CAANZ Business Forum 2016 – Takeaway | Minimum Viable Product

 

The standout questions from our presentation at the CAANZ 2016 Business forum was on the move away from traditional IT “build it all once” / “Build it and they will come” mindset to the MVP mindset.

Key MVP slides from our presentation summarised here:

Minimum Viable Product is significantly cheaper than the "build it once" mantra of old technology planning. Business simply can no longer afford the wait for solutions.

Minimum Viable Product is significantly cheaper than the “build it once” mantra of old technology planning. Business simply can no longer afford the wait for solutions.

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Why CFOs and Business leaders are so critical to getting value from Big Data faster

Business Forum 2016 in WA

 

 

 

 

 

Big data, smart algorithms and predictive analytics is here to stay.

Well, if the emerging levels of digital and data has proven immense value creation and rapid disruptions of old models … Then why do so many businesses leave this strategic imperative to the IT department or outsourced consultants?

It turns out that the business nous and collection of natural anomaly detection algorithms qualified accountants take for granted is extremely well suited to enabling organisational analytics.

Qualified Accountants that operate and lead in the business see value, and effortlessly translate from empirical evidence to business strategy. Business leadership is crucial to get the insights that enable organisations to lead the change they want to see, rather than reacting to what is ultimately forced upon them.

In the Chartered Accountants of Australian and New Zealand’s 2016 Business Forum, Coert Du Plessis will demystify the rapidly evolving big data jargon and systems, share the fundamental building blocks of analytics strategy and build a bridge between the chartered accountant skillset and the urgent need for data driven leadership.  16 June 2016, WA.

CA ANZ Business Forum 2016 Programme

 

 

How Big Data helps with early indicators of mental stress

#BigData + #Wellbeing in #Safety.

Many say it can’t be done. Mental Well being is something only humans can understand! They say that a human connection is crucial to understand the well being challenge we all face in the workplace.

  • In this November talk at IFAPs 2015 Fluoro conference I explored why these positions are wrong and where data really works – The slideshare below has 3 messages.
    We need to stop “Think we know” and move closer to “know we know”. If you don’t understand the true power of big data and how to use it predicting human behavior, and identify unusual behavior – you don’t know what you don’t know – and therefor, not qualified to comment on the impact data can have on early identification of mental health and well-being risks
  • The data only channels our focus and measures impact – ultimately, interventions and verification of results remain a deeply human process, with emotional connection – The point being, that the energy and resources here can be focused on those that need it most.
  • To really hone this amazing gift from big data in improving the outcomes of our workforce, we need to think differently about capturing more data – leveraging emerging areas such as Virtual and Augmented reality, positive reinforcement and wearable sensors.

Ultimately,  the journey into a truly proactive health and well being business requires courageous leadership to guide the business through step change thinking and facilitating a difficult mindset change for those who are used to the current way of “how things are done” to “how things should be done”

Article: Why does WA trail in digital marketing innovation?

Time to take stock of where WAs marketing smarts track on the digital curve…

Daniel Hatch of WA News wrote a nice article following an interview re my upcoming talk at the WA ADMA conference. Read the WA News Article

What is holding WA back from rapid transformation?

WA marketing professionals know that Data and Digital trends are transforming the competitive landscape. But for those in the know, WA is not seeing the same rate of adoption of these trends… yet.

 

C.B.U… Correct, But Useless | The growing tragedy of poorly led analytics

CBU - Correct But Useless

 

Driven by an innate desire for efficiency, I have been compelled to create a new acronym (like we needed more!).  But the need was real and I needed an efficient way to respond to the question I get asked most frequently these days…

” What do you think of this awesome piece of analysis?”

… and more often than not, the answer is simply C.B.U….  or Correct, But Useless.

You see, we have been getting caught up in the sexiness of new discoveries… stories that used to start with “once upon a time” now start with “crunching our data revealed…”

This hype has been fueled by the media and blog posts (like this site – sorry). Today, looking inside the larger corporations, we see an internal managers’ race to have the most “awesome insight,” the most impressive counter intuitive knowledge gem with a big $$$ next to it; ready to broadcast as a prized possession you can forevermore lay claim to.

And it is not hard to see why. Today, previous measures of “importance” – may that be your position, title, your perceived pay or special perks – is starting to play second fiddle to an emerging class of business leader – the leader that gets digital disruption. These leaders can channel the incredibly uncertainty that digital disruption is bringing into an invigorating and compelling vision of opportunity for the future…

These leaders are rare and in particularly short supply in Australia. This vacuum means opportunity; And here is our problem. Some of our leaders feel that if only they can find that most awesome discovery in data and broadcast it to the leadership team and the Board, they will be seen as that sought after “digital disruption” leader; the enigma the executive keeps talking about finding. I call these leaders “digital imitators”… They know the buzz words, they talk the language but never seem to be able to link value and analysis.

As you know, the path from Insight to Impact is not an easy one. These
digital imitator leaders often fall in the trap of what is “interesting” vs what is “impactful”… their actions and allocation of scarce resources ultimately hurt the organisation that is trying to catch up with the “data horse” that has indeed already bolted in many industries.  By the time “the imitation fraud” is discovered the company has invested so deeply in these “interesting” projects without payback, it is almost impossible to back out and restart without deep board level scrutiny – and no one wants that.

I am by no means suggesting that you should know the answer to the insights you are seeking before embarking on your projects, or that agile iteration and discovery should be abandoned – quite the contrary. You do need the “hoodie” mentality to find different insights… quickly. But what then?

Where our current digital imitator leaders mislead the data scientists and internal teams, is the lack of a vision at the start of the analysis journey. A vision makes it easier to know what to do once we have insights…. how to move to impact by empowering the organisation.  These digital imitators fundamentally underestimate the effort to change… and the biggest drawback here is that those managers themselves have not transformed their own thinking.

… so what to do?

If you grew up in the middle ages of flat earth, literally shifting the centre of your universe to the sun is no easy feat.  The same fundamental (and difficult personal) paradigm shift is required when leading through digital disruption.  Only a handful of business leaders will make this discovery on their own.

For the rest of us?  It is time to change inside out.

For a starter, stop looking “up” for guidance. Your newest and youngest employees likely know a whole lot more about data and digital disruption than you – But don’t bother asking them what they think of digital disruption. They just don’t call it that – to them, it [digitally disrupted businesses] is “just how it should be”.

  • Start by listening to your young employees ideas on what can be done; how things could work. Expand your immediate network to people you would not normally interact with; Ones that talk fast and has many ideas are good in moderation. When you start feeling uncomfortable, you are on the right track. A word of warning, in most organisations, traditional IT support is not a good place to start – their governance structures and approach (e.g. ITIL) means that iterative thinking does not work. Even the IT teams who call themselves agile often are tangled up in their own red tape. The test is simple. If in your first 5 mins of your new best friend conversations the words “we can” far exceeds the “can’t do” you found the right person(s).  Remember, the “can” attitude applies to you too.
  • Read – Set aside 15 minutes every night. There are amazing forums out there and LinkedIn groups; it won’t take you long to figure out who are the real thinkers. If you don’t know where to start, I suggest research “Target predicts girl’s pregnancy” and “Lessons from Target’s pregnancy algorithm” – by researching this one well published example you will learn from different perspectives about the power of data, complex techniques, the need for good change management and practical implications at the front line. I follow Tom Davenport’s writings. I also like Wired.com
  • As in “Just do it,” with a catch. That catch is you need to understand to WHOM the insights will be useful and what that will allow them to do; Remember, we are trying to be more than just interesting to those we need to impact. This process of designing for usefulness is called Customer Centric Design – the outcome is that your insight must change something – someone must start or stop doing something… or confirm that they are doing the right thing.
  • Keep a diary in  short single line notes on what you think 5 basic terms mean to you. It will evolve. Everyone will have their own list.  E.g. Analytics, Information Management, Digital Disruption, Big Data, Stream Analytics, Automated Decisioning, IoT, Autonomous systems, Simulation, Deep Learning, Artificial Intelligence, Singularity, Cloud Analytics, Mobile platforms, Social Analytics, etc.  Revisit your personal definitions every month. You will be surprised to see how much you have learnt each month.

Ultimately, leadership through digital disruption is opening yourself to the vastness of new ideas. In my experience, every organisation is a pressure cooker of amazing ideas suppressed by average managers and unduly risk avoiding executives.  Find those bright-spots and find a way to unlock the potential of the individuals and the organisation through real impact. You will be happier doing meaningful work.

Now that is leadership.

 

Moving Big Data to Big Value

Moving Big Data to Big Value – Insight to Impact

It seems the world is all fascinated with amazing insight from Big Data… but we all know what really matters is the VALUE unlocked from those insights…

Too often we assume that smart people will know what to do if the Masters of Data Science unloads new wisdom on the business. The reality is we have to empower the ultimate people who have to act on these new insights with processes and business levers that also smarter.

In this presentation, we explore what is the difference between insight and value… the difference between a finding that is interesting, and a finding that has impact.

The presentation captures a career of learnings in Big Data and Advanced Analytics as the Lead Partner who established and led Deloitte’s Advanced Analytics practice in WA

 

Corner Office Analytics – how to engage with CxOs on Analytics

I often get asked how to have a strategic analtyics conversation with CxOs.
This great resource on “corner office analytics” talks about the key agenda items for CEOs, CFOs, COOs and the other CxO titles. A good way to think about what the analytics opportunity is and how to make the analytics conversation valuable.

View by Portfolio: http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/corner-office-analytics.html1

Interactive infographic: http://public.deloitte.com/media/corner/corner-office-analytics-infographic.html

Corner Office Analytics - interactive graphic
Corner Office Analytics – interactive graphic

From the website, “Business analytics used to be the domain of a few select teams buried deep in the business. Today, it lands on the agendas of most CXOs. And success hinges on CXOs’ abilities to collaborate with one another…”

We faciliated Advanced Analytics Training

DAI_TrainingFor a long time now, our team has been asked repeatedly by our clients about sources of quality advanced analytics training. Not just tool training, or statistics training… but training in the essence of being a data scientist; thinking differently about data. We call this “thinking different about data” – Advanced Analytics thinking. The reality is that other than a few university courses that will require serious time commitment, we have not come across software and tool agnostic analytics training that really helps analytics teams work together; help their managers improve how they use their data scientist resources and improve the analytics processes used.

So, in the spirit of empowering the WA analytics community, we have now made the same training we provide our staff available to our clients and approved partners on a trial basis. The training follows a similar progression style to the six sigma belt system. The technical training that most data scientists would be perusing is provided in yellow and orange belt, and components of green belt. The blue to black belts are management and senior management level training and away from the technical delivery aspects. In my team, all Analysts, Consultants Senior Consultants and managers have to do yellow belt training and demonstrate yellow mastery through various engagements before moving to Orange Belt training.

On the 23rd of July we will deliver the yellow belt component of the training here in Perth. The training is software, platform and tool agnostic. Even though we will use a range of tools on the day, the training is designed to be transported across tools and the facilitators will be able to explain differences. E.g. Whichever visualisation tools you use – Tableau, Excel360BI, Spotfire, Qlickview – the training is relevant to all those platforms as we are delving into analytical thinking as a process, technical application of the process and importantly how team members in analytics work together efficiently

Download the registration form here: Deloitte Advaned Analytics Training – DAI – 23 July 2014
Payment via credit card; EFT payment option also available.

Design Thinking

For those of us working at Deloitte, Design Thinking is something we come across all the time. It’s one of the Four D’s … one of our differentiators:

  • Design
  • Digital
  • Data
  • Deloitte Access Economics

But what exactly is Design Thinking? I’ve posted some articles and videos that give a pretty good overview, and show how we use it at Deloitte:

In particular, the last article highlights the power of the “brainstorm” and building some sort of “creative order” out of the chaos of ideas that are generated.

I recently spent a week in Melbourne with other consulting grads from across Australia and New Zealand getting to know our consulting process better.

One of the sessions was held in a space called “The Source”, a huge room filled with surfaces on which you can write, brainstorm and design to your heart’s content. The room embodies the core concepts of Design Thinking.

Two hours in that room, and we had the walls covered with our thoughts and ideas on various problems. It’s crazy looking at the sum of everyone’s thoughts when they are drawn on all of the walls. Key points, themes and messages start to stand out at you and others quickly start to iterate ideas and change viewpoints. Prototypes and potential tests are formulated, stories and anecdotes are re-lived. Surprising the journey you can take in only two hours!

Coupled with other concepts such as the Situation, Complication, Question and Answer framework, and Structured Problem Solving, Deloitte Consulting grads are equipped with some pretty powerful tools within a few months of hitting the ground! Lock up your whiteboards and hide your butchers paper!

I hope the articles above are able to give you a taste of what Design Thinking is all about

… it’s not just another one of those dreaded consulting buzzwords or catch-phases.

Analytics Trends for 2014 | The year Big Data Kills the Data Warehouse?

I came across this beautifully written whitepaper on 2014 Analytics Trends. The author list includes analytics demigod Tom Davenport and a raft of other impressive leaders in the field.

What I really like about it is that it talks about current issues in a succinct and easy to read format. Is there really a Data Scientist shortage? Is the Enterprise Data Warehouse dead? Will the Chief Data Scientist become the next “in thing”…. A great read and I share their views:

For example, on the talent shortage I see too many brilliant Data Scientist minds being recruited into disparate corners of organisations doing mundane and incremental reporting – something the EDW should be able to do – and under-utilising the skills of the data scientists. In many ways the data scientists have fallen in the trap of the analytics hype by being recruited into a role that was designed on better reporting needs, often justified as advanced analytics investment to plug a real problem for the organisation…

Let me know what you think in the comments section below.

Sports analytics

Like sports? … but more importantly, like data? Big data? Proper big data? Then the following will be right up your alley …

DataBall

Being an AFL nut (Australian football, of course!), and working with my head deep in data most days, something like this naturally interests me. You might remember one of our posts on a little analytics experiment we conducted for dARTa Studio trying to recognise the various factors influencing a team’s probability of winning a given match. Our dataset for that (20 metrics, over a few hundred games, over a 20 year period) pales in comparison to this.

Large datasets in sport is nothing new. American baseball have been doing it for years, in great detail. Back home, our AFL and its teams have their statistics collected, packaged up and licensed back to them by companies suchas as Champion Data. The numbers in these (already huge) datasets relate to key moments in time. For example, recording the outcome of a pitch by a baseballer (curve-ball resulting in single), a shot from a basketballer (two- or three-point shot), or the result of Matthew Pavlich’s kick from the top of the square at Subiaco Oval (out on the full, behind, goal).

The exciting data comes when you start recording the information that lives between each of these events. What was the setup of the fielding team at the moment the baseball was pitched at Alex Rodriguez and he hit a home run; what was the offensive and defensive positioning on the basketball court when Koby Bryant made the shot from “down-town”; what did the rest of the football field look like and what were the velocities of the players when the footy left Pav’s boot?

When you start collecting this kind of data, which to many might seem superfluous, or excessive, you cross into exciting territory. In the case of the article above, we’re talking about recording the position of every player and the ball with split-second granularity (wow!).

The sky’s the limit when you start to think about the kinds of insights you can tease out from this information. How quick is someone from a standing start? Is a player more likely to spray the shot if they are under pressure at a certain spot on the field or court? Or, in the case of the article, what is the “Expected Position Value” at a particular point in time?

It’s an environment ripe for analytics! How do all of these seemingly irrelevant data points come together to tell us something interesting, and most importantly, actionable about our subject matter?

Sports analytics is slowly, but surely becoming a recognised, but more importantly, appreciated tool …

… much the same as analytics in business. We’ve been doing it for years!

Analysing Video Conferencing and Audio Visual facilities

A little while back we worked on a project for a client which was a bit different to the other jobs we have been doing here in Perth. A client approached us with a problem:

Our business is spread over a number of locations, each with their own Video Conferencing and Audio Visual systems. The problem is that these systems are starting to get old and need replacing. Our budget isn’t large enough to replace everything in one go, so how do we prioritise the roll out of equipment replacements?

Now this business is seriously large, with more than 200 meeting rooms with either video conferencing (VC) or audio visual (AV) equipment, such as a computer and projector, in at least 5 buildings.

We thought that it would be worthwhile running you through some of the methodology and steps that we went through in this analysis. It is important to note that Analytics is not just about getting data and running it through a model and coming up with a magic answer at the end – there is a lot more to what we do and making sure we take the client on the journey of analytics is extremely important.

Step 1 – How do we assess each room?

The biggest question was where to start – there are so many factors that could influence a roll out strategy. After some brainstorming with the client, we settled on a number of key themes that we thought were important to help characterise, and prioritise rooms for equipment refreshes. Some of those themes were:

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