I was recently at SAS Global Forum in Orlando FL, listening to Thornton May. He mentioned something that really hit the mark when it came to decision making. He mentioned something called an OODA Loop. I had never heard this term before, so off I went and did some research. The OODA loop is a concept that was developed by Colonel John Boyd of the US Air Force and is really a cycle of events that surround the decision making process in military dog fighting

O - Observe

Decisions are made on observations. Of course today observations are many and varied, they come from internal and external sources and need to be managed consistently and constantly. Boyd suggests the observation includes unfolding circumstances, outside information and the interaction with the environment - sounds the same in business data. Big data plays heavily into this part, with plenty written on this, so let me leave this challenge. Can YOU manage the data you need to in order to OBSERVE?

O - Orient

When you read what has been said about Orientation you see that it's the part of the decision process where organisations apply their lense to what is observed. What does your corporate lense look like? Is it clean? Scratched or damaged? Is the culture of the organisation or defined norms bending the truth on what you observe. Are you ready to let the data you have available to you orient you for success? It's time to let analytics shape your view and today you can with High Performance Analytics.

D - Decide

When you have to make any business decision, it could be as simple as asking "what’s the chance my customer will leave me". How would you know this? How do you measure the propensity to churn? I would advocate using analytics of a predictive nature to do this, otherwise you simply won't be deciding, you will be guessing and you will be looking backwards rather than forwards. The decision process needs to deliver an action (we will get to that in a moment). At every stage of the OODA Loop you will see that we are looking to feed back into the observation stage, we look to re-orient and then make additional decisions. Deciding more than once on anything is an iterative process. A process that is supported by analytics and governed by a robust information management and model management framework.

One simple piece of  advice I would offer is to decide to decide. I have seen so many people keep waiting for the right time, the perfect data or the perfect model. Let me assure you it never comes along, decide to start and start to decide before your rivals make you irrelevant.

A - Act

Now you have a decision, ACT on it. As Boyd says this will produce an unfolding interaction with the environment, take that and add it to your feedback loop, add it to your "observations" and cycle on through.

So what's holding your organisation back from better decision making?

It's a pressing one, too. Everyone's hiring at the moment, but no-one's entirely sure how to find the people they need. Twenty years ago, it was pretty simple. Got a degree in applied mathematics? Great. Knowledge of the right tools? Even better! Business experience? You're hired!

Things seem more complicated now, and I'm still trying to work out whether it's just a general progression towards specialisation or whether it's a fundamental shift in the way we work. I lean towards the second, but some of my colleagues disagree.

Before we go any further, let’s cut to the chase. As you read this blog keep these 3 keys in mind:

• Everything you do should be linked to a measurable and valuable outcome.
• Develop an understanding and awareness of the business.
• There's room for generalists and specialists.

What do people want?

Some things haven't changed. You still need technical skills. You still need a good understanding of how to manipulate data. And, you still need the ability to translate highly technical information into something that a layman can understand. The minimum benchmark still revolves around having:

• Information management skills (including data manipulation and imputation)
• Information visualisation skills (ideally based on Tufte's ideals or the like)
• Modelling and / or basic to advanced statistical analysis skills

Some things are new. There's ongoing pressure to justify the use of analytics; insight without outcomes is often worthless. This drives the need for change management skills, the ability to work closely with other business units, and financial modelling skills. Increasingly, analysts need to either understand or have experience in:

• One or more industry verticals
• Value measurement
• Change management
• Business case development

And, there are some things that are fundamentally different. The concepts of operational analytics and asset management redefine the role of analytics in decision support. They change analytics from being the focal point to being one small part of the picture (albeit still the one that drives competitive differentiation).

Those are pretty significant. However, it's more than that. The unique nature of big data requires specific skills across technology architecture, data management, and high performance analytics. Making a technical mistake "costs" more - badly structured models or process can take days or even weeks to run.

While still relatively early days for most organisations, the best teams also seem to have skills spread across:

• Managing and manipulating big data
• Operational analytics and integrated decisioning systems
• Real-time insight generation and execution

The really interesting question to ask is whether having these skills is all part of being a skilled professional or whether these are all different roles in their own right. Has the definition of an analyst changed, or has the composition of the team changed?

I'd argue both - business analytics teams need a much wider breadth of skills than they used to have. Equally though, I think that analysts need to be far more multi-skilled than they used to be. It's not enough to be a great modeller; to be successful, you also need to be able to change the business. Or be able to model and capture the value you create through your analytics. Or have a position on how best to design decision support systems. There's always need for ultra-specialists. The feedback I'm getting though is that their skills are becoming a smaller proportion of the types of skills demanded in the market. It's also interesting how consistent that is across industry sectors - it's the same message in retail, banking and finance, telecommunications, and the public sector. The only thing that's different is the scope of skills they're looking for: the more mature the organisation, the greater the focus on change management.

Meeting the market half-way

Adapting to this change doesn't need to be scary. It does, however, require a change in attitude - it's not about the insight, it's about the outcome. Regardless of where you focus is, it's where we all need to be. Big data is useless unless you have the ability to analyse it and put the framework in place to operationalise those insights. Knowing the reasons why customer churn is pointless if you don't try to stop them churning.

So just re-iterating where we started, if I could pick out three things to keep in mind, it'd be these:

• Remember that everything you do should be linked to a measurable and valuable outcome. If it isn't, what's the point?
• Develop an understanding and awareness of the business, not just the slice you're working on. Business analytics is about change, and it's impossible to change that which you don't understand.
• There's room for generalists and specialists. Just remember that even specialists need to be able to do more than one thing.

It's an interesting area and I'm always curious to know what skills people are looking for. What do you look for when hiring and how do you adapt to change?

Let's look at some examples of how analytics is helping enterprises in India become high performance organisations:

Power and utilities: India relies heavily on power and and the distribution of power is highly regulated. Regulators specify that the utilities are required to plan, estimate and report their daily demand to the load dispatch centres, which in turn schedule the available power to them for the next day's consumption. Any shortfall or excess in estimating demand incurs either penalties and losses due to wastage. Today, analytics for load forecasting is helping leading power and utilities companies forecast short-term and long-term power demand with accurate results, thereby minimising losses and penalties.

Stock exchanges and regulatory bodies: The magnitude of daily transactions on exchanges brings with it surveillance challenges. On one hand there is the challenge of analysing the behaviour of investors and scrips over a period of time looking to uncover fraud, including insider trading and circular trading. On the other hand the key role of regulatory bodies is to protect the interests of the investors and citizens. New regulations, increased scrutiny and scandals have all increased the need for sophisticated analysis and monitoring for malpractices. Analytics is helping exchanges and regulators in India to identify relationships between investors, fradulent behaviour, manipulation patterns and new unseen patterns for investigators to identify and detect fraud earlier.

Automotive manufacturing: India has a large base of local auto makers as well as foreign players. Add to that a huge customer base with varied preferences and a mindset largely focused on pricing, and you gain an understanding of the challenge to gain profitable market share.  Analytics is helping leading players in India to determine accurate demand forecasting of various vehicle models, warranty analysis, campaign management and social media marketing. The high performance organisation has a single customer view, enabling them to run targeted marketing campaigns, to optimise spend and improve sales.

Government and Public Sector Undertakings (PSUs): For a country of over a billion customers (citizens), the Indian government can be viewed as an organisation that needs to cater to each of them in various ways. This duty carries the many complexities of multiple regions, central policies, state policies, a large number of ministries and departments; and governance in such a scenario becomes an almost herculean task.  Analytics is playing a pivotal role in the Indian government, enabling the policy makers to implement citizen welfare policies in areas of healthcare, inland security, defence and income tax to excise, customs and census.

Finance Industry: The banking industry in India has a long history, from the traditional banking practices to the reforms period, nationalisation to privatisation of banks, scheduled and cooperatives to foreign multi national banks. The opportunities and challenges run hand in hand. The quest for acquiring more customers through providing various services to the issues such as risk, fraud and economic uncertainties. Analytics is helping Indian banks and insurance companies in the areas of integrated risk management, identification of customer characteristics that lead to deliquencies, cross-selling to existing customers in areas like rate making, claims fraud and campaign management.

Retail: The Indian retail sector is amongst the largest in the world and is experiencing rapid growth to address swelling market demand in an ever-changing customer driven world. Growth in the sector has created a highly competitive environment with foreign players opening shops. In such a market place, retailers are under constant competitive pressure for customer wallet share, improving customer experience and loyalty, meeting growing customer aspirations, increasing its breadth of merchandise, and expanding store operations in to new markets.  All the while needing to maintain profitability.  Analytics is helping leading retailers in India with insightful analysis of merchandise, assortment and inventory management, loyalty, campaign management, shelf-space optimisation, including real time knowledge about sales, and store performance.

Telecommunications: The telecommunications industry in India has a big market potential and is a fast growing sector. India has the world's second largest number of mobile subscribers, with over 900 million as of beginning this year. Many challenges are facing this sector, like ongoing price wars and high government taxes.  Hyper-competition is also bringing in other challenges like eroding revenue and decreasing profitability. Communication providers are now focusing on data and content services to increase revenue and profitability. Analytics has been key in helping these organisations to effectively address issues from churn, cross-sell/up-sell, price plan optimisation and network optimisation,  to areas like identifying potential high value users, campaign management and nurturing profitable relationships.

Data is key to decision making. Big data has placed pressure on Indian organisations' ability to manage the conversion of data into insight to make better decisions.  Welcome to the era of competing on analytics.

QUESTION: What could analytics do for your industry?

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?

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:

• Printing customised dockets at the supermarket.
• Having call centre staff that know which products you have already.
• Placing relevant advertisements on websites which are based on what you are currently doing.

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.

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.

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.

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.

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!

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?