Big data in little Byron

Natalie Mendes
Natalie Mendes|May 16, 2012
img credit: http://www.lighthouses.com.au/Images/CapeByron.jpg

The BIG lighthouse at Byron Bay

I recently spent the weekend in the beachside town of Byron Bay to escape the madness of the BIG cities around the world that I had been visiting over the last ten weeks. Cape Byron, the most easterly point of mainland Australia and home of the iconic BIG lighthouse, is the first place where the sun rises in Australia. Why is this so relevant to a big data discussion? Because I thought I had escaped the BIG world of BIG DATA … at least for a weekend. How wrong I was. Everything I experienced during the weekend had some association with big data and the three Vs that are often used to characterise it. Let me explain.

Volume

My first experience was with BIG airline DATA. Given I have been on and off planes (average four flights a week) in the last six months, I had collected many loyalty points along the way, but was too busy to review my loyalty status. So when I checked in at the desk to get my flight to Byron Bay, the customer service agent provided me with great news. I had moved up in the world to another level in the BIG loyalty program. I felt special as if I was the only one. Millions of people fly each day and leave a valuable volume of transactional and behavioral data. For airlines to turn this BIG DATA asset around in minutes makes the difference between making each customer feel special or losing them to the competitor. There is simply no excuse to lose a customer this way?

Variety

The BIG DATA experience continued when using the airline’s loyalty points and hiring a car.  My loyalty program has been busy collecting information from a variety of sources, in particular affiliate rental car agencies where I had claimed loyalty points in the past. What was relevant was the “Rental Cars” offers. This to me was the right information at the right time as I needed to hire a car for the Byron escape. So of course I did with my airline loyalty program. Naturally, being a marketing analyst, I recognised this as a great example of BIG loyalty DATA being used in a ‘cross-sell’ activity. The rental company managed to squeeze some extra dollars out of me, but I didn’t mind because I received another loyalty ‘reward’ and I felt special. There was now a variety of data being collected about me. Do all companies take advantage of their BIG DATA to create strategic assets? If not – why not? There seems to be big benefits in real dollar terms.

Velocity

Let’s look at my next BIG DATA in little Byron experience. Given I had travelled to many countries and many Australian states recently, there was significant irregular activity happening on my credit card, well so my bank thought. There were many different transactions in different places worlds apart. So of course when I went to pay for the BIG breakfast I had just happily consumed, my transaction was declined several times, only to discover after I called the bank that their fraud system had stopped activity instantly – that’s BIG banking DATA in action! My credit card details had been hacked and yes – there was fraudulent activity happening. I appreciated the velocity in which the data was collected and the speed to react to this critical issue. How much more money could I have lost if this was not detected in time?

So what is the big hype about BIG DATA? It seems like we’ve been trying to work with this for a long time. A company has BIG DATA when the volume, velocity and variety of data exceeds the organization’s storage or computing capacity for accurate and timely decision making. Is this where organisations need to think about high performance analytics? How will your business survive if this is not one of your strategic goals?

 

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Culture, the real roadblock to unlocking big data value

Vincent Cotte
Vincent Cotte|May 14, 2012
Driving an analytical culture

Driving an analytical culture

We started off the conversation on big data and the Chief Information Officer (CIO) challenge, by announcing that high performance analytics and information management strategy will level the playing field for competitive advantage.  After meeting with over 50+ CIOs in Australia, New Zealand, Singapore and Malaysia I discussed the challenges and more importantly, the opportunities CIOs saw to grab a seat at the boardroom table.

Now it's time to air the dirty laundry and discuss what is holding companies back from big value.  It's not just about being overwhelmed by data, nor is it just about technology innovation, it's clear that CIOs are struggling with culture.

A CIO from an Australian-based energy company discussed his cultural challenge like this:

Until recently we were grabbing data about consumers and the network quarterly - very manual and time consuming.  The data was gathered for a specific purpose; namely maintenance, knowledge and billing. The advent of smart grid and smart metres sees data now coming in approximately every seven minutes and the data streams contain so much more information than before.  As in the past, we still capture and store the data however now the question is so what? What do I do with it all? The answer was to change our mind set and bring in talent and skills from outside the organisation.  This started at the very top, adding a banking executive to our board who came from an analytically-mature culture. He added the spark and drive to making decisions based on the data and also looking for new insights that were hidden in the data.  As a result we have also brought in more analytical talent from financial services to assist operationally.

As I met with CIOs, four recurring trends continually popped up around culture and the roadblocks to big data value. They were:

  • IT  control is shifting to business enablement – the advent of intuitive business analytic interfaces and mobile deployment has seen the business have more control over reporting and drawing insight, leaving them with a taste for more.
  • Business need it today -  the continual growth of consumers to online mediums is reducing the time in which organisations have to react.  This reduction in the window of opportunity places pressure on detecting negative interactions like detecting fraudulent credit card transaction or seizing a more positive outcome by proactively notifying of a potential failure in an electricity substation.
  • Move from gut instinct to data driven decisions – organisations are moving more towards allowing the data to speak for itself through the use of business analytics.  Whether its finance, marketing, manufacturing or supply chain operations, data is being mined to forecast and optimise decision making.
  • Consumers demand personalisation – this has been focused on marketing use cases.  One Asian government CIO hit the nail on the head when he bluntly discussed his disgust with the banks questioning receiving unrelated and irrelevant spam. His perspective was that consumers have given up their data through social sites, cookies, online registrations, census data, discussion forums, loyalty programs and competitions; the least organisations could do is be personal, relevant and contextual.   Being more targeted is key to organisations that are excelling at customer experience and being a more profitable business along the way.

Here are some examples of how leading companies are using data to drive customer value:

It is clear that culture must embrace these trends  in order to evolve and truly unlock the potential value held in big data.  In upcoming posts we will discuss the skills and capabilities successful organisations are developing to drive big value.

QUESTION: How important do you think culture is to your big data programs? Tell us in the comments below.

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Big Data Seats CIOs at the Boardroom Table

Vincent Cotte
Vincent Cotte|May 8, 2012

After debunking the myth that big data is just for the big end of town I set out on the road to listen to what is happening locally.  For the past two weeks I have met with over 50 Chief Information Officers (CIOs) around Australia and Asia discussing their 2012/13 priorities.  I thought it would be useful to hear about the specific goals and challenges facing them as they move from a mindset of keeping the lights on to that of a strategic seat at the boardroom table. It was very refreshing to see that the CIO was working hard to align IT capabilities to business goals.  A quote from Gartner has certainly sparked some action in CIOs.  The quote discusses the moving trend in Chief Marketing Officers to spend more on IT - predicted to surpass CIO spending by 2017.

Their line of business peers are asking for an increase in the trustworthiness of insight, increased accuracy of data, insight delivered in near real-time, and critically, delivered when the customer is interacting with company.  The big data hype is applying even more pressure to costs but also asking questions as to whether CIOs have the new capabilities to derive value in a consumer-educated world.

Some examples of what the specific lines of business were trying to achieve are:

  • Marketing arm of an insurance company looking to make offers based upon understanding of static history and then using context of current situation and interaction to make a more relevant offer.
  • Risk officer of a bank looking to prevent fraud in real-time to reduce costs in detection and investigation.
  • Chief financial officer of a gaming company asking how to deliver a more personalised experience to punters based on history and current playing habits.
  • Head of marketing for a telecommunications company looking to obtain a better understanding of consumer needs by analysing social and online data and then combining that with their existing CRM and transactional data.
  • Chief operating officer of a transport and logistics company looking to improve the way it reschedules resources, freight and customer expectation based on unforeseen events like the tsunami in Japan.

Here are the top five challenges facing CIOs in trying to deliver to these business goals:

  • Top of the list is data governance.  Specifically the need to automate the way data is martialled and transformed from data entry through to insight and action.  As one CIO put it, “Data is cheap.  Mining the data is expensive and timely.  We need to optimise the data supply chain”
  • Secondly, data quality is back on the table.  A government CIO remarked, “a move towards evidence based policy means data must be trusted and reliable, thus bringing into scrutiny the quality of the data”.  With all the different applications and citizen or customer touch points how do we ensure quality?
  • Close third is an inability to meet performance requirements of the business with the existing platform approach.  Interestingly the problem was not just in shortening a one-off time-to-delivery but in making sure insight could be delivered regularly in shorter intervals.
  • Fourth is an old chestnut - single customer view, asset, product, vendor or employee.  The difficulty is that the customer is strewn across different lines of business with differing details.  A retail bank CIO gave an example of why it is important. “We have a customer; Maryanne Smith for a credit card, Mary Smith for personal loan, and Joe and Mary Smith for home insurance.  Currently the bank are marketing to approximately 20 million individuals when it’s clear we only have around 5 million unique customers. There is a lot of needless cost and effort spent on irrelevant marketing offers with low response rates. Haven’t we all experienced that? So what does that mean to customer experience and churn?”
  • Fifth is the inability to manage and harness value from unstructured data.  While some had experimented with Hadoop none of them had successfully implemented value.

If this sounds like you, then take comfort in knowing there are options out there.  It was clear that making better decisions relied upon increasing the ability to deliver more timely, reliable, trusted and accurate data.  While we have been recently discussing the power of high performance analytics it is clear that data governance, data quality, master data management and data integration are seen as the key to unlocking sustainable value from business analytics.

We've got more to come as we go explore examples of how local companies are addressing these issues to drive value from big data.  In the meantime let us know how your how your data governance initiatives have delivered value.

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High-performance analytics. So what?

David R. Hardoon
David R. Hardoon|April 29, 2012

Define big data - image from http://www.freedigitalphotos.net/images/view_photog.php?photogid=1836I've somewhat had enough with the misuse and overuse of the terms high performance analytics (HPA) and big data. When approached and asked, "How about you write in your own words about HPA," I had no hesitation or qualms in my "Yes, please!" near instant response. It led me tothink about  the misuse and overuse of these terms, specifically HPA and big data ... and so there are two things I would like to do:

  1. Disambiguate on-going terminology.
  2. Focus on the really important objectives that would justify such technology.

In an attempt to take an initial unbiased perspective, I revert to a popular source of online knowledge, Wikipedia, which attempts to define big data as, “In information technology, big data consists of data sets that grow so large that they become awkward to work with using on-hand database management tools.” ("Awkward"?! Last I've checked, I wasn't planning on dating my data). Furthermore, and interestingly enough, there is no Wikipedia definition of high-performance analytics. The closest definition of high-performance computing is for supercomputing,  which states, “A supercomputer is a computer at the frontline of current processing capacity, particularly speed of calculation.”

First, and foremost, two terms which are most commonly used in interchangeable fashion are high-performance analytics and real-time. I believe it is important to emphasise and clarify that high-performance analytics is not real time and vice versa. High-performance analytics is the facilitation and capability of building (developing) analytical models faster than if it were done outside of an HPA environment. For example: marketing organisations, such as Catalina Marketing, help retailers identify what coupons, advertisements and information messages to hand out customers at checkout. Catalina, realised that their current process of capturing shoppers behaviour was unable to catch up with changes in shopping pattern. Incorporating HPA allowed the time it took to model and analyze data related to around 250 million transactions processed per week to be reduced from over a month to just days, which under the current definitions would not be construed as ‘real-time’. However, the underlying business benefit meant that Catalina is better equipped to model the change in customers’ behaviour in an on-going manner so to be better able in offering more relevant information at point of checkout.

So what is real-time you ask? Real-time is the ability to score data in a near-instantaneous manner. Reverting back to the Catalina example, they took several days to build the customer behaviour models (not real time) which were then used to score (real-time) customers at checkout. The requirement for real-time scoring may not entail a requirement for fast model development (where HPA comes into play), and vice versa. In Catalina case, they needed both. Most organisations require real-time scoring capabilities, as it allows the surfacing of knowledge relevant for decision making processes ‘now.’ For example, is this credit card transaction or broker trade fraudulent? Rather than having a quarterly, or even weekly financial risk assessment, to be able to identify what is the risk for loans, products, customers, etc. – now, before it becomes too late to action on.

Finally, deliberately, we have the overuse of the term big data, which quite frankly, irks me. Instead of focusing so much on big data, we should be focusing on relevant data: the identification and extraction of patterns within the data that are relevant to decision making process (regardless of size).

Nonetheless, any conversation about the aforementioned terminology and technology should be an outcome of a business objective discussion, not the initial starting point. So the next time someone wants to talk to you about real-time or high-performance analytics pose a single question, "Why do I need it?" If their reply focuses primarily on the word "big ..." feel free to email me or comment below. The focus of our conversation will be less about hype and more about your business challenges and the value of your data.

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The value of high-performance analytics

Evan Stubbs
Evan Stubbs|April 25, 2012

Like Vince said a few days ago, you don’t have to be Goliath to see how the intersection of big data and high performance analytics creates competitive advantage. Let’s also be realistic though: compared to someone like Walmart, pretty much everyone’s David! While Telstra and the Commonwealth Bank may not be the largest companies by global measures, they’re still great examples of how high performance analytics and big data can drive groundbreaking results.

Understanding why means stepping back to consider the fundamentals - to me, big data is both an absolute and relative definition. The absolute definition is the one people usually focus on and, more often than not, is used by detractors as a reason not to pay attention. That may seem counter-intuitive - after all, if it’s real and measurable, why ignore it?

Put it this way - how often have you heard someone say something along these lines shortly before they dismiss it?

  • “Our warehouse has only gigabytes of data, we just don’t need to worry about it at the moment.”
  • “We’re only starting out - we just need to focus on the basics for now.”
  • “Our warehouse is scaling just fine, we’ve got big data under control.”

Take advantage of what you have, not just what you want

It’s true that one aspect of big data is how best to manage and capture the increasing amount of information that we’re generating. Volume, velocity, and variety are important, but they’re not the whole picture. We’re in this game for value: without achieving an outcome, we’ve wasted time and money.

Most of the conversation about big data is about the mechanics of capturing new information rather than the outcomes from using the information we already have. It’s true that not every organisation has the transactional volumes of CitiGroup, the retail spend volumes of Catalina Marketing, or the market basket data of Walmart.

However, most organisations are sitting on data that they just aren’t analysing for fear of opening Pandora’s box - a typical telecommunications company with tens of millions of customers can easily be working with billions of call detail records, a positive goldmine for identifying networks and relationships to help drive targeted marketing and retention.

Most ignore this data simply because they don’t believe they can practically analyse it in any meaningful way. They may not think they’re missing out on the value of high performance analytics, but they are. It’s the same for any reasonably-sized retailer, bank, or insurer - the opportunity is there, it’s just a case of taking advantage of it.

Big Data: The case of ignoring the obvious

Let’s go back to our detractors - their statements highlight some fundamental misunderstandings, namely:

  • It’s about value, not data. Capturing information is critical, but it’s only the input. You need to do something with it to actually create value, otherwise you’ve just added cost. And, you typically need high performance analytics to actually do something with all that data.
  • It’s about continuous value creation, not a point in time. The variety aspect of big data ensures that there’s almost always a new way of delivering value or a new way that existing information can be leveraged to solve new problems. Big data isn’t a problem to be a solved, it’s an intermediary step to becoming a smarter organisation.
  • It’s about what you can do, not where you are now. There isn’t a maturity curve with big data - it’s just another information source. With the right tools, mindset, and approach, it’s no different to any other form of business analytics. Waiting to capitalise on big data means suffering significant opportunity cost and competitive disadvantage.

People are already doing this. Today. Yesterday, even; this isn’t something to be aware of as an obstacle threatening somewhere down the track. It’s here and it’s been here for years. The starting point may be different depending on the company, but one thing is common - high performance analytics drives real value.

Back in 2010 Telstra was acknowledged at SAS Global Forum with an Enterprise Excellence Award for their use of analytics. This would have been impossible without their ability to scale their processes to deal with ever-increasing amounts of information. The productivity boost was staggering in some situations - they managed to drop processing times down from 11 hours to approximately 10 seconds. They support multiple areas of the business including the contact centre, the retail network, and many other groups. And, by doing this, they saw real improvements - a great example was a 15% lift in their customer retention activities.

On the other hand, the Commonwealth Bank of Australia decided to focus the charge on managing fraud. Most companies align fraud management to various lines of business - credit, mortgages, and so on. The Commonwealth Bank decided not to follow this approach and instead establish a single platform to handle bank-wide fraud management. This would have been impossible without the use of high-performance analytics; the volumes and process challenges were too great. While this chutzpah alone was impressive, the really amazing thing was how well this approach worked - they not only doubled their detection rates in managing cheque fraud but improved their detection in Internet fraud by 60%!

Dare to be different and don’t take things for granted

This all brings us back to the subjective aspects. Big data and high performance analytics can also be seen as relative concepts - it’s about encouraging the attitude of taking advantage of the things you’re not leveraging. It’s rare that an organisation really exhausts the value of all its information assets; more often, it falls back time and time again on the information sources it feels most comfortable with.

A critical aspect of high performance analytics is re-examining those fundamental assumptions in the light of new technical capabilities and asking:

  • How could high-speed visualisation change the way I view current and future performance, driven by in-memory processing?
  • How could I improve productivity by orders of magnitude, driven by in-database analytics?
  • How could I improve operational outcomes by augmenting them with dynamic and powerful predictive insight, driven by real-time analytics?

High performance analytics is a great wake-up call. Too often, we get stuck in doing business as usual, achieving the same outcomes by doing the same thing over and over again. Business analytics is a discipline of change and there’s no greater potential for change than a disruptive technology that allows you to do what was previously impossible.

Take the time to re-examine what you’re doing with fresh eyes. I guarantee you’ll be surprised at what you could be doing.

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Being DICEE and the golden rule of presentations - Guy Kawasaki talks to us

Marnie Macdonald
Marnie Macdonald|April 25, 2012

Today was a career highlight for me - while at the SAS Global Forum Executive Conference in Orlando, not only did I get to hear Guy Kawasaki talk about the Art of Enchantment, I got to meet him afterwards and record a short segment from him.

Guy (I can call him that now that we've talked the talk) has an engaging presentation style with ten steps to achieving this enchantment. You can read about them in detail in this post - Guy Kawaski on enchantment for achieving influence by John Balla. I also wanted to share Guy's golden rule of presentations:

10 slides
20 minutes
30 point font

What valuable information - next time you are preparing a presentation, think about this and more importantly, follow the rules.

My personal favourite point from Guy's talk was the DICEE acronym which encapsulates the third pillar, quality - have something good be it a product or a service. This resonated with me because when I think about ways to tell the SAS story, what we do and how we work with our customers to achieve success, I can see the parallels that Guy draws to illustrate his point. DICEE stands for:

D - Deep
I - Intelligent
C - Complete
E - Empowering
E - Elegant

Guy talks more about this in our interview - enjoy!

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Better analytics equals better decisions equals better business

Greg Wood
Greg Wood|April 19, 2012

Better analytics equals better decisions equals better business image source http://wendy-hewlett.com/wp-content/uploads/2010/08/chimpanzee_thinking_poster.jpgIt is often spoken about, marketed, presented and written, that analytics helps with making better decisions, more accurate and timely decisions and almost every other combination of 'better, faster, stronger' words. I set to thinking about this a little more, and went back to the basics of how individuals and groups make decisions.

If you have ever had the pleasure, or displeasure, of  group dynamics or organisational behaviour, you may have heard of the rational choice model of decision making.

I also noted from Vince's post a discussion about the merits of high performance analytics in the so called "big data era" that high performance analytics can be for everyone, you just need to know how to fit these technology advances into a process and see why they add value.

The underlying concept is that individuals, societies and organisations are trying to maximize their utility while minimizing the effort required, or more simply put maximum outcome for minimal costs.

The model assumes there are six steps in the decision-making process.

  1. Define the problem.
  2. Identify decision criteria.
  3. Weight the criteria.
  4. Generate alternatives.
  5. Rate each alternative on each criterion.
  6. Compute the optimal decision.

It's nice to have a great theory, but we all know the problem with a great theory is that reality is different. So the real crux of this post is to get to the issues in decision making and how high performance analytics can help.

When it comes to improved decision making and our current state, most people and organisations are satisfied to have an acceptable or reasonable solution rather than an optimal one. Most people, when faced with a complex problem, will reduce it to a level which can be readily understood. This is often due to the limited information processing capability that we have to assimilate and understand all the information needed to optimize.

So this is where something like high performance analytics can help. Not only have people long had limited information processing capabilities, so have machines. Now with these game changing technology advances in software and hardware platforms, we can now manage all of the data, all of the time, assimilate it and process it to an optimal outcome in seconds or minutes rather than hours and days.

Now it is possible to optimize. In fact you could ask the question, “What's your excuse for not taking all information into account?”

One thing that is not considered is the time value of money, well at least by marketers. The concept that “a dollar today is worth more than a dollar a year from now” so therefore the financial benefit of making a faster decision that impacts the financial position of your organisation is critical.

The best thing about all of this is that the use of analytics is being continually proven to add to the bottom line of business.

Our research has found a shift from using intuition toward using data and analytics in making decisions. This change has been accompanied by measurable improvement in productivity and other performance measures. Specifically, a one-standard-deviation increase toward data and analytics was correlated with about a 5 to 6 percent improvement in productivity and a slightly larger increase in profitability in those same firms. The implication for companies is that by changing the way they make decisions, they’re likely to be able to outperform competitors.

Professor Eric Brynjolfsson, Schussel Family Professor of Management Science at the Massachusetts Institute of Technology’s Sloan School of Management, Director of the MIT Center for Digital Business, and one of the world’s leading researchers on how IT affects productivity.

Analytics and optimal decision making go hand in hand. It's time to move away from poor decision making habits, from past experience, what we know in our sphere of knowledge, to avoid the apparent inconsistency of not sticking with a previous course of action and High Performance Analytics is the answer.

The ability to improve decisions leads to innovation – join us next week for a discussion about that very topic.
So what is stopping you from making decisions based on all of the data, all of the time, at the speed of right?

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David v Goliath: high performance analytics levels the big data playing field

Vincent Cotte
Vincent Cotte|April 17, 2012

How big does big data need to be before it is valuable?

High Performance Analytics levels the Big Data playing field

High Performance Analytics levels the Big Data playing field

The value in big data is within reach of everyone.  It could mean wanting to mine a couple of extra fields about the customer or wanting to improve the customer profile using unstructured data about customer interactions using Hadoop. Most articles and hype about big data surround the three Vs;  Velocity, Variety and Volume.  However, we should never lose sight that big data is relative to your business plan. The real conversation to be had is about the value in being nimble. 

The 4th (V)alue: The intersection of big data and high performance analytics
High performance analytics is the next generation of analytical focus as we work our way through the era of big data looking for optimal ways to gain insight in shorter reporting windows. It is all about getting to the relevant data quicker and delivering that information in real time. High performance analytics is equipping David-sized organisations with the tools to level the playing field.  Examples include:

  • How a bank determines credit risk assessment in seconds instead of hours.
  • Where a government agency improves social welfare by analysing unstructured citizen interaction data.
  • An insurance company that uses census data to improve marketing response rates.
  • How an online business analyses social data to understand sentiment, and behavioral data to improve campaign targeting.

Regional healthcare provider and an insurers point of view

I recently listened to an Australian healthcare customer discuss their version of big data and high performance analytics. It went like this.

Through some acquisitions we have increased our data base size by approximately 15 percent.  This has resulted in our marketing teams being frustrated with longer than usual time-to-market for gaining customer intelligence and executing campaigns.  Further compounding the issue is the competitive pressure coming from recent changes in government legislation, which is driving customers to shop around.  This increase in competition means marketing needs to be more nimble.  Meaning more campaigns to fewer people with more relevance.

Another example is a local Insurance customer I met with to discuss their version of big data, high performance analytics and real-time analytics.

We have issued our sales force with iPads.  The challenge we face is, how do we deliver intelligence to our sales representatives in a manner where we know it is relevant, timely and contextual?  We know they are meeting with prospects and customers but how do we analyse customer data, analytical data, transactional data and interaction data to provide a Next Best Offer in seconds?

If we understand how to beat Goliath, do we know what to beat him with?  A high performance approach leads us to think about the problem differently and look for a solution that optimises the analytical jobs and the way they were architecturally executed.  I expect there is a target value proposition heading my way, now.

Under the hood: high performance analytics is not that scary

We often think of new technology as being like a Ferrari, always thinking it is out of reach or too complex for the average David.  The reality is high performance analytics provides various approaches that span the spectrum of your maturity and size, from:

  • Moving existing analytical models into operational processes for real-time decisions.
  • Optmising analytical jobs to leverage your existing in-database power.
  • Using in-memory analytics to take advantage of cheaper hardware.
  • Building an enterprise analytical platform to drive down TCO while always prioritising business value using a grid based approach.
  • Visually exploring big data using high-performance, interactive, in-memory capabilities to understand all your data, discover new patterns and publish reports to the web and mobile devices.

The democratisation of analytics, especially high performance analytics has allowed every company whether Goliath or David-sized to benefit from big data.  Over the next few weeks we will be discussing the impact of the intersection with big data and high performance analytics.  In particular providing examples relevant to the world we live in left of the date line. Join the discussion to find out what the innovators are doing and lessons we can learn locally.  You can see some more examples here.


Question: What is your big data opportunity? Tell us in the comments below.

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Business Analytics is about change. Do something.

Evan Stubbs
Evan Stubbs|April 5, 2012

Time's finite, right? In the immortal words of Prof. Bueller, "life moves pretty fast" - who wouldn't kill for another six hours in the day? Most of my time is spent trying to work out how I'm going to do everything I wish I could do ...

If there's one thing I try not to do, it's waste time. This piece at Make really hit home - earlier this year the White House hosted its second science fair, featuring research and inventions from over 100 students. Out of all these amazingly smart and passionate kids, there's a 14 year old named Joey Hudy who got a lot of attention - he not only built a marshmallow cannon but he got to fire it with President Obama.

That's pretty cool by itself. I built a potato gun back in the day, but I didn't get to fire it with the head of state! What's even cooler is that even though he's just 14, he came to the White House with pre-printed business cards! But, here's the poignant bit: at the bottom of every card he printed his personal motto: "Don't be bored, do something".

Don't be bored, do something.

Don't be bored, do something.

Joey's a smart guy. Really smart. The biggest challenge a business analytics team usually faces isn't technology related. It isn't trying to work out what to do. It's overcoming apathy.

Change is hard, and it's easy to forget that despite all the fancy language and technomancy, business analytics is about change. It's about doing things better. It's about harnessing mathematics and creativity to deliver real outcomes. And, none of that comes easy - inevitably, it involves convincing people that things should be different.

We all have a role to play in driving that change. Whether we're the Chief Marketing Officer trying to put the customer front and centre or whether we're a junior analyst, trying to build a better model, every single one of us influences culture. Unfortunately, all too often we forget and assume that it's the sole responsibility of the senior executive to wave their magic wand and change the company.

Being apathetic actively hurts us; it diminishes the perception that business analytics is not only valuable but, more importantly, value-creating. It pushes business analytics into the bowels of the business, sidelining competitive advantages and generally making it harder to succeed.

Doing something doesn't have to be hard. It just needs taking that first step. Some easy steps to take are:

  • Focus on value creation. Business analytics creates real value but unless that value is understood, it's impossible to convince everyone else. Understand how the work you do changes outcomes and make sure people understand.
  • Talk to the people who use your insights. Don't sit back and assume that just because your job's done that it added value. Talk to the people who got your insights and find out whether they found them useful. And, if not, find out why not.
  • Run it like a business. If it were your money, you'd want to make sure it was being used effectively. Look for ways that insight can actually affect the business and don't just find interesting stuff for the sake of it - work out how those insights can change the business and then work with the right people to make it happen.

What do you find works in overcoming apathy?

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What do your online conversations really say? Text analytics tells the story

Natalie Mendes
Natalie Mendes|April 3, 2012

What do your online conversations really say? Let text analytics tell the storyLet us start with a brief text analytics history lesson.  Australia’s first postal services began with the early settlers in 1809 - communication was hard, and they would wait for mail for months on end.  Moving forward in time (approximately four decades), recognising the communication needs of people became the focus.

This is when the post office took control of what was the most modern means of communicating - the telegraph.  There seem to be no public records available on the number of messages created and delivered at the time, however I am sure I can count on one hand the telegraphs delivered in a day in the 1850s. I am also guessing that the content of the communication expressed sentiment,  historical events, current happenings and future wishes in approximately eight hand-written pages or a thousand plus words.  The level of detail allowed the reader to interpret the meaning and context.

Fast forward 200 years to 2012!  BOOM!
My neighbour, George, is also a postmaster in some ways - in that he delivers communications from  his home. For George is a 'serial tweeter' and he is not alone.  In March 2012, Twitter announced that it had 140 million active users, sending 340 million tweets per day. That is a lot of ‘letters’ – 140 characters at a time – being sent worldwide to anyone and everyone every second of the day. The ‘Noughties’ version of the pen pal. There is even a new language that has its roots in Tweets and text messages: 'Tweetish' … LOL, OMG, think I cre8ted a nu word – SMS speak is now so pervasive (used in chat, on Twitter, in SMS messages) that we even have the SMS Dictionary.

If a picture tells a thousand words, do a thousand words give us a picture?
With the millions of words communicated in text conversation today, we can analyse these words and phrases to provide a good understanding of the hot topics of discussion, as well as society’s sentiment, from all around the world.

Analysis of social media using SAS shows increases in chatter about certain topics that are leading and lagging indicators of a spike in unemployment.

Analysis of social media using SAS shows increases in chatter about certain topics that are leading and lagging indicators of a spike in unemployment.

In a unique project recently, SAS teamed up with the United Nations Global Pulse and partnered on a research project entitled ‘Unemployment through the Lens of Social Media’.

This project investigates how social media and online user-generated content can be used to enrich the understanding of the changing job conditions in the US and Ireland by analyzing the moods and topics present in unemployment-related conversations from the open social web and relating them to official unemployment statistics.

It is fascinating research and I recommend you take a look – we have had a lot of interest across Asia in this project. People today are talking much more than they ever did and to everyone in the world about everything in the world.  The next steps are to make sense of the data and turn it into information.

Why is this so important?  Marketing, fraud specialists, risk advisors, journalists, and advertising agencies could all use text analytics to gain competitive advantage and understand the consumer voice.  If my health insurance company analysed my last conversation I had with them a week ago, they would be worried.  My last words to them were “It’s taking you three days to issue me a new policy quote.  I am not happy with your pricing on the policy package, so I will look into other insurers.  Goodbye!”

Question: Think about the online conversations you have had recently. What would sentiment analysis reveal?

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  • About this blog

    In many ways, Australia and New Zealand are unique - we live a day in the future, our birds walk and our mammals lay eggs. However, one thing we have in common with the rest of the world is the need to be globally competitive while staying local. On this blog, Evan Stubbs and his colleagues provide a uniquely ANZ perspective on doing business, using analytics to be more effective, and life in the antipodes in general.

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