9月 282015

decisions-first-tom-stockI moved to Australia from Belgium two months ago for a short-term assignment. I am very concerned by the exchange rate. My dollars have lost over 15% of their value in euros and I share my frustration around me. People tell me, "Just wait, it cannot stay so low, the rate will go up again". So I keep waiting.

Actually, this intuition is against all economic principles and historic observation. If there was information on the market indicating the value of the dollar is underestimated and will go up again, it would be immediately incorporated in its price. Making the intuitive assumption that the dollar will come back to its historical value is like buying a lottery ticket. You are only buying a dream. It has the exact same likelihood to go up or to go down again.

Yet, I am taking the decision to wait. I don’t need the money now, and I buy the dream that the value of the dollar will rise again, with an eye on the next iPhone I could buy with it.

Many organizations have to take the exact same decision as me.

If you were an executive in a company with Australian dollars in the bank, shareholders in Europe and no local investments in sight, would you wait? Would you consider it a rational decision? Can you afford the same subjectivity as me? Can you buy the dream the value will rise again?

Doing business is taking decisions and companies cannot make subjective decisions. All business decisions must be treated with a maximum level of objectivity incorporating all available information. Whether these decisions are strategic, tactical or operational is irrelevant to this principle. So, organizations cannot buy the dream the dollars will rise again.

And it is a challenge for many organizations to get rid of that subjective bias. They must engineer their processes in order to take optimal decisions, requiring the right information at the right time. This information consists of analytical insights, patterns, sentiments and anomalies hidden somewhere in data. It will require to read, store, crunch and process tons of records, texts, trades, news and databases at extreme speed to meet the decision timeframe.

This process must start by defining the decisions that the company want to objectivize. The price of an airline ticket, the purchase of additional ships for oil prospection or the insurance premium of a policy holder.

These decisions define the insights needed for improvements. It may be simply surfacing the most recent update about a customer. It may require some probabilistic computations of oil prices or sentiment analysis based on previous phone conversations.

And only those expected insights will ensure the organization put up the right data requirements. What data is needed to provide these insights? Comment fields, sonar echoes, phone calls, pictures? Is the data to be stored or only processed? Where and how does it have to be stored? Etc.

Organizations must aim at decreasing the subjectivity of decisions and this process must drive the requirements for technologies. As such, decisions will define the analytical strategy, and analytics will define the data strategy.

tags: analytics, decision support, operationalisation, sas, strategy

Decisions first! was published on Left of the Date Line.

9月 192013

Optimise your high performance team using analyticsI recently spoke to one of our customers about how they could better leverage the knowledge, skills and capabilities of each of their many and highly-skilled employees, and in return be able to execute projects faster and achieve better results. Our customer's business is complex, and their organizational structure so fluid that no manager can keep up with the skills and capabilities of each employee. As a result there can be value in optimizing the way employees are deployed and assigned to projects.

As with any large organization, the one in question often sets in motion new cross-organizational initiatives to support one or several strategic objectives. For such initiatives to be successful it is crucial to put together a high performance team that posses the required experience and capabilities.

How can analytics help?

What if we consolidated the relevant skills, preferences and behaviours of all employees in an enterprise-wide human capital database and used analytics to determine how to utilize them in the most efficient way?

Who says that, for example, the head of corporate business development is the best person to decide which ten employees are the right ones to head a project focused on introducing the company's products to a new market such as Indonesia?

The future of setting the right team

Imagine that you are the manager in charge of the above strategic initiative. Your business plan has been approved and you have been given a budget and time frame for when the new office is supposed to be set up. All you need is the right team.

You request the ten best matching employees from the company's human capital database, which contains information about each of the organization’s 25,000 employees.

You enter the project's start and end date, Indonesia as the main location where work is to be carried out, total budget, and select the required skills, such as understanding of Indonesian culture etc.

The system returns the ten best matching employees. You notice how each employee has been given a score between 0 and 100% depending on how well they meet the requirements.

When looking closer at the employees' profiles you learn that several of them have worked together in the past, two of them were born in Indonesia and speak the local language, they all live close to an airport with direct flights to Jakarta, and three of them have experience with launching products in a new market.

After going through each employees profile you feel satisfied that you have identified a good team but there is a small problem. One of the employee's score is only 70% fit for the job due to the fact that he cannot participate from day one. It seems he is locked up in another project during the first two weeks.

You click "find similar profile" and the system returns a match that is free during the entire project period. The bad news is that she is only 73% fit for the job as she lacks the necessary understanding of Asian culture; however, according to the system she will be 81% fit if she participates in a short cultural training course. You check the new candidate's upcoming schedule and note that she is free to take the course prior to heading to Jakarta with the rest of the team.

With an average matching score of 90.4% you are confident that you are sending the right team to Indonesia.

Analytics adds increasing value to many areas of the organization, and human capital optimisation is just one of them. This scenario gives you a taste of where an organisation can optimise to achieve better business outcomes. How can analytics help you build your business?

Philip Reschke is the Head of Global Business Advisory, Asia Pacific, at SAS and author of Stock Market Edges. Follow Philip on LinkedIn, Twitter and Google+.

tags: analytics, business analytics, business performance, decision support, employees, human capital, measurement, operationalisation, optimisation
5月 142013

I was privileged enough to have the opportunity to celebrate the coming of age of analytics with 4,200 SAS users in San Francisco last week.  It's clear that analytics is front and centre of strategic conversations in agencies and companies alike.  Check out the newspaper headlines, the explosion of events surrounding deriving big data value and of course we now have a Hollywood movie.  In the opening keynote, Oakland A’s General Manager, Billy Beane (played by Brad Pitt in the movie Moneyball)  made a couple of great observations about the emergence of analytics. Here are three that resonated with what I am seeing with local companies in Asia Pacific:

  • Organisations can no longer ignore the fact that numbers don’t lie.  Analytics is helping to identify trends and patterns to guide and implement more accurate and impactful decisions.
  • Leaders of the future will be numbers driven, be it sports, government or business.  Data is a key asset that needs to mined for value.
  • Once analytics is embraced to enhance one part of the business the competition will quickly catch up.  You must look to innovate and explore how analytics is used across the breadth of your business.

Ensuring our customers are at the forefront of analytical innovation SAS released a series of groundbreaking announcements.  SAS’ Chief Executive Officer, Jim Goodnight and Chief Marketing Officer, Jim Davis took to main stage, assisted by customers JP Morgan Chase and OfficeMax, to demonstrate some of our newer offerings followed by discussion of vision. There were three themes driving our vision; analytics for everyone, everywhere and in everything.  I have broken down the themes into the key capabilities they bring to market.

Analytics for everyone

  • SAS Visual Analytics next release will include  powerful new scenario analysis capabilities, adding prescriptive analytics to existing descriptive and predictive analytics, helping organisations rapidly and easily move to more advanced uses of analytics. SAS Visual Analytics will also support Windows 64-bit.
  • At no additional charge for the high-performance procedures, customers currently licensing products like SAS/STAT®, SAS Analytics Pro and SAS® Enterprise Miner™ will receive this new functionality when they upgrade their respective analytic products on the newly released platform for SAS Business Analytics 9.4.  Making it easier for existing customers to access the power of high-performance analytics while leveraging current hardware and software investments

Analytics everywhere

  • The June shipment of SAS 9.4 software is cloud ready allowing your IT team to meet requirements around security, authentication, scale and resiliency for private or public SAS cloud deployments.
  • Availability of SAS High Performance analytics procedures in your current environment allowing you to take advantage of the capabilities of High Performance Analytics everywhere.
  • SAS Web Editor, a Web-based tool for writing and running SAS code will require no local SAS software installation. Users simply connect to a website to access SAS code, files, projects and libraries, anytime and anywhere. SAS Web Editor supports multiple browsers including Safari, letting Mac users take full advantage of powerful SAS programming software capabilities. SAS Web Editor is available on iPads® that support iOS 6 or greater. Support for other tablets will follow.
  • SAS Visual Analytics continues to enhance mobile business intelligence with increased collaboration with annotation markups and sharing, integration with mobile device management. Having on-the-go access to current, relevant information means faster decision cycles as critical information is always available anytime, anywhere – on iPads or Android tablets.

Analytics in everything

  • The newly redesigned SAS® Customer Intelligence suite makes advanced analytics more accessible to all marketers to address today's challenges surrounding the effective management of customer relationships across channels, creation of meaningful customer experiences and making optimal decisions based on big data. Inside one application, marketers can plan, create and execute campaigns; optimise scenarios; and engage with customers across all channels. No longer will marketers have to manage work in disparate applications and interfaces.
  • SAS is increasing the value analytics delivers by extending focus from “data at rest” to “data in motion”. Fast-moving organisations are embracing real-time data streaming into their enterprises by performing relational, procedural and pattern-matching analysis of structured and unstructured data as it is received to provide immediate action and insight.  Event Stream Processing and Enterprise Decision Management filters out the noise analysing high-volumes of data and events in motion to drastically cut time to action.
  • SAS 9.4 provides access to numerous new features in our data management products, as well as enhanced Hadoop integration. Our goal is to decrease the time to value for SAS users while reducing the total cost of ownership for IT.

Subscribe to our blog to read Bill Gibson’s in depth breakdown of the key announcements from Global Forum 2013.

If you missed the action you can attend your local forums in the following countries.  Click here to check them out.

tags: analytics, cloud, customer intelligence, decision support, high performance analytics, Mobile BI, operationalisation
7月 272012
Customer-centric marekting: why is testing so 'testing'?

What is the cost of not testing your marketing campaigns?

I was first exposed to experimental design in marketing over 15 years ago. It made sense then and it makes sense now. Sadly and somewhat surprisingly, it seems that few organisations have embraced this approach since then. Driving better marketing return on investment is a key part of effective marketing. It’s hard to succeed without being good at optimising offers, channels, price, targeting, timing and so on. And, with budgets shrinking, it’s more than a nice to have - it’s a necessity.

Some things we know ahead of time. Others we don’t. For these, in-market testing is essential. Marketers are good at many things. They’re often experts at creating offers and, given the freedom to innovate, they’ll end up with several competing alternatives for marketing the same product or service. Given this degree of choice, what next? Instincts and experience may help trim the list. This can only go so far though; even after ruthless cuts, the final handful may all look attractive. Settling on the final offer structure needs more than a best guess - ideally, it should be a fact-based decision. Who wants to take a chance on a potentially costly "roll-out" without quantifying the risk first?

The only way to get that evidence is through in-market testing. This takes planning. The tests need to happen quickly and not to be too costly, suggesting the need for small trials. “Small” is a relative term though - in balancing cost with accuracy, how big should these samples be? Get it wrong and the results might be unreliable or even totally misleading! The whole point of experimental design is to solve this problem. Given how common and obvious the problem is, it’s astounding that the marketing community has been so slow in applying these techniques. It’s not like this isn’t a real problem, either. I’ll often ask, “why are you continuing with this campaign, when it obviously isn’t delivering returns?”. I usually get a blank look followed by answers such as, "Oh, don’t worry; we’ve always run that campaign since I can remember”.

Comments like this show how big the opportunity is. We can all do things better, and for many groups, getting better at 'learning' and taking action, the results can drive real improvements.

Effective testing achieves two things. It helps marketers make informed decisions. Informed decisions lead to better decisions. The key is good testing takes rigour. My feel is that there’s a great deal of talk around 'test and learn' and 'experimental design' at the moment. Ironically, there seems to be little serious effort to support this. What makes things worse is that even where testing is done; it’s usually not done anywhere near rigorously enough. Sometimes, it’s a lack of focus. Other times it’s because of too few skilled practitioners in the marketing environment. Usually though, it’s a case of blinkered vision. Most groups have had the concept of mass-marketing hammered into their culture over the last decade. Given this culture, it’s almost counter-intuitive that adopting smaller more focused tests or campaigns can actually drive better results and more informed decision making.

The thing is, not testing offers in a controlled way can actually reduce effectiveness. Make the wrong offer and you won’t make a sale. You might even lose a customer. When debating the value of doing in-market tests, it’s important to also consider the cost of not doing the test.

Many groups seem more focused on pushing creative rather than relating to their customers. This may help win awards but it does little to drive better engagement. Good testing has an important role in driving offer relevancy. If a group is seriously interested in improving their effectiveness, they need to give testing methodologies an acknowledged place in their marketing plan, and make sure it happens by allocating budget and resource to testing.

To see where you sit, ask yourself the following questions:

  • When faced with campaign choices, how do you know whether they will 'fly' with your customers or not?
  • How do measure your 'customer-centricity'?
  • How do you validate your beliefs or judgements about what will or won’t work?
  • How do you determine the cost-effectiveness of a campaign prior to launch?
  • How much risk are you carrying by going straight from ideation to execution?

Those that ignore testing should consider this: testing is the means by which an intelligent organisation acquires knowledge about customer behaviour. And this, after all, is at the heart of a customer-centric approach to marketing.

Relevancy requires timeliness and personalisation. Simple mass-marketing doesn’t work in a world defined by real-time engagement and “markets of one”. The best of the best have the ability to monitor results in continuous time, to make confident decisions on how to drive real results, and to squeeze every last drop of value out of every tweet they send.

Question: Where do you sit with respect to adopting a rigorous testing approach to support decision making?

tags: customer intelligence, measurement, operationalisation, optimisation, success
7月 232012

Bringing analytical insight to the point of decision
Organisations are moving their focus from being product-centric to customer-centric. I recently spoke to the CIO of a leading Asian insurance company who discussed their CEO’s new mantra … the desire to focus on doing business at the speed of life.

He went on to elaborate:

“Consumers today demand instant satisfaction; regardless of the occasion, whether it’s online purchases, money transfers, package tracking, social communication, and even over-the-counter purchases. Organisations have it tough trying to convey a relevant message in a timely fashion, especially considering the move towards acquiring knowledge using the web and social media, as well as the proliferation of marketing messages everywhere you look, flip and click. Consumers now expect a personalised, relevant and contextual customer experience. Organisations face a decreasing window of time to detect threats and take advantage of opportunity. The challenge facing us is how to combine ten years of ERP data, 20 years of customer history and combining that with the current customer interaction data. Add to this that some of that data is unstructured, and it quickly becomes apparent that our information technology environment and our enterprise data warehouse simply was not designed for this real-time contextual focus.”

This reality is where many organisations find themselves today. Industry leaders see big data as an opportunity and high performance analytics (HPA) as the enabler to gaining competitive advantage. The focus is how to bring analytical insight to the point of decision, whether that be the sales team, customer service representatives, ATMs, websites or other operational applications.

The recipe to great customer experience
Organisations that are successfully delivering great customer experience through real-time insights are consistently:

  • Accurate. Poor insight, sped up, means angry customers faster. Increasing accuracy of insight requires analysing existing structured data (source of truth) and unstructured data together. It means looking at all of the data not just sampling the data.
  • Event-driven. Doing business at the speed of life means we need to understand, predict and listen for the life stage triggers. Knowledge of when and why a customer is interacting with you is key to improving your cut through. The opportunity is not to make an offer, it’s to make the right offer in real-time. We have all received a credit card offer in the mail. Somewhere there was an event that was captured and the offer was relevant at that point in time. Three weeks later it’s spam.
  • Empowering the channel. Operationalisation and delivery of insight to the interaction points with your customers is vital to success. Some have labelled this the consumerisation or democratisation of analytics. It could be as simple as a pop-up window on your website, a dynamic script for your call centre representatives or a salesman looking at location based reports on a tablet. Placing insight in the hands of those who make decisions about your business every day is the real-time analytics pay off.

HSBC improving customer experience through analytical innovation
Traditionally credit card fraud is detected by sampling some transactions post the event and looking for fraudulent patterns. Then kicking off a process to investigate, notify and fix the fraud. HSBC moved to real-time fraud prevention, differentiating their credit card product by improving security, lowering fraud and improving the customer experience. Think about the customer experience if they get it wrong. The customer is paying for shopping or expensing a corporate dinner. The stakes can get no higher, a negative experience here would likely end in a customer churn. HSBC analyse hundreds of millions of credit card swipes every day ensuring their customers have a higher confidence of security, all delivered using all of the data all of the time without being obtrusive.

HPA delivers insight at the speed of life
HPA enables organisations to drive real-time competitive advantage in three key ways

  1. Ability to look at all the data – Improved accuracy requires the ability to aggregate structured data from your enterprise data warehouse and unstructured data from inside the firewall (email and voice) and outside sources (social). HPA embraces technologies like Hadoop to bring unified insight.
  2. Operationalisation of insight – Delivering insight faster is crucial for organisations to take advantage of that ever shrinking window of opportunity. Shortening the decision cycle requires the ability to expose the insight as decision services that can be embedded in applications like your CRM system, online store and website. HPA manages the end-to-end data-to-value process allowing the velocity to increase exponentially. This is achieved by capturing, transforming, and storing data, and performing advanced analytics over in-memory storage to provide pure speed while managing larger datasets.
  3. Mobility – It is paramount that the insights can be placed into the hands of those interacting with customers. As such the ability to support mobile delivery by web and native applications means the right insight gets to the right person at the right time and in the right location.

Real-time analytics is the enabler to a customer-centric strategy.
So where do you start? Look for areas where you can bring structure to a key decision making process, that positively impacts the customer. The challenge is how to do this profitably, and that’s where analytics comes in. Here are some examples to kick start your thinking:

  • Offering real-time personlised marketing offers to a segment of one at the point of sale. Include detail on location and type of transaction to bring more context and relevance to the offer.
  • Predictive maintenance of assets affecting customer experience. Analysing machine data looking for the beginning of historical fault patterns E.g. electricity grids, telecommunication towers, ATMs, production lines
  • Individual risk based pricing in insurance. Highly personalised policies can help differentiate you in the market while looking at greater profitability in the long term.
  • Disruption management in transport and logistics. The ability to understand the ripple effect of an error and re-coordinate the different assets and resources to achieve the best possible outcome for the customer and your bottom line. Oh and doing it in real–time.
tags: analytics, Asia, Australia, big data, business analytics, business intelligence, challenges, cio, customer intelligence, data integration, high performance analytics, New Zealand, operationalisation, scalability, Singapore
6月 282012

What a difference a day makes! SAS Forum Singapore 2012 was packed full of insights and some truly inspiring case studies. One of my favourites was the story of DBS Bank’s analytics journey and their challenges and successes as they strive to become the “Asian Bank of choice for the new Asia”. David Gledhill, Managing Director and Head of Group Technology at DBS talked about the early days of this transformation which revolved around the central “bright” idea that building a data warehouse would solve all their problems. In fact it did not, and this led to a complete rethink of the project, starting with aligning the IT strategy to the vision and embed a culture of analytics within DBS.

I want to focus on the enabler priorities David discussed, specifically analytical culture and customer centricity. These are often part of a company’s motherhood statements and left languishing in the corporate vision. Not at DBS, where the priority is to “place the customer at the heart of the banking experience”. The journey we were taken on with David showed they are more than paying lip service, this is an internal commitment that starts at the bottom AND the top, and in turn impacts outwards on the customer.

For DBS, the transformation project needed more than just CEO buy in. I found myself nodding as David described the typical scenario where a project leader thinks that if they secure CEO support: “This is so important it’s a CEO Program” and therefore success is a done deal. Not true. Developing an analytical culture is about changing that mindset and bringing the team on the journey – empowering them to innovate and impact the bottom line, in this case, the objective of becoming the Asian bank of choice. Identifying the right executive to give that support is key – it’s critical to connect the project to the right part of the business to ensure the drive is there, and the KPIs are behind it.

Aligning strategy with the customers was then broken down into three areas:

  1. Know your customer well – drove the agenda of customer and credit analytics
  2. Serve your customer well – operational analytics, process improvement – become an organisation that is run on actual intelligence
  3. Hear your customers well – big data and analytics – how much are we using to drive the organisation.

Breaking the project down into manageable steps, DBS established a Business Intelligence Competency Centre (BICC) is at the core of the project , owning, driving and evolving DBS’ Business Analytics Master Plan. Here’s a great example of where DBS partnered with some of SAS’s brightest developers to implement an innovative analytics project.

David told us that with some of the busiest ATMs “on the planet”, DBS was receiving complaints about ATMs being out of action because they were being refilled at the most inconvenient time for customers. Analysing the data meant the refill schedule has now been optimised for time of day. As David put it “when you see our ATMs being refilled it’s happening then because that’s the best time for our customers”.

We also talked to Jurgen Meerschaege, VP Business Analytics & Decision Support at DBS who told us more about the evolution of analytics at DBS Bank and the catalyst for change that is driving internal change.

This is an operational example where DBS listened to the customers and used this knowledge to optimise the customer experience.

Question: What are your customers telling you? 


tags: Asia, customer experience, customer intelligence, operationalisation, optimisation, Singapore
6月 222012

Our job as marketers is to make it easy for the customer to buy from us.

Why is that hard?  “Where do I start?” I can hear you ask.

We’ve got multiple channels that don’t talk to each other, so many products that it is hard to explain to our sales staff, let alone the customer, my contact rules are restrictive… and how do they take into account inbound and online again?  And then there’s big data!!!

Start with the customer’s journey.  Are you actually measuring it?  Do you know when the customer starts their buying journey with you?  It’s a lot harder to measure than a campaign response rate, but if you do, you know who is interested in what product.  This is especially true if the customer displayed interest online via the web or on a mobile device.

Make it relevant

Is this possible?  Absolutely.  Technology allows the automation and embedding of analytic outputs into your business processes.  A great example of this is the 'people like you also bought' concept made famous by Amazon. You are now able to track so much of what’s happening on your website … structured data, unstructured and social data, big data and the list goes on.  Start by prioritising the business problems you want to solve and then go after them one at a time.  This will tell you what data, analysis and business processes you need to focus on.

A mistake to avoid?  Don’t just focus on the customer experience.  It needs to be mapped to the sales funnel.  There is no shared value if the customer doesn’t leave with what they came for.

Where does relevance kick in?  The customer expects us to use their data responsibly.  If we just try to sell them a product without understanding their needs, they will vote with their feet.  Know when and how to use that data.  For example, if a customer has just burned their mobile phone cap and they have a high future value, now is the time for the contract upgrade next best action, not a fine for exceeding the current contract cap. Be relevant and take them on the journey to becoming more valuable for your organisation as well.  Show the customer that you are on their side.

Make it personal. 

At the recent Premier Business Leadership Series in Amsterdam, Karen Ganschow, Head of CRM and Online at Westpac spoke of the bank’s project KnowMe.  “It's about understanding a customer so that communications are relevant and timely" she says. "We want to bring back that personal contact; we want customers to feel we know them -- things like whether they're buying a house or planning for retirement.”  Karen goes on to discuss the outcomes when the engagement is done well.  In short "customers say, 'yes, that's something I needed, yes, you've made it easy for me', and they'll reward you with their business.".

Make it easy

For this to happen the marketing process must align itself to the customer buying process. An understanding of behavior, key events and context help to personalise each interaction.  The customer experience is improved because the organisation has had a relevant dialogue and done so on the customers terms.  This makes the buying process easier and the outcome is a happier customer and new profitable business.

The conclusion:  Make it relevant, personal and easy.  If you don’t, someone else will.

How have you made your marketing relevant lately? We'd love to hear some examples.

tags: Asia, Australia, big data, business analytics, business intelligence, CRM, customer experience, customer intelligence, measurement, New Zealand, operationalisation, skills, value
5月 162012
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.


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?


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.


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?


tags: analytics, Asia, Australia, big data, cio, high performance analytics, operationalisation, optimisation, value