In many ways the challenge in communicating the complex and technical details behind Information Management has been my greatest joy over the past few years — and yet also frustrating. Standing before key decision makers in a number of organisations and trying to explain why this is so important, how it can radically change a business and what it could be can be incredibly exciting — when I get it right. Yet every time I walk away from a meeting feeling like I haven’t connected with a CEO or CTO, I feel like I’ve wasted an opportunity. Information Management and it’s related capabilities of analytics and business intelligence is critical in the Information Age and it is my driving desire to see all organisations introduced to the endless wonder of truly understanding how and why their business works. The only way I’ve found to do this is to place these complex and technical details into stories and analogies — examples if you will — of how and when this would be useful; taking my listeners and readers on a small journey until they see just how amazing this could all be.
With this in mind, in todays article I want to start a 3 part miniseries describing my efforts building a Smart House. Given I recently built a new place, this is one of those great moments when I get blog about something I’m actually doing right now! Along the way I hope to draw out some principles about Information Management and Data Analytics so that you can see parallels in how they might be useful in your own organisation.
ContextImagine I live in a city which is truly networked. Each house communicates with a city super computer which then uses this information to deliver power, water, gas and other services to my house to my house in the most efficient manner possible. The benefits are enormous — infrastructure costs are down, the power grid is stable and the city has seen an absolute boom in house prices!
To enable this incredible efficiency, the city mandates that owners are responsible for capturing data from their homes and deliver it to the network through a list of approved connections. Owners can do whatever they like within the bounds of their own home (provided it’s safe) and be as creative and interesting as they choose.
Because I’m a bit of a tech nerd (this is NOT mythical) and I love beautiful spaces, I’ve decided that I want to set up my house with some beautiful lighting to highlight key features, my artwork and provide a deep, warm ambiance. Furthermore, I want to contribute to reducing my energy footprint, have a limited budget and want a system which is simple and easy to use. In the future I want to look at storing all my usage data so that I can use some Artificial Intelligence methods to further improve my consumption and make it more efficient.
​
Step One: Building My Infrastructure
To start with, I sat down and considered what I wanted to do, reducing it down into four main goals:

1. Create beautiful spaces,
2. Reduce my power usage,
3. Make it simple to use (my iPhone?),
4. Have a storage capacity.

As a picture, it looks a little like this:

Then I had to figure out how to make it all together. Ask myself — how can I get all these different things to work together? How can I meet all things important to me without spending months getting there? Luckily, because I was building a new house, it was pretty easy for me to build out a network which was easily extendable, able to connect to all different kinds of devices/things and be gathered together in a single point. For a small fee, I arranged with my builder to include fibre optic cabling through my house, including access points in the lounge, both bedrooms and office. This allowed me to plug in many tools, was easy to use, and provides ready access for guests and visitors. Furthermore, anything I added to my infrastructure would be modular and easy to upgrade.
The AnalogyFor many organisations, information infrastructure investment is one of the most difficult and expensive processes they are faced within the initial stages of building an effective information system. It requires an intimate knowledge of who you are as an organisation, where you want to go and a lot of smarts to design a network not just now, but for the future. I was fortunate — I knew exactly what I needed, had ultimate flexibility in getting there, and could afford to execute quickly on my plan. Many other organisations do not have this.
Yet I would also propose that this is one of the first and best times for an organisation to make a statement about who they are as a company, where they are headed and why this is important. Truly going through an infrastructure program requires many questions to be asked and answered, and is the opportunity of a lifetime to bring all the decision makers together to work it out.

The discussion about infrastructure investment should feed directly into your vision for YOUR organisation. It will establish what you need, how it’s to be delivered and how it will contribute to your business being amazing!I would also state that this is the best time to start building your Information Team. Giving them significant input into your plan can have huge benefits and save you a ton of cash and time!

Step Two: Choosing My Tools
Having built my infrastructure, the next challenge for me was to figure out what tools I wanted to use to achieve my vision. As it turns out, this is really an exercise in design thinking — something I deeply love. I already knew that I wanted to create something simple, beautiful and elegant, but now I had to work out exactly what that meant.
Because I feel that ambience is a way to make a room feel warm and inviting, I started by looking at lighting. It turns out there’s quite a number of players in the smart lighting space, all with varying degrees of quality, usefulness and connectivity. Fortunately, every single option was an LED of some description which is great — low power, long life and generally robust. After a bit of research, I decided to go with the Philips Hue option — it seemed to be well thought out, quite optionable and I really liked how easy it was to connect. It was a bit expensive, but for me the simplicity outweighed this; particularly the number of reviews which talked about how consistent it was. As an added bonus, the Hue range also included a throw light (Hue Bloom) and LED strips (LightStrips), both of which would be amazing ways to highlight things around my home.

The next challenge for me was working through the storage/routing tools. This is a dual challenge, as there are many different ways of connecting up routers and storage spaces. For me, because I pretty much run Apple products, I chose to go with an Apple Airport Time Capsule. This integrated seamlessly with my other Apple products and was easily plugged into my network. For my router, I chose a FritzBox wireless router. Again, it is easily configurable and really powerful, making it easy for me to connect multiple devices.
I want to make a quick note here about my routing decision. As you can see in the picture below, my earlier decision to wire everything together is now paying off. It is super simple to connect my router to all my fibre cables, and this massively extends my network. This is just one of those things where having spent some time in the infrastructure phase, I’m able to considerably speed up my toolsets space.

It all seemed to be going well, and there’s many parallels I could bring out of this story. Things like taking the time to work through the problem. Investing heavily in solutions which suit your values (i.e. simplicity for me) and not being afraid to reuse tech you already have — provided it makes sense.
Yet the final part of my initial build was where it all started to come unstuck. Powerpoints. Who would have thought that this was where it would all start to get difficult! Yet there I was trying to work through my values, figure out what to do and how to do it when I realised I was approaching the problem all wrong!
I’ll share the challenge and the solution next post, so stay tuned to see what happens :) As always, I’d love to hear and receive your feedback, thoughts, ideas and comments.
James

As I go around different organisations looking at their information systems and how they manage, analyse and use their data, there are a number of common themes which breed success. In some of my previous posts I’ve talked about how important it is to build a data driven culture; to employ the right people; and how important it is to build analytics and business intelligence upon a solid information management platform. All of these things are true — and in many cases not that much different from success in other areas of business. But today I want to share some of my thoughts and observations about the beauty of an amazing User Exerience and how a well designed and constructed user experience and user interface can increase information quality and convey decision information.
Improving Information Quality
I’ll start with what is arguably the most important aspect of information management — ensuring the accuracy of information. Accuracy of information is critical for any information system, as without trust in the information being provided it is impossible for analytics and ultimately business intelligence to succeed.
While a huge body of work is devoted to using machine learning and statistical methods to reduce the impact of incorrect information from users, one of the most surprising things I’ve observed about well designed systems is their use of visual cues to users to reduce user error. This isn’t just a minor observation — in an article by Oleysa Krysynyak entitled ‘How to Save Lives by Reducing User Interface Errors’, links are made between UI design and providing fast, reliable and easily understood information to doctors, literally saving lives!
Visual cues provide almost unnoticed pathways for users to enter information. Things like consistent color schemes (i.e. red is always bad, green is always good), consistent transactional interactions and a commitment to a reduction in clutter all contribute to providing a seamless experience for users and induce far less errors. As a result computing resources are freed up and the analytics and business intelligence which supports decision making increases in impact.
This is a non-trivial challenge. In teams I have worked with in the past, up to 50% of our time was spent working through each user interaction and relating it back to common themes we wanted to convey. Often times we would find that much of our programming construct needed to be changed to simplify variables and data tables, further refining our data sets and assumptions about users. In turn, this would create further discussion about how we wanted to convey information. Yet by sweating each detail of the user experience, and bringing an unrelenting commitment to convey information clearly to users, we would come up with sometimes amazingly simple answers to complex problems. The beauty of this kind of user experience is that the quality of the information being gathered is increased without the user even being aware of what is happening!
Decision Quality Impact
My second observation about well designed systems is their ability to convey often complex and comprehensive data to users in a manner they can understand and react too.
For me, this was bought home on a system I worked on which displayed some performance metrics for a platform. A previous organisation had come in and could track all sorts of interesting things — the uptime of each platform, the performance of different components for each platform and how frequently this had contributed to success. Yet as my team and I started to work through the consultation process with users, we quickly discovered that the presentation of this data wasn’t helping users at all. What they really wanted to know was which components failed the most, which components contributed the most to cost, and which stuff was going to be the most critical for them to solve. All of this information (other than the finance data) had already been gathered by the previous organisation, but it wasn’t helping the users of the system. As a result, the quality of their decision making hadn’t improved.
In contrast, great information systems come up with innovative ways to take a broad series of data sets, combine them together and present the results to users. In many cases, these teams behind these information systems end up finding solutions which are so simple in their elegance, and so well presented in the User Interface that their end users are entirely abstracted from the complexity of the problem!
This kind of User Experience is beautiful in its ability to allow individuals to focus on their own areas of expertise. Programs like Tableau and Highchartsallow User Experience designers to present complex information sets in a simple way. In turn, this allows users to use their expertise to contribute to the organisation. This in turn allows managers to know what is important and so on. The end result? Users are able to make accurate, timely decisions from the data being presented.
Users Don’t Hate the IT System
My final point in this post is one of my pet passions — building systems which actually help users. Far too often I see organisations which spend millions on marketing, talk a great game to customers and then punish their employees with substandard DOS based, text driven systems which require an intimate knowledge of search strings and building SQL queries to actually get information out of it.
These kind of back end systems don’t help users or customers at all, and almost always the outcome is counterproductive to the organisations goals. If users of the system hate it, they won’t use it to get good information, won’t be able to help their customers and they will figure out ways around it. As a result, the analytics of the information system reduce in quality, the organisation cannot get good business intelligence and everyone loses. All because someone somewhere didn’t take the time to get it right.
In contrast, great information systems become a tool to help users access the information they need to do what they’re doing better, faster, easier. When users don’t hate their IT system, everyone wins and a positive feedback loop is introduced. It’s definitely worth it!
So What To Do Now?
In my opinion, building a simple, elegant User Interface and User Experience is one of the great contributers to Information System success. I am constantly challenged in my work to keep refining systems, and often this refining starts with looking at how the user is presented, conveyed and interacts with data. It’s a time consuming process and while many people try to short cut the process, I’ve never found a way to do so without compromising effectiveness. For me there is no substitute for reading about psychology, learning to really listen to users and gaining an intimate knowledge of the goals of an organisation and what matters to them. I would challenge those who are reading this article to take the time to get this right. Take the time to support those UX designers in your team who really do sweat the detail, or, if you are one of those designers, I would challenge you take the time to keep investing in this area. In doing so, you’ll build information systems which will transform organisations into in the Information Age!!!
I hope this article has helped, and as always, please feel free to contact me on LinkedIn, Facebook, Twitter or in the comments below.

The Great User Challenge

You’ve drunk the Cool Aid, caught the dream, understand the vision. After many months of consideration and research, brainstorms and meetings with stakeholders, you’re ready to launch into analytics and bring your organisation into the Information Age. You know it’s going to take time, but you’ve invested in your leadership team and are building a data driven culture, backed by a cohesive data driven team. Funds are set aside for the change, and you’ve now become one of the agents for positive change in a data driven world.

So now you ask: Where to start?

Todays post is about answering this question and I’m going to look at three areas:

1. The User Concept
2. The User Framework
3. Technical thoughts

The User Concept
As always, the start of the answer is a discussion about the concept of Information Management and Analytics, both of which feed Business Intelligence.

Conceptually, Information Management is about people. In fact, if you abstract it a bit further, information itself is about people. From the perspective of business, all information is gathered, analysed and distributed to increase the efficiency and effectiveness of business. From this perspective, the discussion quickly becomes about how a user can measurably benefit an organisation, rather than being a problem which needs to be solved. 

To further distil this concept, I offer this definition of a user:

There is no distinction here about internal and external users, nor is there any reference to automated trawlers of an information system (also known as bots). This is deliberate as an information system should to be built to provide a seamless connection between customers, clients, employees and managers. When any of these groups (or malicious users) choose to use automated methods to trawl information, the information system should be capable of handling this.

For many organisations, this concept is not new. It’s been used successfully in customer service, product selection and is a straightforward application of the laws of supply and demand. However, the application of this concept to information systems is almost unheard off.

Consider this: Almost every organisation in the world (outside of tech paradises like Silicon Valley and New York) uses Microsoft Word, Excel and Powerpoint to store, write and analyse information in various formats. Even when an organisation uses a backend system like SAP to start combining their information stores together, often times this is copied and pasted out of the ‘Datamart’, transferred into one of these applications and then shared via email. 

Take a few moments to consider the time inefficiencies this introduces to workflow. Each point of interaction introduces delays, replication and error, all of which are considered necessary by the users of the system to do their job. As multiple departments get involved, the situation continues to get worse. Before you know it, people are creating spreadsheets to store their own little information empires (I call them spreadmarts), introducing macros…the list goes on.

The User Framework
In contrast today, I present a more excellent way. I call it ‘The User Framework’.

The User Framework is based around three assumptions:

1. Users want to achieve something from an information system
2. An information system changes over time
3. Analytics and Business Intelligence are about empowering people

Here’s a simple flow chart of how The User Framework operates.

There’s some interesting points to be drawn from this:
Firstly, any information system which uses this framework needs extra storage capacity. This is not actually a problem, and I’ll cover why in the technical part of this post.
Secondly, such an information system will change over time. Tracking what a user does and then analysing this to find efficiencies can provide some pretty radical results. For instance, when you find that a user is accessing a particular information daily, you can drastically increase efficiency by surfacing this information sooner, reducing search time. This is not to mention the efficiencies in being able to preprocess results.
Thirdly, this kind of information system creates an iterative improvement framework, where it gets better and better over time. This is important as it allows an organisations competitive advantage continue to improve.

This kind of system is incredibly empowering and enabling for organisations. Over time and with the right team, the quality of data will improve as will the analytics being performed. Furthermore, as the analytics team continues to go deeper into process flows and outcomes, the requirement for information will be refined, leading to a higher quality of business intelligence.

Technical Thoughts
A post like this would not be complete without some mention of the technical aspects of such a proposal. I will state up front that I receive no commission from these mentions; these are just tools I have used and/or know and am impressed with.

In mentioning these things, I want to be clear that each organisation will take a different path to Information Management. I’ve worked with companies who outsource almost all of this stuff, through to ones who choose to do it entirely in house. I have also personally worked on building aspects of these systems, and it takes a while. As such, the mix of products you choose to do these things will be individual - however, I also believe the products mentioned below will meet almost every organisations needs.
I have also witnessed some pretty poor advice being given by software/hardware vendors in the past. Any organisation or IT staff who claims that increasing storage capacity or server power is difficult is not telling the truth. If you get this, find a good company to help you (like mine)!!!

Storage - Red Hat
This is becoming more and more of an issue. Many organisations are stuck using proprietary systems which charge astronomical amounts of money for adding storage and server capability. In my honest opinion, a better way forward is to look into the many products Red Hat offers. Not only is their code open sourced, they have a long history of figuring out ways to take legacy programs and import them with no loss of data.

Web Backend - Django or Ruby on Rails
Most organisations around the world are starting to realise that the most efficient way to create this kind of information system is using an internal web server of some kind. Both Django and Ruby on Rails are open source with massive communities.

Analytics Language - Python
There’s a lot of debate about this in the analytics world. My personal preference is Python, simply because its so broad. Furthermore, Python integrates with Red Hat and Django seamlessly, which for an end to end solution is pretty powerful.

Server Stuff - Red Hat Linux

As the concept of becoming data driven embeds more and more into business operations, and more and more information systems are brought online, one of the central questions which is asked by CEO’s and executives across the world is - ‘What kind of person, team or contractor should we employ to achieve what we need?’

Getting the right people involved in data driven business is the primary issue of the decade, with businesses rising and falling on the quality, diversity and integrity of those they entrust with their data.

In this post I want to share my observations on what I believe to be the top five defining characteristics of excellent Data Driven Teams. Teams of this nature create enormous competitive advantage for their organisations, finding new ways to increase efficiency and delivering decision quality information to decision makers. As a side benefit, they are often able to help businesses execute quickly on new market opportunities, creating a positive feedback cycle for all involved.

Getting the right people involved in data driven business is the primary issue of the decade!

​#1 Passionate About the Business
Every single successful analytics team I’ve ever met has exhibited this characteristic. It’s not just about the way they dress or talk, or the lunches they have or perks they enjoy - there’s a buzz around them. When you hear them talk about their company and the intimate details about how the organisation works and how they found this AMAZING insight when they combined some different data sources, you walk away feeling inspired.

A great example of this is a post I recently read by the Air BnB team, talking about migrating their data. I’m not going to expand on the details of what they did (although it was impressive) but what I noticed is how passionate they were about their organisation. I could feel the excitement of the challenge oozing out of them, leaping out from the page. Yes, there was some technical details in the post, and yes, the challenge they faced was significant, but it was clear that they loved what they were doing and were determined to succeed.

This kind of passion is what separates organisations who do data because they’ve been told what a great idea it is from those organisations which see the transformational benefits it brings. For me, I was so inspired by the Air BnB post I actually applied for a job there!
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#2 Know technology
The second defining characteristic of an excellent Data Driven Team is a fundamental commitment to becoming technical experts in their field. This is critical.

The Information Age is fast gathering pace, and every day there are new methods being released to market. Even over the past couple of years there has been the rise of No SQL database infrastructure, the introduction of Meteor, a continuing development in the thoughts and principles of REST based web access, big data analytics like Hadoop and so on. This is not to mention the incredible developments in cybersecurity including user behaviour analysis, encryption and the growing rise of mobile interaction.

For many organisations this constant barrage of technologies appears to be simply a very expensive and continuing hobby for a group of people who like doing interesting things with computers. In truth, each of these new technologies, architectures, ideas and concepts come with their own advantages and disadvantages and an excellent Data Driven Team will be able to explain this.

As time goes on, this defining characteristic will be clearly felt across the organisation as information becomes more timely, more accurate and more in depth. It will revolutionise the way you see data and create depth to your activity.

#3 Exercise Leadership at All Levels
Leadership is defined by John C Maxwell as 

Leadership is not about titles, positions or flowcharts. It is about one life influencing another.

It is this ability to influence those around which is a defining characteristic of excellent Data Driven Teams. As organisations build effective informations systems and begin to dive deep into what it’s telling them, there comes a point where the data needs to be translated from geek language into decision maker language. Only excellent data analysts and scientists can make this link, figuring out the most effective graphs, plots and emails to send to the right people.

It goes deeper than this though. Understanding the right data almost invariably leads to tradeoffs between efficiency and effectiveness, often related to timeliness. Due to the nature of data analytics, these tradeoffs typically exist in the realm of extremely technical details, which means that only technical experts can really make a decision. 

When this defining characteristic is missing, teams become paralysed by inaction and unable to present their case to the organisation for further action. The impact of this will not be felt immediately, but over time information quality will diminish and the usefulness of the analytics team to building competitive advantage will disappear.

#4 Able to Focus on the Big Picture
Many IT teams attempt to solve every individual issue on a case by case basis, never taking the time to look at the broader issues being raised. As a result, the teams end up with an increasing burden of supportability for individual solutions, effectively parcelling out their time on issues which of only minimal benefit to the broader organisations. 

In contrast, one of the defining characteristics of excellent Data Driven Teams is the ability to contribute to the larger organisational goals. This means being able to assess the distinction between smaller issues with large implications and larger issues which have individual implications. Excellent teams often spend a lot of their time on whiteboards and in meeting rooms plotting data flows and implications, circling back to users and how to provide analytics - but always with the larger picture in mind. As a consequence the entire organisation benefits as information flows are streamlined and analytics steadily improved. 

Ironically, when this is done correctly, the change within the organisation so subtle it’s often missed. Individuals end up happier and more motivated as they are being provided relevant data and decision makers at all levels feel more supported by the data they are being given - but often the actual Data Driven Teams fade into the background. Long term the organisation will streak ahead of the competition, creating an almost unbeatable competitive advantage.

#5 Team Players
Finally, excellent Data Driven Teams are defined by their ability to play as a team within a larger organisation. They are generally the first to get involved in a new idea or brainstorm, looking at ways to help, rather than hinder. They seek opportunities to get involved in departments and with staff so they can better understand requirements. And most importantly of all they realise that in many cases they are only one aspect of the broader organisation.

These kind of teams are an absolute pleasure to work with. They get the point. They want to help. They are incredibly invested in the organisation and they really want the company to succeed. For the organisation, it’s of huge benefit as they find new and better ways to improve information flows and analysis, providing a continuing competitive advantage. Returning to my example of the Air BnB data team, these kinds of teams are there to serve their organisation and take the abstract and mundane and make it amazingly relevant. 

Conclusion
The end result of these defining characteristics are teams who provide incredible benefit to their organisation. They understand the business and care deeply about it, working hard help it succeed and grow. As new technologies come in they will continue to grow their organisations data driven capabilities and create enormous competitive advantage.

Take the time to invest in building your team, as they will become your single greatest advantage in a fast moving time in history!!!

As always, if you like what I'm saying add me on social media - I'd love to hear your comments!

As the Information Age continues to gather pace, there’s three terms which continue to pop up about becoming data driven:

1. Business Intelligence. Also referred to as Business analytics.
2. Analytics. Covering the areas of predictive analytics, proscriptive analytics, big data analytics.
3. Information Management. 

These three areas are generally presented as three separate capabilities, each offering something the other areas can’t. This perception is reinforced as the companies offering these services continue to try to differentiate themselves by coming up with all sorts of creative names and descriptions. To make things even more confusing, there’s hundreds of different technical terms which get thrown in - things like Hadoop clusters, test driven development, responsive design and my favourite architecturally driven dissemination (whatever that means).

The result of all these words getting thrown around? Key decision makers who know the exact effect they want have no way of deciphering the noise to see if the method being proposed will give them this result. 

In this post, I’m going to offer my perspective on these terms and explore the central question: Is this method going to give me the effect I need?

The Endstate: Competitive Advantage
It doesn’t really matter if you’re a not for profit, government organisation or good old profit driven enterprise, the entire purpose of organisations is to deliver lasting value to key stakeholders. It follows, then, that the entire purpose of becoming data driven is to allow your organisation to create a competitive advantage through the capture, analysis and dissemination of relevant data across your organisation. In my mind, I define this as:​

And that’s where it really starts. There’s actually no point in investing in becoming data driven if there’s no competitive advantage to be gained. Furthermore, unless an organisation invests in becoming data driven with an understanding that this the greatest competitive advantage to be had, there will not be the will to go through what can often be an uncomfortable change process.

With this in mind, I present a picture on how these terms all relate:

Information Management.
Starting at the base of the triangle, I’ll tackle Information Management first. In a previous post, I go into detail about some of the aspects of what information management is, but in the context of this discussion, I’d like to add some thoughts.

Put simply, Information Management is really the delivery, when and how mechanism of being data driven. There’s plenty of different terms and methods relating to this, and the companies selling this stuff will often talk at length about their Operating Systems, Hadoop clusters, storage solutions and why their technical solution is better than the oppositions. One day I'll write about some of my experience in this area, but it's key to understand that regardless of using Red Hat, Windows Server, SQL Storage or No SQL storage you're still ultimately building the pipes which connect an organisation.

It’s for this reason that Information Management is at the base of the triangle. The first challenge for any organisation is to figure out what on earth they’re trying to track (data), who needs what (information) and how to get it there (management). Understanding this flow and then enhancing it will literally change how you do business!!!


Analytics
The natural desire once data is being gathered and information is being passed around, is to start analysing the day to day transactional nature of things to see what deeper insight can be gathered. There’s a fair bit of art and science which goes into this analysis, but really, from an effects perspective, analytics can be defined as:

This is where all the branches of analytics spring from. All the different methods which get mentioned in sales pitches are really about trying to give decision makers quality information about what is happening, relieving them and their staff of the burden of gathering this information themselves and it’s something I’ve written about in previous posts.

However there’s some pretty important intricacies about analytics which often get missed. 
Firstly, analytics requires a pretty equal split between data scientists and operationally focused individuals. It’s imperative that any solution is developed in tandem with an organisations culture and decision making process. 
Secondly, analytics is never ending (at this stage). It seems that the deeper you go into analytics, the more you realise that you’re just scratching the surface. For instance, in my latest work, I and a team of five individuals are tracking user interactions with data to start speeding up how quickly this information gets given to them. I’ll go into this in a later post, but in this instance, my team and I are actually developing analytics upon analytics, with predicted massive increases in efficiency. The take away point about this is there’s no such thing as a one off purchase in this area.


Business Intelligence
As the top part of the triangle, business intelligence spans the point at which analytics is fed through to decision makers. It requires an effective information system combined with quality analytics and will take a while to get going.

Theres a few points I’d like to make here about business intelligence.

1. All. Business intelligence is exponentially more useful when it is designed to be given to all decision makers, not just executives. When each member of a company is given access to a broader understanding of their roles and how they are being measured, it’s amazing to see the results.
2. It requires an information system. Often companies selling products make out that if you simply lay their analytics on top of existing information systems you’ll come out with some good insights. In reality this is almost never the case. Unless you have systems capable of delivering pipes of information around the organisation, including data warehousing, replication, error checking, web servers, machine learning and so on, your systems will be ineffective at best and at worst actually cripple your people. Take time to see the larger picture.
3. Define what is the ‘right’ information. It’s a simple facet of management, but often missed. Business intelligence is only useful when it is providing decision quality information and for this to happen, it is critical that this is defined for each position.

Does this method give the effect required?
Hopefully by now a method of assessing how effectively you manage your data is being drawn out, along with a framework for building your own capabilities. Here’s a few questions I typically work through with any team I’m involved with to continue to build this framework.

1. What are we trying to achieve? Is it process change, a new market analysis, or tracking of performance?
2. Who needs this information?
3. How is information currently being gathered?
4. Where is analysis currently being done? (it’s almost always in Excel)
5. Why is this information important?

As these questions are answered, it should become pretty clear what needs to happen next. If you have a solid information and analytics team working for you, the rest should start to shake out. If not, please drop me a line as I’d love to help!!!


Conclusion
To wrap up today, I’d like to take the time to encourage you if you’re on this journey. The Information Age transformation has just begun and it’s only gathering pace. Mastering the three areas discussed in this article will take time and be a hard journey, but it’s worth it when you start to see what can be discovered about your organisation! 

​James

The definition of an Epic Business
Epic businesses create lasting value for owners (shareholders), influencing their employees, communities and environment for good. It’s a huge challenge yet endlessly rewarding - something truly worth living for!!!

There’s a whole ton of stuff which goes into creating epic business. It starts with leadership (favourite author: John C Maxwell) and flows right into ethics, purpose, vision and process. Once you hit the practical side of things, you need excellent capital allocation, worthwhile returns on capital and ways of tracking relevant performance metrics - all supported by a management team who can create a highly motivated, passionate workforce.
​
Todays post is my argument that without epic analytics it’s impossible to build epic business!!!

Epic performance metrics
I’ll start with everyones favourite part of work: performance measurement - aka KPI’s, Bonus Indicators, What My Boss Rakes Me Over the Coals With or All Management Really Cares About.

There’s a bunch of research about how important tracking performance metrics is. Many management courses teach the theory and meaning of performance metrics, but the reality is getting this right creates incentive for people to come to work and means that everyone is pulling in the same direction. One leader of an epic company had this to say:
​

Here’s the thing: Only an epic analytics team can make this possible!!!
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As the Information Age gathers momentum, the speed at which business is conducted, combined with the nature of decision points being tracked results in enormous quantities of data pouring into information systems. All this data has to be analysed, considered, tested and turned into information - and getting this right is a combination of art, science and a touch of intuitive magic.
An epic analytics team gets this. They get the business, get the leaders intent and know how to take this river of data and transform it into a stream of useful information. Working with the IM team, they will build an analytics capability which will provide the kind of metrics most people can only dream about. All of sudden a business transforms into being data driven, changing everything!!!

​Epic capital allocation
Within the world of value investing, championed by the legendary Warren Buffett, one of the greatest ways to create value for business owners (shareholders) is through effective capital allocation. 

Capital allocation refers to the manner in which capital is invested on behalf of a company to build margins. The margins part is critical, because capital allocation is not just about increasing revenue - it can be as much about reducing costs or figuring out how improve processes as anything else. 

Epic analytics in this area is crucial to creating epic businesses. One of the most profit destroying issues within organisations are the hidden costs of doing business, and it takes epic data analysis to figure out where and what they are. Once these hidden costs have been discovered, capital can be invested to eliminate this waste, creating value for owners. This process can take months or years and uncover all sorts of challenging issues - but the result is transformative. Capital allocation becomes exponentially more effective, with the flow on effect of freeing up more and more capital for other projects.
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Epic employee motivation
Pretty much every leadership book ever has one point - motivating those around to achieve the goal. Epic CEO’s and founders are renowned for being able to motivate individuals and create motivating work spaces. 

It’s interesting then to note that there’s a huge amount of research which shows that employees are most motivated when there is a clear link between an organisations values and their own - known as value driven organisations. It's even more interesting to note that one of the factors which comes up when this is discussed is employees desires that their performance be tracked and results mapped out - for instance in this article 'Values Driven Performance: Seven Strategies for Delivering Profit With Principles'

So here’s the thing - linking values to outcomes requires understanding what indicators drive these values and which measures of success need to be tracked. Planning on positively impacting a community for the long run? Define positive. Show employees what that means. Want to create products which change the world for good? Show me what you need to achieve to get there, per person, per day.

These are the kind of questions epic data analysis provides answers too. And if this capability is missing, it’s going to be impossible to get there!

Epic analytics
And so it is that I reach the point of this post. Epic business requires epic data analytics. 

Data analytics forms the glue which binds organisations together to pull in one direction. It dives into the data and begins to tease apart the questions of why and how and who and when and what. It gets involved in an organisation and brings insight, understanding and resolution. Data analytics allows you and every person who works for you to understand the same story and reasons and work together to fix them - testing, assessing and adjusting each step of the way as you see the results unfold.

For this reason, I submit to you - reader, CEO, CFO, CIO that you need data analytics.

James

What is Predictive Analytics?
Predictive analytics is a pretty amazing field of data science and has the potential to revolutionise the way we make decisions in business, so in this post I’d like to see if I can clarify just what the field of predictive analytics really is.

At its core…
Imagine for a moment you’re sitting at home planning your Christmas shopping. You’ve got a list of all the people you’d like to buy presents, and probably assigned each of them a rough dollar amount you’d like to spend on each. In your mind you’ve probably started working backwards from Christmas Day and figured out roughly what dates/times/places/websites you’d like to visit in order to find the perfect gift and then thought about how long it’ll take for those gifts to be found/packaged/wrapped/delivered so that they are ready for the big day. Without realising it, you have been performing many of the functions of predictive analytics. 

Conceptually predictive analytics is precisely this: based on past experience, knowledge and insight, make predictions about a future event, including some kind of likelihood of this occurring.

As a passionate data scientist, it is my honour and privilege to work with some of the most intelligent, interesting and driven individuals on the planet. As a result of my recent efforts within an organisation I was offered postgraduate study at the University of New South Wales focusing on predictive analytics across a logistics supply chain. 
Yet despite the calibre of the individuals I work with and my general passion for this field of work, I remain often discouraged by a lack of understanding about the field of data analytics and the tremendous value it can add to companies. This blog is my personal effort to add my voice to this commentary. Hopefully I can be insightful and interesting, at times offering advice, training and direction to what is an incredibly interesting and diverse field. 

I'd like to start by laying out what I think data analytics is and why it's important.
What is data analytics? 
Data analytics is about taking mountains of data and analysing it to see what insights can be drawn gained through synthesis, in-depth knowledge and the art of extraction.
Synthesis. In a a modern logistics framework, there is literally hundreds of thousands of data points per item being recorded, everything from their GPS location, to check in times at key nodes. At a very basic level, this data is pretty useless and frankly annoying/overwhelming. I mean a piece of freight is going to arrive when it arrives right?
Synthesis is about taking these data points and starting to look at them in combination with lots of other disparate pieces of data to get insights. For instance, if your logistics network was global in nature, then you might want to look at normal weather patterns and understand the impact sunny days could have on your network and optimise around that. Or you might choose to look at your workforce and understand the impact substandard equipment has on your bottom line. Or you might want to look at the maintenance cycle of your assets and change it around to reduce total downtime. The point is that in each case, the insight was gained through combining different bits of information to produce a better understanding of the whole.
In-depth knowledge. Truly effective data analysis is a combination of synthesis combined with a thorough understanding of the subject being studied. For me this was shown in my MAGNUS project. The engineering firm I was working for had massive inefficiencies generated through a stove piped information environment, leading me to be confident in achieving a good result through simple synthesis. However, it was when I befriended one of the other engineers and was able to use his expertise in their engineering decision tracking tool that the savings ramped up into the millions of dollars. This was because it was in knowing and understanding the process that we were able to see the opportunity and make effective changes. 
The Art of Extraction. For me this is the true test of effective data analytics. The process of synthesis and in-depth knowledge can produce many amazing insights into what is happening in an organisation. But sometimes those insights are pretty useless. True data analytics is just as much about filtering out the noise as delivering insights to end users.

So Why is this Important?
Data analytics has the potential to really revolutionise many organisations. It's very nature is about fully understanding and quantifying the risks, rewards and opportunities inherent in a choice. As a result, when data analytics is employed correctly it provides deep insight into the impact of decisions, individuals and equipment on the desired outcome. 
A great recent example of this lies in Walmarts recent decision to continue investing in streamlining their trucks and delivery network. By analysing the routes they typically drove, they were able to increase the economy of their network by about 2% by reducing wind drag on their vehicle fleet. Furthermore, by identifying where the largest hold ups were in their network, they were able to redistribute deliveries and decrease overall down time. 
For many organisations this kind of insight can improve returns dramatically. It increases competitive advantage and decreases risk.