Monthly Archives: August 2017

Five ways Analytics and Data Science can add business value

August 30, 2017

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The conversation around big data has grown, well, big in recent times. So much so that it is now part of the day to day vernacular for businesses around the world. Nowhere is this more prevalent than in the thriving technology ecosystem happening right now in the UK. Any organisation can leverage the exponential data growth but size is on the side of smaller businesses who are perfectly suited to act on data-derived insights with speed and efficiency, unlike large organisations that are often less nimble and hindered by clunky, legacy IT infrastructure. All that’s required is somebody in the business that understands the key fundamentals: how to extract business value through data analytics and data science.

However, while a business can be built on a combination of inspiration and perspiration, being able to manage, analyse and interpret data requires a very specific skill set that will actually enable growth through innovation. From predicting and reducing churn to winning business from new and existing customers, the opportunities are endless. Whether you are looking for funding, thinking about the best way to deploy your latest round of investment or a scale-up looking to fuel growth to stay ahead of the competition, here’s five quick ways analytics and data science can help you:

  1. Evidence-based decision making: One of the rarest commodities when a business is in the growth stages, is time. Decisions are taken in days, sometimes hours, that in more established organisations would take months. Young businesses especially spend most of their early stage time probing the market and looking for the right product offering to execute upon. Unlike an established company, one mistake can cost its future so having a data scientist on board is key to being able to gather and analyse data from multiple channels and use proven approaches to mitigate risk and improve decision making.
  2. Test your decisions: Making decisions and implementing change is only half of the battle; it’s vital to know how those changes affect the company. A data scientist can measure key metrics related to important changes and quantify their success (or lack thereof) so that learnings are made and substantiated when it comes to playing back results to investors and moving the business forward.
  3. Perfecting the target audience: Everything from social media profiles to website visitor reports – the IoT ecosystem – contains data which can help a startup pinpoint its target audience – and therefore target them more effectively. Even if it has gone as far as roughly identifying its demographics, a data scientist can identify key groups and consumer patterns with laser precision through using the latest technologies enabling careful analysis of disparate data sources. This in-depth knowledge can help tailor products and services to meet the consumer patterns of the key customer groups.
  4. Making use of the information: Data has to be at the fingertips of every decision-maker and key stakeholder across the organisation, which is usually most people in the business at its early-stage. This is reflected in the data science and analytics space right now with predictive modelling and machine learning both attracting huge amounts of interest – a sentiment underlined by the recent acquisitions of DeepMind and Swiftkey. It is not hard to see why when this particular type of data management enables real-time responsiveness when it comes to translating the raw data into insights, which can be transformed into actionable applications to propel business growth.
  5. Attract the best talent: With a wealth of information on the talent available to businesses today, a data science or an analytics specialist can hunt out the candidates who fit best with a company’s needs. Through data mining the vast amount of data talent already available, in-house processing of CVs and applications, and even sophisticated data-driven aptitude tests and games, data science can help recruitment teams make speedier and more accurate selections saving money in both the short and long term.

You don’t have to be a large company to develop a big data strategy, and as a startup, you can gain a significant competitive advantage when you engage an experienced data scientist to start leveraging your data. Implementing a data strategy in an intelligent, structured way is what differentiates a big data-driven enterprise from one that is simply using data on an ad-hoc basis. And the basics are no different for a small, agile and growing company than they are for the tech industry giants who have been using big data for years. After all, most small companies don’t want to stay small. Data analysis can lead to big things for small business – but it’s much more likely to happen if you go about it in a smart way.


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Eliano Marques, Head of Data Science International at Think Big Analytics.
Eliano has successfully lead teams and projects to develop and implement analytics platforms, predictive models, analytics operating models and has supported many businesses making better decisions through the use of data.

Recently, Eliano has been focused in developing analytics solutions for customers around Predictive Asset Maintenance, Customer Path Analytics, Customer Experience Analytics with a focus in Utilities, Telcos and Manufacturing.

Eliano holds a degree in Economics, a MSc in Applied Econometrics and Forecasting and several certifications in Machine Learning and Data Mining.

The secret to AI in the Enterprise could be little-known transfer learning

August 29, 2017

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Consumers have spoken — artificial intelligence is a profitable industry. From Amazon to Google to Apple, major tech companies have all made inroads, crafting intelligent software — housed in sleek, accessible hardware — that has gotten massive customer attention.

This trend is set to soon move out of home devices, like Echo and Google Home, and out onto the streets, where self-driving cars leverage major breakthroughs in computer vision so passengers can ride easy, knowing their vehicles will “see” and react to objects and road signs in real time without their input. In fact, cars with these features are already popular with consumers, and by 2020 10 million cars with self-driving attributes will be on roadways.

But while there are plenty of ways for consumers to leverage AI, enterprises are asking themselves how they can get in on this wave of innovation. And a big part of the answer lies at the crossroads of computer vision and an emerging field known as transfer learning.

Transfer learning has the potential to unlock dozens of new AI use cases in the enterprise by reusing existing, state-of-the-art deep learning models.

In short, transfer learning is an approach to taking existing AI models and applying them to new data. In this case, we’ll talk about computer vision models that make deductions based on images and visual data and applying that learning on numerical sets of data. It means that businesses could use the very advanced deep learning models that perform computer vision functions, like for self-driving cars, and apply that level of sophistication to a whole new set of, non-image datasets on a spreadsheet.

The way transfer learning works is that the algorithm functions much like the human brain does when looking at a small dataset in Excel. We use our eyes to scan through information and mentally detect patterns. Computer vision applied to numerical data does the same thing, through convolutional neural networks, which look for both high- and low-detail features of an image to help classify what is pictured.

Turning Numbers Into Images

Through transfer learning, a model built originally for computer vision use cases can take a layered approach to curate the new numerical data it’s fed, improving its guesses on patterns in data from a separate domain. Researchers have proven this out, showing that transfer learning that is built using vision-based source data can be applied to a target domain of data that doesn’t contain visual information, like sensor data from internet of things devices for example.

For instance, a 2-D sensor’s numerical readings can be converted into pressure distribution image heat maps. Then the convolutional neural network’s last layer is peeled back, and transfer learning aids the computer vision model to reinterpret the data as a visual.

In terms of use cases, often, for many deep learning applications to work, computer vision included, it needs a much larger dataset to gain new insights from — something many companies have as the world ramps up to having more than 4.4 zettabytes of data. However, using transfer learning, enterprises can extrapolate patterns and real business insights from smaller data sets as well.

Companies can apply existing and sophisticated computer vision models to more traditional use cases like fraud detection and prevention, preventative maintenance, and marketing attribution. These are all areas where enterprises can apply image-based deep learning models to more general, or numerical, data sets. Much of this results in net-new insights, not achievable with traditional machine learning methods or advanced analytical approaches.

It is important for business leaders to know that deep learning, and specifically computer vision, has much wider applications in the enterprise beyond simply vision-based use cases like autonomous driving and identifying facial micro-expressions. In fact, applying computer vision to enterprise problems can unlock dozens of new use cases and business outcomes. It’s by no means easy, but deep learning does provide a path ahead. Not every company can afford to spin up 1,000 new hires in data science, like Google or Amazon — and they don’t have to.

Not only are the state-of-the-art models mentioned available, but they can also be embedded as pre-built functions through popular libraries like TensorFlow and Keras. Therefore, methods like computer vision combined with transfer learning can help level the playing field for any businesses today that want to use AI to get more out of their data.

To learn about some of the use cases that deep learning enables in the enterprise, read my blog here.

This post originally appeared on InformationWeek.


 

MoPatel_Headshot_ResizedMo Patel, based in Austin Texas, is a practicing Data Scientist at Teradata. In his role as the Practice Director, Mr. Patel is focused on building the Artificial Intelligence & Deep Learning consulting practice via mentoring and advising Teradata clients and providing guidance on ongoing Deep Learning projects. Mo Patel has successfully managed and executed Data Science projects with clients across several industries, notably Major Cable Company, Major Auto Manufacturer, Major Medical Devices Manufacturer, Leading Technology Firm and Major Car Insurance Provider. A continuous learner, Mr. Patel conducts research on applications of Deep Learning, Reinforcement Learning, and Graph Analytics towards solving existing and novel business problems.

Hurricane Harvey: How to help in a time of need

August 29, 2017

The devastation Hurricane Harvey continues to bring to Houston and the surrounding areas is beyond comprehension. With many team members, partners and customers who call the gulf coast of Texas home, we at Teradata are deeply affected by this unprecedented level of flooding, high winds and accompanying tornadoes. Even for a nation that regularly faces tropical storms of all types and ferocity, there is no getting used to the destruction that these storms bring.

Reports estimate that 30,000 Houston-area residents will need shelter as flooding lingers. Each of these individuals will need food, water, and other supplies in the short term and ongoing support until their lives can return to some sense of normalcy. The response to this storm will no doubt take months, even years, of effort by many organizations to help Texans rebuild.

Keeping our employees, customers, partners, friends and family who are impacted by this storm in mind, we compiled a list of resources that you can use to aid victims of Hurricane Harvey:

Resources for those affected by the storm, per the U.S. Federal Emergency Management Agency (FEMA):

To find homeless shelters that have beds available, visit the Coalition for the Homeless’ Hurricane Harvey portal.

To register yourself as safe, go to the American Red Cross Safe & Well site.

To report a missing child, contact the National Center for Missing & Exploited Children at (866) 908-9570.

For help with emotional distress, talk to a professional that can help you cope at the Disaster Distress Line: 1 (800) 935-5990 or text TalkWithUs to 66746.

For alerts from the National Weather Service and to get safety and survival tips, download the FEMA Mobile App.

Ways you can help:

The American Red Cross is accepting donations. Its funds go toward providing overnight shelter, supplying victims and comforting kids with basic needs, distributing emergency supplies so victims can clean up their homes, and providing food and water. It is also urging all citizen to donate blood. To find a blood and platelet drive near you, go to www.redcrossblood.org.

Make a donation or sign up to volunteer with Team Rubicon, an organization that deploys rapid response teams of military veterans and first responders to perform operations like debris management, hazard mitigation and home repairs.

Catholic Charities of the Archdiocese of Galveston-Houston is accepting donations to provide food, clothing, shelter and support to Texans. This nonprofit also has a portal where donors can search to see if they are eligible for a donation match through their employer.

The Salvation Army provides immediate emergency response and long-term disaster recovery for disaster survivors.

The Texas Diaper Bank is collecting donations to provide children and the elderly with the supplies they need during this disaster.

Once it has the ability to reopen, the Houston Food Bank will provide a full day of meals for every $1 it receives in donations. Volunteers can also sign up to donate their time.

Austin Pets Alive! donations provide evacuation and shelter for pets of Texas Gulf Coast residents.

Teradata is dedicated to helping our customers achieve great outcomes, and in this time of extreme need, we are extending that dedication to the people of South Texas.

A message from Teradata CEO, Victor Lund

August 28, 2017

The recent events in Charlottesville and Barcelona have caused me to reflect on the importance of leadership in ensuring that we, as individuals and as members of our various fraternities, move forward in productive ways that enhance our lives and our society in general.

Leadership involves many things, but I believe the most important attributes of a leader are integrity, honesty and commitment to the common good. These traits develop over a lifetime. They ultimately reflect one’s beliefs and priorities as to what is important and right. My personal development has been driven by many experiences, and the current situation reminds me of the sixties. There was a great deal of unrest and division in the US.

My wife and I were in our early twenties at the time. I had just finished my undergrad degree and we had just established a new life in a new town when I received my draft notice. I did not support the war in Vietnam; however, I was a proud citizen and believed in many of the things that made our country great and felt an obligation, as a citizen benefiting from these great things, to support the war despite my beliefs about the war itself. As a result, I accepted my draft notice, joined the U.S. Army and spent a year in Vietnam. During that time, I learned so many things, from a diverse group of people, which improved my understanding of people and the world—things I would not have learned had I not had this experience or been willing to embrace people and perspectives that perhaps I disagreed with.

Among the things I learned is that anyone can tear something down, but to build a strong future requires people to stand up for what they believe in and commit to making things better. Improvement is best served by inclusion, valuing others’ opinions and realizing that we all have a responsibility to not only our own improvement, but also contributing to the overall good. Hate and violence are not productive tools in this process. They cloud judgment and reason.

At Teradata, we are about building an organization. We value differences of opinion, put forth in the spirit of improving our company and the people who are the core of what we do. Diversity is not an issue to be dealt with, but rather a wonderful opportunity to reach better solutions because broader views lead to better outcomes (and there’s much research to support this if it’s not intuitively evident). It is not something to fear, but to cherish. To the people of Teradata, I want you to know that I am committed to conducting myself with integrity, honesty and commitment to the common good, and it’s an honor to be given the opportunity to be part of influencing our principles and setting this standard for our company.


TDC_Vic_LundVictor L. Lund has served on Teradata’s Board of Directors since 2007, and has served as chair of its Audit Committee. Previously, Lund was the non-executive chairman of the board of DemandTec, Inc., a publicly-held, on-demand applications company, from December 2006 until February 2012.

Prior, Lund was non-executive chairman of the board of Mariner Health Care, Inc., a long-term health care services company, from 2002 to 2004, and he was vice chairman of Albertson’s, Inc. from 1999 to 2002. Lund was also chairman of the board of American Stores Company from 1995 until 1999 and its chief executive officer from 1992 until 1999. During his 22-year career with American Stores, Lund also held many operating executive positions. He also serves as a director of Service Corporation International and has served on a number of publicly-traded company boards, including Del Monte Foods Company and Delta Airlines.

What It Means To Partner With A World-Class Sales Organization: Part Two

August 28, 2017

 

strongPart 2: Building A Strong Sales Culture

Click here for Part 1: The Role of the Front Line Manager

In this 3-part blog series, I’m excited to share some powerful insights from a lively discussion I recently had with Mike Weinberg—top-performing sales coach on new business development & sales strategies, and author of Sales Management. Simplified and New Sales. Simplified—and Teradata’s own Karen Thomas, EVP of Americas Sales & Services.. Mike, Karen, and I talked at length about what it takes to build a truly world-class sales and services organization for sales professionals while at the same time building a culture of innovation in the greater company.

Highlights from our talk continue here with Part 2, Building A Strong Sales Culture—the importance of embracing competition and, from a sales operations perspective, how to align the rest of the organization to effectively support sales and services.

You’ve heard it before: excellence is not an accident. It takes more than a stroke of luck to transform a marginally performing sales team into a cohesive, dynamic sales operation—a lot more. And it begins with a purposeful approach to creating and nurturing a high-performance sales culture, from the inside out.

As a sales professional, I love the idea of a thriving sales culture, and I believe many companies have the best of intentions to create just that. After all, a sales culture is a philosophy that extends across the business; one that essentially reminds us that we are, in fact, a sales organization—and everything we do is a result of our ability to sell our products.

But I also believe many companies fall short; mainly because of a popular misnomer that sales culture is kind of a soft topic; that is, the belief that a healthy culture means going easy on your people. I think, in sales and services, that couldn’t be further from the truth. A truly healthy sales culture strikes an effective balance between respect and appreciation for the talents of your sales team against firm accountability.

It’s the perfect storm, a sales culture trifecta if you will: It’s all about goals, results, and support.

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A high-performance sales culture equals high-performance in both metrics and achievement—and sales teams love it! Recognition is something our sales culture thrives on, because those that drive results want to be identified as top performers. In effect, their recognition lets them stand out as the people who are driving value not only for Teradata, but also for the customers we serve.

Top producers, by nature, are results-minded and highly competitive. What’s important is recognizing the value of the sales team as a whole. Nothing happens without an extended organization to support sales. They are front and center, so it’s critical to recognize that they’re the ones taking risks—the ones out in front of the customer day after day.

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One of the really exciting things we see at Teradata is a pull-through effect; the sales support that engages from teams across the organization. It’s clear that people want to be part of successful, winning teams. Whether it’s professional services, the product organization or marketing, everyone wants to take part in our successes and winning results.

Empowering a sales and services team with sound credibility—so they instantly gain recognition in areas Teradata excels at to drive business value—is all part of the ongoing support that helps build a foundation for promoting a successful sales culture.

Look for Part 3 of this 3-part blog series, Plan The Attack. Watch the full video discussion here, What It Means To Partner With A World-Class Sales Organization.


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Nate Holiday leads Teradata Sales and Services Operations. He has global responsibility for Teradata’s field effectiveness and go-to-market operations – including account planning and deployment, sales enablement and training, sales technologies, operational analytics, and field deployment. Nate’s team’s overall goal is to optimize the Teradata field team’s ability to help our customers take advantage of data and analytics to deliver business value.

Nate has 13 years of management experience in scaling business and management consulting organizations. Nate’s teams have generated more than $1B in enterprise revenue and more than $3B in customer business value by leveraging their data assets and analytics.

Nate holds a Bachelor’s degree from Brigham Young University.

It’s time to wake up to the big data gold mine

August 25, 2017

cloud analytics Marc Clark TeradataOriginally featured in IT Pro Portal.

There is no excuse for any modern business to operate without a big data strategy. We have moved beyond discussions about unmanageable data volumes to a time where companies can easily adopt a method to analyse and garner insights from their data to make more informed business decisions.

So, in a world where organisations can better predict the future and prescribe solutions for their customers, products and services, why would they go at it blindly? Not only does it sound risky, but at a time when businesses can employ a tailor-made big data strategy, it borders on irresponsible.

However, laying the foundations that enable successful implementation of complex programmes remain the key: producing a business objective-driven strategy and roadmap coupled up with the assessment of big data capabilities in-house. With the foundations in place, businesses can start to embrace the potential of their data and eagerly look to capitalise on the benefits it can deliver to drive their businesses forward – faster, further, and more competitively.

Yet, despite the tremendous strides made in the adoption of big data, many private and public sector organisations are still playing catch-up. Although it has become one of the most powerful phenomena in an increasingly competitive market, many are only just waking up to the realisation that data is like digging into a goldmine – an extremely valuable asset that needs simultaneously protecting and exploiting.

But, done correctly, it can have a hugely positive effect across the entire business. To give you an example, here are five real-world examples of key areas where big data has delivered real business impact:

Operations

Businesses looking at operational improvement should look no further. From decision-making to innovation, virtually every area of a business can be improved significantly through the adoption of data management. Increased informational transparency, improved managerial decision-making, more accurate customer analysis, risk minimisation, product development and supply chain efficiencies – the list goes on.

However, even if a business recognises the benefits, it must ensure it has a scalable data lake equipped to respond appropriately to the changing nature of how the business is using data. Companies need to understand that how they utilise data today and in the future will have a direct impact on realising the wider business needs and goals. Amazon, one of the early adopters in the use of data analytics, was the first company to have patented the shipping of goods before an order has even been placed to increase delivery efficiency and cost savings.

The e-commerce giant is also exploring the use of drones as means of delivering smaller goods by dropping them into gardens, thus improving the effectiveness of logistics.

Customer Experience

The fundamentals of business haven’t changed – it’s still about meeting customer needs and managing the increasing pressure of being able to respond to them in real-time. The more useful information businesses have to facilitate this through analytics and data science, the better.

Luckily, there is more information available than ever before. Data mined from websites and social media, to name a few sources, can be used to develop 360-degree view of consumer behaviour and patterns. However, the information is only useful if the data is collected, collated and analysed through the lens of the wider business strategy. If done correctly, big data will tell you more about your customers than you ever thought possible.

For example, the Burberry Group is using radio frequency identification (RFID) tags in its stores to create a richer shopping experience by showing a video of how the item was made and offering other products complementary to it in order to boost sales.

Business Development

Using big data to the best of its potential requires adopting a company-wide approach – not just in one or two areas that are of strategic value to the business. The reality of today is the emergence of big data provides a competitive advantage for those businesses engaging with it properly.

In fact, those that harness big data faster than their competitors will have almost an unfair advantage. According to a report published by The McKinsey Global Institute (The opportunities in business and government of using big data), the use of big data is becoming a key way for leading companies to outperform their peers. It estimates that for example, a retailer embracing big data has the potential to increase its operating margin by more than 60 per cent’.

But, it is not limited to this sector, there are other tangible examples across all industries from financial services to manufacturing, retail, telecommunications, logistics and public sector. When it comes to improving performance, nowhere was this more prevalent that in the German FIFA World Cup Win. Germany analysed video data helping to reduce the average possession time from 3.4 seconds to 1.1 seconds. That made all the difference when they defeated Argentina in the final.

The Bottom Line

All in all, when used and analysed properly, data from various sources can solve a number of business challenges that companies face. Experts advise automating data collection, generating insights out of acquired information and taking relevant actions that can lead to desired outcomes.

However, when dealing with enormous sets of data, there’s a risk of being caught out by it unless you have a plan for managing and monetising your data. Therefore, creating a short and long term strategy aligned and being flexible with the evolving is key to a successful implementation.

Leadership

In many cases, the adoption of big data still needs driving through – ideally by having people around the boardroom table that are tuned-in to its potential. This has led to the rise of the Chief Data Officer (CDO) and most recently to CAO (Chief Analytics Officer) – the ones who bring together the technology and business strategy, who understand the value big data adoption can generate and is capable of articulating its benefits to the wider company and most importantly to the board holding the budgets.

These internal evangelists and experts of the data world understand that tomorrow’s leaders will take a role in shaping and directing big data projects. They are the force to champion the invaluable cause, its relevancy to the wider business, the business returns and understand the parameters needed for success.

Even President Obama understands its value. Big data analytics was the president’s go-to tool to beat the competition, enabling insights to be gained on what people were discussing and a platform to respond to their concerns in real time.If you do not have a big data strategy in place, now is the time to make the move.

It can fundamentally change the way your organisation runs for the better and, regardless of your sector or industry, is able to add value across the business, generate new revenues and place you ahead of your competition. So what’s stopping you?


Mike Merritt-Holmes

Mike Merritt-Holmes, Chief Strategy Officer, Think Big Analytics a Teradata Company

Mike, Chief Strategy Officer and Co-Founder, has been a Big Data evangelist for over 7 years. He has a proven track record of developing and implementing some of the most complex and exciting solutions for leading enterprises in the public and private sectors. Being a renowned leader in the Big Data community, he dedicates his time and expertise to provide thought leadership to our customers and also to drive the company’s high growth strategy. Mike also regularly speaks at leading industry events and is a board member at techUK and a board advisor for Triggar. With his unparalleled knowledge and the drive for innovation, Mike has led a team of Data Scientists to create a number of new, ground-breaking products such as brandmatcher.

 

Henry Ford didn’t build a faster horse – and neither should you

August 24, 2017

oilfieldThree mistakes the oilfield keeps making about Big Data and what to do about them

I’ve often heard “the upstream industry has handled big data for ever. G&G data is huge, so we are the experts”. Even if the basic premise was correct (it’s not: lots of other industries work with larger datasets), size isn’t everything. My response used to be that we don’t have big data; we have lots of data that lives in silos which limit its value. That’s true, but it doesn’t mean much to a lot of people. Instead, I’ll offer up an analogy.

One of the greatest innovators of the industrial age, Henry Ford is renowned for having had the vision to give people what they didn’t know they wanted – he is famously quoted as saying that if he had asked, people would have told him they wanted a faster horse. As it turns out he didn’t say this, but he did something far more powerful.

His genius lay in the adaptation and integration of existing designs, tools, and processes to revolutionize automobile manufacture. He delivered on something that he actually did say; a vision of a mass-produced car for “the great multitude…constructed of the best material…so low in price that no man making a good salary will be unable to own one.”

Ford’s game-changing idea was not the improvement of the car itself, but that a good, reliable car at a fair price would sell like crazy – and the world was never the same again.

A successful big data story follows this same principle: integrate data, tools and processes to revolutionize business performance. The idea isn’t to just incrementally improve existing systems, but to explore, discover, and implement new ways to deliver value. This is where we make our first mistake.

RS2848_shutterstock_397577668Mistake 1: Thinking small about big data

Forget about technology-focused initiatives to give faster answers to known questions. What are the major priorities of your organization? What kind of information is needed to support decisions to achieve those goals? What range of data is needed to create that information? The system and processes that make that data available should be flexible, connected, and extensible to answer the next set of questions that power your business.

It’s what Walmart did when they started to connect store transactions with demographics, weather patterns, trucking, vendor supply chains, and more – to enable them to optimize inventory and logistics, and beat everyone on price.

Closer to home, it’s what ConocoPhillips did when they decided to connect wellhead SCADA, transport logistics, equipment configs, maintenance planning, and more – to optimize Eagle Ford production and boost their balance sheet with a more accurate EUR.

In both of those cases, success demanded a focused effort to implement analytics at scale. It didn’t happen overnight, it wasn’t a single project, but a comprehensive program for which no single part of the organization was wholly responsible.

Accelerating existing processes may get you a faster horse, and that’s probably not a bad thing, but don’t be surprised if you’re beaten by a Ford. It may well require a cultural shift, which is always difficult. Which bring us to our next issue…

Mistake 2: It’s an IT thing. Just give me an easy button

Give me what I want with no effort on my part? I’m in! Buying from Staples may well be as easy as their marketing claims, and the appeal is undeniable. But nothing is ever that simple under the covers – especially making something “easy”.

Lots of technical systems are unnecessarily complicated, not just in terms of software, but in the wider process. Studies show that engineers spend over 75% of their time finding and preparing the data they need to do their jobs because systems are so disjointed. Naturally, this creates huge frustration.

Taking a big data approach can drastically reduce data sourcing time as well as adding analytic insight.

It may seem quicker to sidestep the “official” system and do your own thing on your desktop, or in the cloud, but a bunch of point solutions won’t fix the problem, and will never be an easy button. Oh…and free open source software? Make sure that you understand that we’re talking free speech, not free beer.

To build and maintain an analytic capability at scale is a significant undertaking that can only succeed if all parties are fully committed to its success and trust in their partners. This industry should be very comfortable with this kind of setup, because we see it in other areas of our operations.

Operators stopped doing their own drilling and completions long ago, choosing instead to focus their expertise on how to best develop assets and pay service companies to perform the work. To get their gear to the wellsite, service companies don’t build their own trucks; they extend a base platform from a specialist truck builder. And those trucks are assemblies of subsystems from numerous suppliers, each with their own expert knowledge of brakes, hydraulics, navigation systems, etc.

It comes down to understanding between businesspeople and IT support people, each trusting the other’s expertise to do their part without imposing their opinions unduly on the other. Without such trust, a “solution” for one group can be a nightmare for others, and a losing proposition for the organization. Get the balance right, and analytics at scale can deliver enormous value.

RS3865_shutterstock_570062134Mistake 3: We’ll start as soon as we’ve fixed the data

A few years ago, I sat through a presentation given by a major E&P representative, reviewing a project called “Backbone”. This was a mammoth data management project with the noble goal of cleaning up and modeling all of their global operational data so that their engineers could analyze it. After seven years on the project, the speaker told us, they were now “looking for some quick wins”!

This kind of ocean-boiling approach never works. Don’t even try. Yes, data quality is always a problem, and yes, you have to clean it up if you want to support the best decisions, but the way to do this is to start small and focus on answering a specific problem, working together with subject matter experts and fixing things as you go.

If that sounds contrary to my earlier advice to not think small, it’s not – provided you are committed to the delivery of an enterprise solution. Starting small will highlight your data issues and probably show them to relate to data access and availability, not quality. Even more important, you will quickly realize value through addressing a specific problem, and be able to build on that solution to answer other questions with the same data platform.

For example, a US land operator saw excessive bit failures in their mid-continent operations. Over the course of a couple of years, numerous suggestions as to cause had been put forward, numerous equipment variations tried out. Everybody had their pet theories, but none had come up with an answer.

Everything else having failed, they brought all of the real time, well survey, and well operations data together in an analytic system capable of a foot-by-foot evaluation across thousands of wells.

Within six weeks, the answer was confirmed and an operational solution defined. Most of that time was spent in data sourcing and cleanup processing. If they had waited until they had a perfectly curated system for managing all of their data for all of their operations before starting the analysis, they would still be suffering the same failures today.

The legacy of such a project? A rich reserve of high quality data and a process for ongoing collection that can now be used to answer a range of other operational questions. And if the next question requires, say, maintenance data from ERP, that can be added and the range of answerable questions expands further.

The biggest mistake of all?

There is a common thread that connects these mistakes. It’s the notion that if we can just clear a specific hurdle, we will have a permanent fix and a permanent advantage.

Coincidentally, it is the same thing that brought Ford’s dominance in the nascent automobile market to a screeching halt in 1927. Ford changed the game, but then he couldn’t change his winning formula to compete when others added new rules. No matter how cheap he made the Model T, the new features and choices that GM added to Chevrolets rendered Ford’s only product obsolete, and he had no plan B.

The speed of technical development will always outpace the ability for a particular technology solution to deliver a permanent advantage. If you disagree, I’ve got a lightly-used Blackberry that you might like.

Ask yourself or your leaders how your organization is meeting the data challenge. If the answer is anything like “we researched technology some time ago and chose <insert vendor name here> as our solution provider”, or conversely “we researched this some time ago and chose to develop our own custom tools”, then take a note of where the exits are.

To my way of thinking, a textbook answer would be along the lines “from top to bottom, we see data as a vital asset and we’re always looking for new approaches to create useful information that we can use to support our decision-making”. I’ve never heard that response from an oilfield company, but if you do, perhaps you’ve found the true Digital Oilfield company – one that will thrive in the Information Economy.

This article originally appeared in Infill Thinking.


MugshotSteve Matthews started his career in the oilfield in 1990 running downhole tools for Halliburton at drilling sites around the world. Over the next two decades he worked his way from the field into management, and Halliburton’s software and app development department. Seeing the opportunity presented by big data technologies to improve operational performance in the oil business, he joined Teradata five years ago to lead the Oil & Gas Consulting team.

The oil business has been through enormous turmoil since the oil price crashed in late 2014. Throughout this period, Teradata’s Oil & Gas team has helped clients develop and implement new technologies the right way. While the industry’s adoption of enterprise analytics has been slow, interest is growing as the recovery becomes more established and companies realize that they must make their data work for them. In this post, Steve highlights three common mistakes to avoid.

 

‘Game’ theory: Perfecting in-app purchasing through analytics

August 23, 2017

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When you think about buying and selling, it’s usually about a tangible product or service. But what if the job is to sell new forms of reality and value itself? If you’re an online gaming company, that’s exactly your goal: to create and sell virtual worlds of escape and adventure, with the “value” increasingly coming through analytics-fueled “in-app” sales of virtual accoutrements — like weapons upgrades or special outfits for player avatars.

Analytics to navigate change and competition

It turns out the rise of in-game, or in-app, purchasing is fueling a massive business; online gaming companies generate $109 billion in revenue annually in a low-overhead, high-profit industry. What’s more, some companies are now looking to analytics to further optimize the in-app marketplace — specifically, they’re positioning analytics to automate many of the routine decisions about when and where to put in-app purchase offerings, and at what price.

This is worth exploring as a powerful example how big data and automated decisioning are helping an entire industry adapt to a changing business model. The past two decades have seen a massive shift in the gaming industry from subscription-based pricing to in-app purchases. World of Warcraft, for instance, became the industry’s first billion-dollar game back when it was enjoying wild success from subscriptions a decade or so ago.

As consumer preferences and demands have changed, WoW has since adopted a hybrid model of subscription and in-app purchases to generate income from its millions of players worldwide. Other games, like Hearthstone, now rely primarily on in-app purchasing. The challenge is: an enterprise level in-app marketplace requires countless low-level decisions that are just crying out for automation.

If you have only human analysts busy with these routine, tactical decisions about what to sell and where, it becomes  to keep up when you are operating with millions of players. The right analytics architecture, on the other hand, can parse and curate high volumes of behavioral game play data to help decide the best product, placement and price for an in-game purchase. This frees your people up from minute tactical decisions — so they can focus on bigger and more strategic priorities.

Staying true to a mission

I have a contemporary who is the chief technology officer of a major online gaming business. When I visited him recently on the company’s campus, larger-than-life statues of game characters loomed on the grounds outside the main building. It reminded me how seriously he and his colleagues take their work. To developers and players alike, these characters and worlds are real — at least in terms of the time, effort and money that’s invested.

For these worlds to stay credible and compelling, they need to be fair; we must make sure in-app purchases generate revenue without tipping the balance of power. Analytics can help sift through rich behavioral data to see who’s winning, who’s losing and where an in-app purchase might help —- but in ways that don’t tip the balance of power so fully that people can buy their way to what he referred to as “guaranteed wins.”  

“All these issues of fairness, marketing and functionality need to happen with high concurrency at a scale of more than 220 terabytes of game-play data per day for a typical game,” he explained. “So, the more we can automate that flood of tactical decisions, the better those decisions will be and the more we can free up our people to think more strategically, make more games and come out with new iterations and better features for existing games.”

Broader implications

Given how in-app purchasing is an increasingly common tool in many industries, especially retail — the benefits I’m describing can be applied more broadly than just the gaming industry. More and more companies are realizing analytics is the only way to manage the amount of data, processing power and automated decision-making needed to position and price in-app products at scale.  

And there’s a bigger lesson as well: The way analytics can automate the in-app marketplace is just one example of the ways analytics can be used in all sorts of fields to take over routine decisions. In many kinds of industries — not just gaming, but also manufacturing, marketing, telecommunications, retail and any number of other sectors — autonomous decisioning that’s driven by sophisticated algorithms can free up people’s time for important strategic decisions versus countless tactical ones.

Adapting to changing circumstances and autonomous decisioning, it turns out, are two critical elements of an analytics approach I co-developed with the Northwestern Kellogg School of Management’s Mohan Sawhney. I’ve written a lot about our “Sentient Enterprise” analytics capability maturity model, and there’s even a full-length book that’s coming out soon. It’s just one way of looking at the challenges of big data, but whatever your approach, it’s hopefully designed to embrace agility and position data to help with, and even make, decisions at the speed of business.


Oliver_RatzesbergerOliver Ratzesberger is executive vice president and chief product officer for Teradata. Oliver leads Teradata’s world-class research and development organization and provides strategic direction for all research and development related to Teradata database, integrated data warehousing, big data analytics, and associated solutions.

Prior to this appointment, Oliver led the software teams for Teradata Labs, including the Teradata Database, Aster, Client tools, and Viewpoint as well as Hadoop integration. Joining Teradata in early 2013, Oliver has a powerful and impressive background in leading big data analytics initiatives and technology transformation for Fortune 500 organizations. For example, Oliver drove a large analytics effort to consolidate systems into a newly redesigned Unified Data Architecture. He has a successful background in driving analytics culture and advancing the value of high-volume and high-velocity analytics solutions. Oliver spent seven years at eBay, where he was responsible for its data warehouse and big data platforms. During his tenure at eBay, he led eBay’s expansion of analytics and was responsible for the co-development of the Extreme Data Appliance as part of eBay’s Singularity project, leading Hadoop engineering teams and driving the integration of Teradata and Hadoop.

Blockchain in your supply chain: What’s all the hype about?

August 22, 2017

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Have you noticed lately that supply chains are morphing into all kinds of new chains? We now call the temperature-controlled supply chain the “cold chain.” The process of adding value to activities within the supply chain is part of the “value chain.”  Now along comes the new kid on the block — blockchain!

What exactly is blockchain? It is basically a database that runs across a global network of independent computers. It serves as an open ledger, where every transaction is recorded and available for all companies and participants to see and verify. By providing a singular view, a blockchain eliminates the need to transfer information between organizations. Gone are such things as emails, spreadsheets, data feeds and the like. Bottom line, blockchain helps to reconcile any differences in data between suppliers and customers. Most importantly, the blockchain database cannot be changed without all partners in agreement.

Now that we understand blockchain, what are the implications upon the supply chain? Simply put — enormous! Experts believe there are many ways blockchain technology can applied both in the U.S. and globally. As the Harvard Business Review article “Global Supply Chains Are About to Get Better, Thanks to Blockchain” shares, there are numerous opportunities for blockchain within the supply chain. Walmart is working in Beijing to follow the movement of pork in China with a blockchain. Mining giant BHP Billiton is using the technology to track mineral analysis done by outside vendors.

Tracking and tracing of goods across multiple parties is by far the most logical use of blockchain. The current processes are cumbersome and lack reliability. Blockchain offers the ability to embed the origin and transfer points, destinations, and lot codes in the chain.

Every supply chain today relies on EDI as the foundation for communication of information. This batch process inherently involves latency. Imagine blockchains that are by nature updated in near to real time. Can blockchain replace EDI?  While is it a stretch goal, it is certainly coming in the not-so-distant future.

So what can blockchain do for your supply chain? Don’t sit back and watch. Get busy and create your new chain today!


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Scott Collen, Senior Business Analytics Consultant, Think Big Analytics a Teradata Company

Scott Collen is a Senior Business Analytics Consultant for Think Big Analytics, a Teradata Company, providing data analytics consulting to manufacturing, retail, and logistics & distribution organizations. Based in Dallas, Texas, Scott has over 25 years of combined industry and consulting experience including a concentration in Distribution Center Operations Management, Operational Improvement, LEAN/Six Sigma, Supply Chain Management and Engineering, Technology Consulting, and 3rd Party Logistics. Scott’s recent projects range from Supply Chain Cycle Time Reduction to Sales Order Forecast Variability Analysis.  He has a BS in Industrial Engineering from Texas Tech University.

What It Means To Partner With A World-Class Sales Organization

August 21, 2017

Part 1: The Role Of The Front-Line Manager

As a sales professional, you know your strengths—and what it takes to thrive and succeed at what you do best. You also know that your success is directly tied to the innovation an organization creates to drive a strong sales culture.  Innovative companies innovate in every area and leadership within the sales culture is the key to everyone’s success.

In this 3-part blog series, I’m excited to share some powerful insights from a lively discussion I recently had with Mike Weinberg—top-performing sales coach on new business development & sales strategies, and author of Sales Management. Simplified and New Sales. Simplified—and Teradata’s own Karen Thomas, EVP of Americas Sales & Services. Mike, Karen, and I talked at length about what it takes to build a truly world-class sales and services organization for sales professionals. To kick things off, it started with an exchange of ideas on the Role of the Front-Line Manager and their value-driving behaviors.

One of the main goals of any sales enablement strategy is to empower teams to have successful sales conversations, right? True. But it goes deeper than that. It also includes ongoing professional development to continuously improve sales savvy—and that begins with front-line managers. They are the key lever in the organization, a critical part of the sales enablement process, the ones that drive sales culture.

The influence of a talented front-line manager is far reaching, impacting sales teams of 6 to 12 or more—in effect, they’re multiplying themselves into the people they support. This makes their role core to the business, focusing much of their time on coaching and development to drive performance across the organization.

A common challenge among busy sales managers, however, is that so few spend their time on the highest value activities—like ensuring the right processes are in place to effectively support a sales team. This importance can’t be over emphasized, as that’s where organizations get the scale they need to drive business results.

WeinbergTeradata builds its incentives around helping sales managers drive engagement, so they feel connected to the importance of developing talent. So while it’s fantastic to attract superhero sales athletes, Teradata looks for a little more. We seek individuals capable of coaching and mentoring, and sharing their talent across the team to help everyone up their game.

We know it’s important to see scale, but it can’t always be achieved if folks aren’t empowered to coach people up.

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We are all in to support Teradata sales and services to help make sure we’re successful, along with the right tools, products, services, and capabilities. But the front-line sales manager plays a critical role in helping us drive a winning, high-performing sales culture. We have a rally cry within Teradata, “We’re all in!” You can feel this energy across the sales organization, from behind the scenes to the front line—we’re in this together to drive success for Teradata while creating value for our customers.

Look for Part 2 in this 3- part blog series, Building A Strong Sales Culture. 


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Nate Holiday leads Teradata Sales and Services Operations, He has global responsibility for Teradata’s field effectiveness and go-to-market operations – including account planning and deployment, sales enablement and training, sales technologies, operational analytics, and field deployment. Nate’s team’s overall goal is to optimize the Teradata field team’s ability to help our customers take advantage of data and analytics to deliver business value.

Nate has 13 years of management experience in scaling business and management consulting organizations. Nate’s teams have generated more than $1B in enterprise revenue and more than $3B in customer business value by leveraging their data assets and analytics.

Nate holds a Bachelor’s degree from Brigham Young University.