point in their live and based it on his or her particular situation. Businesses with such a capability will retain customers and continue to be profitable.

Learn more about Teradata’s Business Analytics Solutions and RACE.


TatianaDr. Tatiana Bokareva is a senior data scientist for Teradata in Australia. She has more than 10 years of experience in research and analytics, and has been working in commercial data science and big data consulting for the last three years. She is responsible for the design of analytical solutions while leading and managing the delivery of analytic projects.

Understand the Customer Through Art

April 27, 2017

There are many ways to look at the customer. One way to identify life events is to look at a customer’s behavioral patterns. This can be done through the Art of Analytics, which presents data as works of art to give the information new meaning. “The Burning Leaf” is one example of an artwork that looks at significant variations in customers’ weekly spending patterns. This Art of Analytics piece was born out of a Teradata engagement with a global bank.

burning leaf

The Analytics Behind the Art

The Burning Leaf was built across different technologies. Teradata Aster integrated and processed rich transactional accounts and credit card spending data. The Change Point Detection (CPD) algorithm identified the change points in a time series, and R produced the visualization.

The graph is read from left to right. Each line in the graph represents spending habits of an individual customer. Like a customer-spending time series, each rise or dip on the graph represents a deviation from the customer’s average weekly spending.

A significant rise represents a potential life event and the point in time when this event occurred, such as school fees, a new baby, a major purchase (car, house deposit, expensive holiday, etc.). Once a large jump or fall is identified, it triggers the event classification procedure to approach the customer with a targeted offer.

Most customers have less than eight major changes in a year, although a few have 10, which created the “tail” to the right of The Burning Leaf. The system does not have to trace the entire yearly transactional history of a customer; it only needs to react when a change is detected.

Beyond Eye Candy

The Art of Analytics goes beyond being a striking visual. The art can bridge the gap between the technical domain of data science and the business users who need to take action on the analytic insights that are produced. It acts as a powerful marketing tool and is instrumental in helping to grow and attract businesses.

The art breaks the ice, eases customers, and starts conversations with organizations about data and how it relates to their business problem. The reaction to an Art of Analytics picture ranges from “I never thought data could look like this” to “I never saw my business through this perspective” to “I want this for my business.”

Unlock Sales Opportunities

Understanding life events provides a more complete picture of who customers are, where they are in their life journey, and their financial needs. Any major life event can trigger a sales opportunity. A marriage, kids enrolling in a private school or going to college, buying a house, having a child, or travelling overseas are all points along the customer lifecycle that are ripe for a targeted sale.

To identify events for up-sale or cross-sale offers, companies must have visibility across the entire customer lifecycle; from the first touch point until the present day. Moreover, the events library as well as the ability to expand, analyse, and act upon it needs to be accessible to the business throughout an entire organization.

An ability to act on these insights develops stronger business relationships with customers, encourages and rewards loyalty, improves NPS scores, and pinpoints which particular financial products are most appropriate at any given point in time. In the end, all of these will lead to a higher customer lifetime value and increased profits.

Click here to read Dr. Bokareva’s blog on why a holistic approach is the “missing piece” to analytics. She also explains how a low risk, consultative engagement for business analytic use cases can accelerate time to value across a range of businesses.

Watch a video with Dr. Bokareva, to hear her explain Burning Leaf in greater detail.


TatianaDr. Tatiana Bokareva is a senior data scientist for Teradata in Australia. She has more than 10 years of experience in research and analytics, and has been working in commercial data science and big data consulting for the last three years. She is responsible for the design of analytical solutions while leading and managing the delivery of analytic projects.

Customer Journey Analytics – One Bite At A Time

April 26, 2017

food bins

The “customer journey” means many things to many people. Depending on your role, you may be hyper-focused on delivering the right message to the right customer in real-time on your website. Or maybe you’re trying to understand how customer interactions across several channels work together to affect satisfaction.

These may sound like daunting challenges that require months-long sales cycles, consulting and services engagements, and technology implementations to solve, but I have good news. If you’re focused on the analytics around Customer Journey, you don’t need to boil the ocean to deliver value.

In a matter of days (or a few weeks at most), you can be performing advanced analytics around your customers’ journeys within business-ready interfaces – no coding – that make it simple to share your analyses and insights.

Teradata’s Guided Analytics Interfaces make it easy to prove the value of Customer Journey analytics. These standalone applications are built with a hyper-focused intent to help you perform and share advanced analytics around:

• Marketing Attribution. (BRAND NEW!) Which campaigns and ads are leading to conversions? How do these results differ across various attribution models?
• Path Analysis. How are my customers flowing from event to event? Are we creating obstacles that force them to bounce from channel to channel?
• Customer Satisfaction. Across all my channels, which events are creating the most dissatisfaction? Who are my most satisfied and least satisfied customers?
• Text Sentiment. Which terms are trending up and down in social media or online reviews? Are certain products, or categories of products, generating positive sentiment?

The Marketing Attribution Guided Analytics Interface helps you identify which campaigns, ads and promotions across various channels are generating conversions.

This Sankey Diagram in the Path Analysis Guided Analytics Interface shows the common events for customers preceding a checkout error on a website.

This Sankey Diagram in the Path Analysis Guided Analytics Interface shows the common events for customers preceding a checkout error on a website.

Don’t be intimidated by the world of possibilities around the Customer Journey. We are here to help you start delivering value from your Customer Journey analytics today. As you quickly prove the value of these initial use cases, then you can grow your Customer Journey initiative and transform your organization.


ryan-garrett-headshotRyan Garrett is senior business development manager for the Americas Analytic Business Consulting group’s Analytic & Data Science UX Team. His goal is to help organizations derive value from data by making advanced analytics more accessible, repeatable and consumable. He has a decade of experience in big data at companies large and small, an MBA from Boston University and a bachelor’s degree in journalism from the University of Kentucky.

 

Proprietary Analytic Approach Accelerates Time to Value

April 25, 2017

risk
In reality, only a small percent of insurance claims filed each year are fraudulent. However, that small percentage costs insurers big bucks. According to the Insurance Information Institute, property and casualty (P&C) insurance fraud amounts to about $32 billion each year. This requires insurers to be diligent about rooting it out.

Most companies have data sources that are not being used or fully analyzed. Many companies are also limited by their technologies and skill sets. As a result, these businesses are unable to quickly and efficiently identify risks such as fraud.

One Week Hackathon Uncovers Insights

With the right approach, expertise, and technology, companies can quickly uncover risk. This is critical in industries like insurance in which fraud must be recognized before a claims payment is made.

Teradata combines data science, analytic technologies, specialized consulting methods, and intellectual property to find data sources not being used, or find new ways to use existing data, and accelerates the time to gain new business value. That’s the approach we took when we worked with a P&C insurance company. We held a one week “hackathon” with four of our data scientists and business consultants, and six insurance company specialists. We looked at claims data that had not been used before and applied it to three previously identified business use cases.

We used multi-genre analytics, in which multiple techniques are intelligently brought together to gain insights. Techniques included text mining, graph, cluster, machine learning, and predictive analytics. After just a few days, we were able to identify how the data could show potential instances of fraud.

The hackathon is an example of a business outcome led, technology-enabled approach that can deliver high value results quickly. In this case, we had results in just one week.

Low Risk Engagement Offers Freedom to Innovate

Teradata business analytics consulting teams are using the same approach we took with the insurance company to deliver high impact outcomes to other industries. The approach, called the Rapid Analytic Consulting Engagement (RACE), allows us to engage a business by prioritizing use cases that deliver the most impact and align with the company’s strategic goals.

RACE eliminates the significant investment and several month or longer requirement to get answers that is common with other consultative approaches. One problem with these other approaches is that by the time the business sees the results, if decision makers aren’t happy with the outcome, there’s a feeling that it’s too late and too much is invested to change course and start over.

By contrast, the RACE is a low risk, low cost engagement. Unlike other consultancies, RACE gives analysts the freedom to fail, which can sometimes lead to new discoveries. We have the freedom to try approaches that have not been done before, give them time to develop, then analyze the results. We’ll know in just a few weeks if the approach will deliver value and is worth pursuing, or if we need to make a change.

Uncovering Networks Delivers New Business Value

When we worked with the insurance company, we were able to use a subset of data to determine claims that had a high likelihood of fraud. This allows the small team of fraud investigators to focus their limited resources on the cases that have a relationship to a person, organization, or other entity already known to be fraudulent. We also created an Art of Analytics picture called Fraud Invaders that offered a detailed visual of those relationships.

The large dots in the picture represent known instances of fraud. The small dots are claims that have not yet been investigated, but if we see a line connecting a small circle to a large one, we see a relationship that is worth investigating. This is just a suspicion of fraud at this point. People buy cars, change phone numbers, move to new homes, and do other activities that could result in a coincidental connection to a fraud activity, so it doesn’t mean a claim is fraudulent just because of a connection. However, focusing on suspicious claims is a good place to start.

This ability to identify relationships can also benefit other industries. For example, in the highly competitive telco industry, companies can use our advanced analytics to show networks of family, friends, and business contacts. Telcos can use that information to determine the “alphas,” who are at the center of networks. If alphas change providers, they influence others to do the same, so telcos can work to ensure these customers are not at risk of churn.

Likewise, identifying networks can help with preventative maintenance. Sometimes parts are likely to fail at the same time. Knowing this information, vehicle manufacturers can proactively replace all of those parts when one fails, which improves customer satisfaction and loyalty, and eliminates the need for multiple trips to the repair shop.

Analytic solutions help businesses uncover new insights for their data. What business outcomes could you achieve if you could get deeper insights from your data?

Watch a video with Christopher Hillman, to hear him explain Fraud Invaders in greater detail.


Hilman Chris_Web_MG_8878Christopher Hillman is a Principal Data Scientist in the International Advanced Analytics team at Teradata basedÊin London. He has over 20 years experience working with analytics across many industries including Retail, Finance, Telecoms and Manufacturing. Chris is involved in the pre-sale and start-up activities of Analytics projects helping customers to gain value from and understand Advanced Analytics and Machine Learning. He has spoken on Data Science and analytics at Teradata events such as Universe and Partners and also industry events such as Strata, Hadoop World, Flink Forward and IEEE Big data conferences. Currently Chris is also studying part-time for a PhD in Data Science at the University of Dundee applying Big Data analytics to the data produced from experimentation into the Human Proteome.

No, You Can’t Machine Learn Everything

April 25, 2017

machine learningMachine Learning is fast becoming a source of both confusion and anxious hope to many organizations.  So much so that several customers last year told us, “please don’t talk about analytics to our senior stakeholders, because we’ve told them that we are going to machine learn everything!”

Now, Machine Learning already provides enormous value in just about every industry you can imagine – with use-cases that span from preventative maintenance through smart recommender systems to fraud detection.  But you can’t “machine learn everything” – and even if you could, there would still be quicker routes to goal to solve some problems.  The most successful data-driven organizations tend to think first in terms of the business problem that they are trying to solve; second about the data that are – or that could be – available to solve it; and only then about the methods, techniques, algorithms and technology that they should employ.

Part of the problem, we think, is that terms like “Analytics”, “Data Science”, “Machine Learning” and “Artificial Intelligence” are used by commentators both interchangeably and to mean different things.  By understanding the history of the field and the origin of these labels, our hope is that business and technology managers will be able to truly understand the possibilities – and the limitations – of Machine Learning.

The recent history of Machine Learning arguably begins with the brilliant British mathematician and early computer scientist, Alan Turing. Turing and his contemporary, Alonzo Church, had already produced what subsequently became known as the Church-Turing thesis – proof that digital computers are capable of computing anything that is computable – when in 1950, Turing turned his attention to another, related question.  Could a machine exhibit intelligent behaviour, equivalent to – or even indistinguishable from – that of a human?  And if it could, how would we know?

Turing proposed what came to be known as “the Turing Test”; that a human evaluator, eavesdropping on a conversation between a human and an “Intelligent Agent”, should not be able to tell which is which at least 70% of the time.

The Turing test – or “Imitation Game” – is now often held to be flawed, for all sorts of very good reasons that we don’t have time to explore here.  But in the 1950s it was a revolutionary idea that helped to give birth to the idea of Artificial Intelligence (AI) – and led to the first academic study of the subject at Dartmouth College in 1956.  As the author of the proposal, J McCarthy, put it: “The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

In 1956, researchers believed that they were only a decade away from computers that could achieve true Artificial Intelligence.  That turned out to be wildly optimistic, with the field going through at least two “winters” – epochs when research money dried-up in the face of AI’s apparently intractable problems and when other approaches, like rule-based systems, looked more promising.  But Artificial Intelligence had now entered the academic mainstream as a sub-field of Computer Science.

Research into Artificial Intelligence can be divided into disciplines that focus on specific problems.  Among the more important problems is enabling the Intelligent Agent to harvest data from the environment – and then using those data to improve its performance of a task.  And so the quest for Artificial Intelligence led naturally to the study of “Machine Learning”.

Since Artificial Intelligence is also concerned with many other issues – reasoning and problem-solving, knowledge representation, agency and cognition, Hollywood movies about a dystopian future ruled by killer robots, etc. – Machine Learning is only a sub-field of Artificial Intelligence, which is itself a sub-field of Computer Science.

It was the quest for Artificial Intelligence that gave us Machine Learning.  And in the next installment of this blog, we’ll explore how machine learning gave us Data Mining – and how vendor marketing departments have now taken Machine Learning back to the future.


Martin_WilcoxMartin Willcox –
Senior Director, Go to Market Organisation (Teradata)

Martin is a Senior Director in Teradata’s Go-To Market organisation, charged with articulating to prospective customers, analysts and media organisations Teradata’s strategy and the nature, value and differentiation of Teradata technology and solution offerings.
Martin has 21 years of experience in the IT industry and is listed in dataIQ’s “Big Data 100” as one of the most influential people in UK data-driven business. He has worked for 5 organisations and was formerly the Data Warehouse Manager at Co-operative Retail in the UK and later the Senior Data Architect at Co‑operative Group.

Since joining Teradata, Martin has worked in Solution Architecture, Enterprise Architecture, Demand Generation, Technology Marketing and Management roles. Prior to taking-up his current appointment, Martin led Teradata’s International Big Data CoE – a team of Data Scientists, Technology and Architecture Consultants tasked withassisting Teradata customers throughout Europe, the Middle East, Africa and Asia to realise value from their Big Data assets.

Martin is a former Teradata customer who understands the Analytics landscape and marketplace from the twin perspectives of an end-user organisation and a technology vendor. His Strata (UK) 2016 keynote can be found at: https://www.oreilly.com/ideas/the-internet-of-things-its-the-sensor-data-stupid and a selection of his Teradata Voice Forbes blogs can be found online, including this piece on the importance – and the limitations – of visualisation.

Martin holds a BSc (Hons) in Physics and Astronomy from the University of Sheffield and a Postgraduate Certificate in Computing for Commerce and Industry from the Open University. He is married with three children and is a lapsed supporter of Sheffield Wednesday Football Club.  In his spare time, Martin enjoys playing with technology,flying gliders, photography and listening to guitar music.


 

Frank SauberlichDr. Frank Säuberlich – Director Data Science & Data Innovation, Teradata GmbH

Dr. Frank Säuberlich leads the Data Science & Data Innovation unit of Teradata Germany. It is part of his repsonsibilities to make the latest market and technology developments available to Teradata customers. Currently, his main focus is on topics such as predictive analytics, machine learning and artificial intelligence.

Following his studies of business mathematics, Frank Säuberlich worked as a research assistant at the Institute for Decision Theory and Corporate Research at the University of Karlsruhe (TH), where he was already dealing with data mining questions.

His professional career included the positions of a senior technical consultant at SAS Germany and of a regional manager customer analytics at Urban Science International.

Frank Säuberlich has been with Teradata since 2012. He began as an expert in advanced analytics and data science in the International Data Science team. Later on, he became Director Data Science (International).

 

Standing With Women in Tech: Tips for Success

April 20, 2017

Big Data Success Starts With Empowerment, Chris Twogood, Data Points, Teradata

Embracing the unknown

Had I planned my career path, I would most likely be in another location today, interacting with people from a field unknown to me and carrying out a list of tasks using knowledge and experience from another learning path.

Planning my career is not a task I have knowingly ignored, it is a task I have always found challenging. With the exciting marketplace, disruptive trends and rapid progression in science and technology, there is an abundance of opportunity that I could not have dreamed of even a year ago. When I studied as an undergraduate, the data science field did not exist. It was not yet a concept, let alone a set of courses that could be studied.

I am fortunate to say that the roles I have undertaken in my career have been positions that were new by design and in multiple cases, roles that were created to fulfill a new requirement that did not exist before. The unknown has made my career exciting.

Challenges create opportunities

Challenges are a part of everyday life. When working in a rapidly changing and advancing field, it is inevitable that there will be challenges to overcome. I have had to overcome obstacles throughout my career, from becoming the first Data Scientist recruited into Teradata UK&I, to building a team from scratch, to more recently defining a strategic vision, developing new go-to-market strategies and implementing new operational models. Every step of the way, I feel fortunate to have faced these challenges that I perceive are an opportunity to grow and learn.

Vital to overcoming these challenges has been strong leadership and mentoring. It has been key to seek out individuals who would support me through the ups and downs, providing their external perspective and experience.

Don’t be afraid to aim high

 Be brave. You will never be ready for your first leadership position, you will be challenged by new and complex situations you have not dealt with before. In many cases there will be no right answer, you will be required to make difficult choices but the key is remaining authentic and true to your values.

Avoid the trap of becoming just a manager, organising and coordinating teams. Go beyond management to leading with a vision. To be a success, you must complete tactical tasks and activities every day, however to become a strong leader you must set yourself additional goals that help you become more strategic for long-term impact.

Passion goes a long way

If I were to decide between two equally qualified candidates for a role, the candidate with most passion, motivation and drive would be the winner. This candidate is more likely to go above and beyond what they have been asked to do and bring their own drive to the role.

Passionate candidates are always challenging themselves to continuously learn and grow. They do not work the conventional week. They spend time thinking beyond the tasks they are assigned and find novel ways to add value. These are the people who not only have a positive impact on the business but they also have a strong influence on the team, lifting and inspiring others and setting a high standard of execution.

Honesty is the best policy

Ongoing, consistent interaction with my management has always served me well. During my career I have chosen to work for people who inspire me. These are the people I know will push me to better myself and teach me a great deal.

I am very open and honest with my manager, ensuring I discuss the key challenges I am facing, what I am trying to develop in my practice area, as well as discussing the upcoming risks. By ensuring that I share these details with my manager, I am able to leverage their experience and advice. In most cases, my manager has had years more experience, understands the politics of the organization and is adept at people management. I can leverage this insight to perform better.

My manager cannot help me, if I do not ask. Furthermore, a constant and consistent dialogue provides my manager with the required context to help guide and course correct, ensuring the activities I carry out are aligned with the global aims of the organisation.

Find your ideal workflow

I’m an early morning person – I get more work done with an early start than I can often complete all day once the calls and meetings start. The morning provides me time to gather my thoughts and do my most creative work.

My days are not usual, my career has involved a lot of travel. On average I am on the road 5 days a week: flights and train journeys, a team across different time zones, a multitude of global customers to work in partnership with. This results in no typical end of the day. However, I like to make sure I get some me-time to take a walk in a new city, go to the gym, wind down from a hectic day.


Yasmeen AhmadYasmeen Ahmad – Business Analytics Leader and Partner Practice at Teradata

Over the years, I have worked with many businesses to identify challenges and understand business context, providing an analytical perspective for achieving solutions. This has meant using new, or untapped, sources of data coupled with innovative analytics to enhance competitiveness. Techniques have included event-based analytics, predictive modelling, natural language processing, time-series analysis, and attribution strategy development. My work takes me to all parts of the globe, covering a wide range of industries including finance, retail, utilities, and telecommunications. I speak regularly at international conferences and events. After an undergraduate in computing, I took a tangent into the world of Life Sciences, completing my PhD in Data Management, Mining, and Visualisation, at the Wellcome Trust Centre for Gene Regulation and Expression. Following this, I worked as a data scientist, building analytical pipelines for complex, multi-dimensional data types. Along the way, I’ve provided leadership, training, and guidance, for many analytical teams, creating actionable insights and business outcomes through the development of analytical use cases. I have also lectured on Masters programmes for BI and Data Science. I grew up in Scotland, and love the great outdoors. Consequently, I’m an avid hiker (the Scottish Munros are perfect) and sea kayaker. Oh yes, and a keen traveller, too.

 

5 Ways Cloud Vendors are Dealing with Data Privacy Concerns

April 19, 2017

image of vista over mountains, explorerCloud computing has undoubtedly transformed the way organisations manage their business and data, however it has brought its own unique set of security concerns. While some businesses are quick to embrace the convenience and agility of the cloud, others remain hesitant because of fear about data breaches and cybercrime.

So how exactly are cloud vendors dealing with these data privacy concerns?

  1. Making security the number one priority

When it comes to customer data, security is a top concern of cloud vendors. Make sure that you understand your own Data Privacy requirements and discuss these with your cloud vendor from the outset.  Chances are that your requirements are similar to those that the cloud vendors can already meet. Companies may, for example, have legal restrictions that prevents corporate and personal data from being stored outside national boundaries. However, most companies are finding that cloud instances can easily be set up within respective countries to store information according to regulation, and that cloud security standards and certification are maturing quickly.

  1. Providing the relevant training

Cloud vendors are also recognising the necessity of having cloud security training available for all employees or contractors who have access to the cloud and the data your company stores within the cloud . It’s also equally important to establish a data breach policy and to know your cloud provider’s incident response plan. Finally, it’s essential you have the ability to audit your provider on a regular basis. An effective cloud vendor security team will be ready to assist you in any or all of these areas.

  1. Delivering across-the-board support

Today, cloud vendors are designing managed cloud services from the ground up to meet the most advanced data security requirements, giving current and prospective customers the peace of mind that their data is private and secure. They should also deliver across-the-board support for every aspect of cloud security including physical security, network security, data protection, monitoring, and access controls.

Data encryption for data in flight and at rest along with tokenisation of sensitive data items are strategies that can help improve Data Security and help to meet the most stringent of data privacy requirements.

Cloud vendors understand that any successful cloud security solution requires close collaboration between you and your cloud service provider, knowing that it’s critical that your organisation has a programme that covers everything from data governance and compliance to cloud user access.

When it comes to physical protection of the data centre infrastructure, regular monitoring of all physical access to the facility to detect and prevent potential security incidents is also of upmost important to cloud vendors, as well as access to control and alarm systems, administrator logging, two-factor authentication, codes of conduct, confidentiality agreements and background checks.

  1. Adopting hybrid cloud

A hybrid cloud approach is when public or private clouds, or a combination of the two, are fully integrated with traditional, on-premises IT and centrally managed through a single platform. Hybrid cloud is an attractive option and flexibility is certainly one of the key benefits. In a hybrid architecture built on a common database, companies can economically shift data, applications and workloads between environments according to business needs, while also fulfilling data privacy and security requirements.

As a starting point a company may decide to move Dev and Test capabilities to the cloud using existing data privacy policies which would usually prohibit sensitive data items existing in non-production systems. As the capability matures and the data privacy requirements in terms of cloud become clearer, companies can then make informed choices on which workloads and which data are appropriate to move to cloud.

As businesses increasingly look to the cloud to drive competitive advantages, a hybrid approach can also offer greater agility, while lowering financial and time commitments. When flexibility in deployment meets increased responsiveness and the ability to achieve high levels of security, data privacy, and regulatory compliance, it’s no wonder so many companies are looking to hybrid cloud solutions.

  1. Being prepared

To help strengthen the disaster recovery and business continuity efforts of their customers, cloud vendors can maintain a contingency plan that identifies essential missions and business functions along with associated contingency requirements. In addition, recovery objectives, restoration priorities, and related metrics and address contingency roles and responsibilities can be provided.

Companies should review their existing data management policies to ensure that data privacy requirements are captured at every stage in the data lifecycle.  In many cases this will mean amending those policies to address situations whether data is held on or off premise.

The disaster recovery and business continuity plan, which should be tested and reviewed regularly, also shows businesses how to maintain vital missions and business functions despite potential information system disruption, compromise, or failure.


Mike WhelanMike Whelan – Head of Product Management, International

Mike is responsible for Product Management for Teradata’s International region driving global innovations. He serves as the linkage point between the International management, International Field organization, the corporate product management and corporate product marketing.

Mike has held a number of technical roles spanning both pre-sales and post-sales activity. Mike’s background in open distributed systems and network systems led him into Enterprise Architecture and Systems Design.

Since the early 1990s Mike has worked with Teradata systems and has been involved with many large organisations across a number of industry sectors. Mike recently led the Teradata International Big Data Technology COE so has experience of Aster, Hadoop and the Teradata Unified Data Architecture. Mike has a BSc in Computing & Data Processing from Napier University in Edinburgh.

The Age of the Machine

April 12, 2017

RS2676_shutterstock_317361941
Robot slaves performing menial tasks.

Is this all we’ve got to look forward to as machines take over the world, capitalising on their ability to learn and create faster, more accurate decisions? Could it be that advanced algorithms are becoming the biggest threat to business and our way of life now machine intelligence is proving superior to our own?

The digital revolution amazed us with products and services tracking our every move, placing location and context at our fingertips. Machines began to tell us where we could go and how long it would take to get there bearing in mind the weather, roadworks, driving conditions, etc. Today, machines are not only capable of identifying possible routes, they’re predicting (with a high degree of accuracy) which path we’re likely to pick.

In almost every field, machines are outperforming humans. Not surprisingly, the automation of machine learning is opening businesses up to a new reality where they become self-optimising, real-time decision-making engines that enable new business models and maximise revenue streams.

So, should we be worried about machines making humans redundant? The reality is that machines can operate businesses more efficiently and effectively, allowing us to devote our time to innovation, transformation, and creativity.

Machines powered by big data

Gartner’s last ‘Hype Cycle Report Of Emerging Technologies’ showed machine learning taking the most prominent place on the curve, replacing big data altogether. Whereas, in previous years, the focus might have been on big data potential, today, we understand that business success is achieved through the development of sophisticated algorithms.

Machine learning may be the new black, but it’s not new. It has been used extensively throughout the academic community where specially designed experiments create large volumes of data that require complex analytics. And now businesses have moved beyond big data, they need new techniques to handle the Robotmassive amounts of streaming data.

Data is capturing more knowledge, experience, and learning, than we can comprehend and process. Algorithm intelligence is directly related to the data we provide; the bigger the better. As I’m sure you’re aware, the machine-learning algorithms we use today would be dumb without data. And just as humans improve with experience, algorithms improve their accuracy in step with the increasing amounts of data they work with.

Moreover, studies have shown that when it comes to predicting events, machines outperform humans in most cases. The human mind is constrained by the volume of information it can process, what it can hold in memory at any given time, and the difficulty of identifying complex, hidden patterns. These skills are essential in big data world where the volumes are enormous in scope and complexity.

Mind over machine

Traditional businesses are run by management and executive-committee members trained to make decisions that impact the daily business. Executives who base organisational decisions on intuition, gut-feel, and experience, are now up against a data-driven, machine-learning approach that benefits from the massive amounts of data collected.

As a business, we encourage management teams to develop bonus-maximising behaviours which result in suboptimal decision making. They focus on the data that supports personal theories and preconceived opinions, discarding contradictory information. This bias is compounded by the fact that they’re under pressure to deliver ever-greater results, quickly.

Reliance on a series of mental shortcuts rather than the assessment of evidence impairs our judgement. Consequently, it makes sense to hand over our future decision making to machines, an act which will change business, society, and the economy, for good.

This embedding of machines is forging new relationships between people and machines. It’s putting science at the centre of business life and opening up new ways of doing business through fresh technological capabilities and new corporate cultures.


Yasmeen AhmadYasmeen Ahmad – Business Analytics Leader and Partner Practice at Teradata

Over the years, I have worked with many businesses to identify challenges and understand business context, providing an analytical perspective for achieving solutions. This has meant using new, or untapped, sources of data coupled with innovative analytics to enhance competitiveness. Techniques have included event-based analytics, predictive modelling, natural language processing, time-series analysis, and attribution strategy development. My work takes me to all parts of the globe, covering a wide range of industries including finance, retail, utilities, and telecommunications. I speak regularly at international conferences and events. After an undergraduate in computing, I took a tangent into the world of Life Sciences, completing my PhD in Data Management, Mining, and Visualisation, at the Wellcome Trust Centre for Gene Regulation and Expression. Following this, I worked as a data scientist, building analytical pipelines for complex, multi-dimensional data types. Along the way, I’ve provided leadership, training, and guidance, for many analytical teams, creating actionable insights and business outcomes through the development of analytical use cases. I have also lectured on Masters programmes for BI and Data Science. I grew up in Scotland, and love the great outdoors. Consequently, I’m an avid hiker (the Scottish Munros are perfect) and sea kayaker. Oh yes, and a keen traveller, too.

 

Teradata on Azure – Available Now!

April 10, 2017

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Teradata on Azure: Available Now!

Good news! Teradata Database, the market’s leading data warehousing and analytic solution, is now available for deployment on Microsoft Azure via the Azure Marketplace.

As previously announced, Teradata Database on Azure is offered on a variety of multi-terabyte Virtual Machines (VMs) in supported Azure regions. We’ve been working very hard across the company to make this launch a reality – and it’s rewarding to be part of an exciting new chapter in our hybrid cloud evolution.

To be clear, there is a strong shift toward the adoption of hybrid cloud, which more than 90 percent of Teradata customers surveyed in 2016 plan to employ by 2020. By blending on-premises and cloud-based deployment in a hybrid and cohesive environment, customers can focus on what matters to them: creating business value.

I believe Teradata software on Azure is significant because it represents yet another new hybrid cloud deployment option for Teradata Database, which has long been the industry’s most respected engine for production analytics.

Indeed, Gartner’s most recent Magic Quadrant for Data Management Solutions for Analytics shows Teradata as a Leader and the one vendor with the highest position for Completeness of Vision.

Furthermore, as highlighted in Gartner’s Critical Capabilities for Data Management Solutions for Analytics, “Teradata received the top score in all four of our defined use cases, demonstrating a mature product with market-leading depth and breadth of functionality.”

This is a big deal and one of many recent accolades that highlight the fact that here is simply no substitute for the wisdom and insight gained through experience.

By incorporating Azure as a strategic public cloud deployment option for Teradata Database, we make it easier for companies of all sizes to become data-driven with best-in-class data warehousing and analytics. Subscribers can be up and running with an entire Teradata ecosystem in about an hour.

It’s interesting: in many ways, Teradata Database on Azure is both similar to, and different from, what we currently offer via other deployment modes.

Similarities:

  • Same Teradata Database software as available in our on-premises solutions
  • Same Teradata Consulting and Management Services as available across all Teradata offerings
  • Same Teradata ecosystem software capabilities as available elsewhere in other Teradata deployment options

Differences:

  • Initial lower node count limit than what currently exists in other deployment options – but the ceiling will be elevated in coming quarters
  • Initial hourly pay-as-you-go pricing without an annual subscription option – but this will be addressed with BYOL (Bring Your Own License) in coming quarters
  • Initial narrow set of Virtual Machines (VMs) available for deployment – but this will be expanded in coming quarters

Many companies – especially in retail – have already invested in, and aligned themselves with, Microsoft software and Azure cloud services. In my opinion, consuming best-in-class Teradata software in that same trusted Azure environment is a true no-brainer.

Any organization of any size can use and benefit because the offer combines the power of Teradata with the convenience and performance of Azure. New-to-Teradata customers will find it just as compelling as existing Teradata customers.

I’ll summarize: Teradata Database on Azure is available NOW for global deployment via the Azure Marketplace. See for yourself. I think it’s great, and I hope you do too.

Please feel free to let me know if you have any questions about our latest cloud innovations.


 

brian wood headshot teradata cloud marketingBrian Wood is director of cloud marketing at Teradata. He is a results-oriented technology marketing executive with 15+ years of digital, lead gen, sales / marketing operations & team leadership success. He has an MS in Engineering Management from Stanford University, a BS in Electrical Engineering from Cornell University, and served as an F-14 Radar Intercept Officer in the US Navy.


 

Path Analytics Shouldn’t Be This Difficult!

April 6, 2017

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Have you ever asked your web team which pages customers visit prior to making a purchase (or which actions lead to errors at checkout)? Even better, have you asked them to show you the paths from promotions in social media or other channels that lead to revenue in your brick-and-mortar locations?

Then you have probably exclaimed, “This shouldn’t be so difficult!”

Teradata has a grand, industry-leading vision of what we call Customer Journey: pulling together all the data about your customers and their journeys; analyzing that data for deep business insights; and acting on the results of those analyses or specific events to better engage with your customers. It’s a big vision, and we are already tackling it with some of our most ambitious partners.

But when I talk with many companies about this solution, I also see how a better understanding of incredibly narrow portions of this journey can have a significant impact on the bottom line.

While journey analytics have long been the purview of experts who were well trained on highly specialized tools, we have recently leveled the playing field. Teradata’s Path Analysis Guided Analytics Interface makes it easy to:

  • Visually, interactively build & explore customer paths.
  • Interact with advanced visualizations including Tree, Sankey, Sigma and Sunburst diagrams and traditional bar charts.
  • Identify individual customers on specific paths.
  • Export path-based customer lists for operational follow-up.
The Path Analysis Guided Analytics Interface shows the most common paths to Bill Pay Enrollment in an online banking data set.

The Path Analysis Guided Analytics Interface shows the most common paths to Bill Pay Enrollment in an online banking data set.

With the Path Analysis interface, a business user can dive incredibly deep into their most important paths by simply specifying a few parameters in drop-down menus and clicking “Show Me.” No coding. No specialized skills. Just deep insights in seconds.

Yes, as our Customer Journey story promotes, we can help you identify hundreds of events that are significant in your customers’ relationships with your brand across dozens of channels and give you the ability to respond to those events in real time.

But we can also help you understand why Mindy (or a group of customers like Mindy) didn’t click the link that you thought was so prominent on your website and instead spent 30 minutes talking to your customer support team before returning to the website to again search for answers.

And we can help you identify – visually – the handful of events across channels that most commonly precede a conversion and where there are cracks in the process.

I’ll write more about these types of use cases for path analysis and other business analytics – and the tools we use to make them business-friendly – in the coming weeks. Until then, please remember, “This doesn’t have to be so difficult.”


ryan-garrett-headshotRyan Garrett is senior business development manager for the Americas Analytic Business Consulting group’s Analytic & Data Science UX Team. His goal is to help organizations derive value from data by making advanced analytics more accessible, repeatable and consumable. He has a decade of experience in big data at companies large and small, an MBA from Boston University and a bachelor’s degree in journalism from the University of Kentucky.