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5 posts from April 2015

30 April 2015

What do organisations need to know about Big Data

Why do we need Big Data Analytics?

Every organisation collects data, but very few are harnessing the most value from the information their data offers. Big data analytics is needed to create smarter and leaner organisations which can have cross channel conversations and prepare organisations for the future.

Big data analytics focuses on analysing existing data (from existing and new data sources) to find hidden patterns, unknown correlations, market trends, customer preferences and other business information. It enables data scientists to tap into unconventional business intelligence by performing predictive analysis, data mining, strategic analysis etc.

How can we use Big Data?

Big data can be used in many conventional and unconventional ways to help organisations find answers. The findings from big data can offer organisations insights to:

  • Strengthen marketing
  • Create new revenue streams
  • Improve the customer experience
  • Gain operational efficiency
  • Build competitive advantage

It can be tempting to just go out and buy big data analytics software, thinking it will be the answer to your organisation’s business needs. Technology and software on its own aren't sufficient to handle the task. Well-planned analytical processes and people with talent and skills are needed to leverage the technology and carry out effective big data analytics.

How can we succeed with Big Data Analytics?

Big data analytics can offer big rewards but it is also accompanied by big risks which need to be mitigated. Any given program’s success is driven by strong planning, setup and execution, and big data programs are no exception to this. Some of the key steps that can be taken to ensure success are:

  • Find business sponsors with solid business plans in mind. A business case should include a clear set of well-articulated business objectives with realistic timelines. This will help define a scope which is achievable and supported by technology.
  • Make learning (and mistakes) part of the project plan. Learning needs to be part of the project and not a separate outside activity, as it is often treated. The project needs to create project schedules and budgets based on a long learning curve, including the mistakes that will be made in the process of that learning.
  • Get innovative and agile on application development. Agile methodologies help keep the project visible, transparent and help build regular communications. The change management needs to be very tight in this methodology and the focus (end goal) should not move.
  • Set realistic expectations and manage them proactively. In organisations that are new to big data projects, lofty expectations can be set especially by technology vendors that claim big data tools are easy to use and manage. Every organisation has a different maturity level and grows at a different rate. This needs to be acknowledged and expectations set accordingly.

If you would like to discuss these issues, or the impact of emerging technology or data and analytics on your industry, then contact our Data & Analytics team.

20 April 2015

Really big projects - and the interesting questions they raise about business intelligence

PwC Data Analytics have an offering called Mission Control. It's a bit of a game-changer if you happen to be running a multi-billion pound project. We know this because our clients who run those programmes tell us. Traditionally, it is very hard to be confident that your massive, complex programme is going to deliver on time, to scope and budget. Even harder to be assured that it will deliver the benefits attached to the investment. The reasons for this are:

  • You probably need to integrate across many systems. From scheduling, cost, risk, health and safety, accreditation, performance to many others that will inevitably appear.
  • You probably have multiple versions of each of those applications; each contractor will have their own version and data structures.
  • You probably have to integrate across functions, processes and work packages e.g. you want cost by department but also by the cost of that sub-system that you need to do a variance report on.

And then, just when you were about to think you could handle it, the project moves from design to build and many of the requirements change! No wonder many organisations give up and muddle through on gut instinct and 'human middleware'. This raises some interesting parallels in how we give ourselves the best chance of success in BI projects.

  • Why don't we think of BI as something that needs an end-to-end, horizontal solution/ architecture? The ability to deal with breadth and variety is key. If you don't think so, imagine the taxpayer is your end customer for the 10 years that you might be running this huge cost centre.
  • Why don't we prototype our way to the right metrics and data model? Rapid prototyping embeds the experience of the very rare and valuable senior staff in the information structures and visuals. A vendor might offer an off the shelf model but do they know as much as the leaders that have been to chosen for this endeavour?
  • Why don't we spend more time de-cluttering the information landscape. Everything that is hardwired in our applications has a cost to be undone. Before we build new information structures in our core systems have we put a cost for 'technology debt' into the business case?

If you would like to know more about Mission Control and how it can give a new level of control to leaders of the biggest projects and portfolios then please get in touch.

If you would like to discuss these issues, or the impact of emerging technology or data and analytics on your industry, then contact our Data & Analytics team.

14 April 2015

Artificial intelligence and robotics: The end of the world (as we know it)?

by Mark Spain Senior Associate

There have been a lot of developments in robotics in recent years, with a focus on artificial intelligence and machine learning. More recently, there have been several stories about practical uses of robots with ‘artificial intelligence’ in everyday life, from simple things such as delivering packages in a specific area, to serving customers in one of Japan’s largest banks. So, I wanted to write an article to discuss these topics and pull together a few ideas of what more we can expect from future developments in this field, and whether these developments will ultimately be a good thing.

Many of you will have heard of Amazon’s trial run of using drones to deliver packages that had been ordered online, back in 2013. Drones were programmed with a specific route and are supposed to be able to traverse to their end destination, and then return back to their starting position. This is an idea with quite a simple concept, but one that has huge practical applications and many of the world’s largest delivery companies are anxiously awaiting completion of successful delivery drones. Amazon is still testing its drones here in the UK, but a recent announcement by the Federal Aviation Administration has made delivery drones active in US air space a much more difficult achievement.

Have you ever been stuck in a long queue at the bank? If you have, now you’ll wonder if it’s because the robot serving all the customers is re-booting – yep, robots serving customers! Mitsubishi UFJ Financial
Group, Japan’s biggest bank, is employing a robot called “Nao” to assist in serving its customers. Nao has a camera on his forehead that allows him to see his customers and recognise the tone of their voice – he can then interact with his customers, greeting them and offering the bank’s services to them. He has a great knowledge of finance and can speak 19 languages. Depending on his performance, more robotic staff will be employed with the hopes that they can perform tasks that human employees can’t, such as helping when there is an increase in foreign customers and working 24 hour days.

While you could argue the convenience granted by the examples above is great, my next example is actually helpful. In Australia, robots are being developed to assist in farming. Yield loss is a real issue, and competition for minerals and nutrients by plants means that most crops suffer. The plan to introduce robots to farms should revolutionise the farming industry and increase the yield of crops, which is beneficial not only to farmers, but to all consumers.

So it seems that everybody wants to make robots. I’m not sure if it is due to watching Robot Wars too much growing up and thinking making robots is cool, or that people can see the benefits of having machines that can operate and perform tasks autonomously. One thing is for sure, currently these are all just pre-programmed machines and do not truly have artificial intelligence. In fact, I watched a film recently, called “ex-machina”. It is about artificial intelligence in robots and how a machine cannot possibly possess artificial intelligence unless it can pass the Turing test. However, in this film, a spin is put on the standard Turing test and a third party must have a conversation with a machine, knowing that it is a machine, and yet still be able to believe it can ‘think’ and process its own answers to his questions. Now back to reality. Developing a machine that can think and act for itself, as well as being indistinguishable from a human, is a long way off. The closest we are to achieve this currently is to go through a process of machine-learning. This process requires a machine to mass calculate models using example data so that it can learn from the answers it obtains – it should then be able to make informed decisions or predictions about other things, based on the knowledge it has from the data it has already seen. That is a very high level description, but more can be read here. IBM’s Watson computer is probably the pinnacle of modern machine learning and is able to understand natural language and generate hypotheses to answer questions based on evidence obtained from unstructured data. The learning works quite similarly to how a human would learn – Watson can guess, and will learn from mistakes and feedback it receives – it learns dynamically, getting smarter with repeated use and learning as it goes.

The developments in these areas are amazing. Worldwide economies will boom as robotic workers become more of a norm and gain an increased knowledge of finance and as robotic farming creates further efficiencies in farming and food shortages are a thing of the past, we will laugh and be care-free as we slowly gain weight and begin to live a life floating along on our hover chairs, looking like the humans on “Wall-e”.

But then you have to ask yourself – what happens when the machines learn? When the machines become capable of thinking for themselves and realise that we sit there incapable of doing anything for ourselves because years of having tasks automated for us has meant we no longer possess the skills or knowledge required. Will they take advantage of our weakness? Will the machines, possessing advanced financial knowledge as well as control of the supply of food and its delivery, decide to extort us so that they can further prosper from their resources? Or will they rise up in anger at how we have treated them as tools to make our lives easier, and decide to revolt and slay us all like in Terminator? Which poses the final question – which one of our leading technology firms is SkyNet, and how do we stop them?

If you would like to discuss these issues, or the impact of emerging technology on your industry, then please get in touch with Euan Cameron.

by Mark Spain Senior Associate

10 April 2015

Smarter, happier, healthier workforces

by Anthony Bruce Pharmaceutical and Life Sciences Leader

With excitement mounting over the arrival in stores of the Apple Watch I’m not busy thinking about which style I’m going to buy (well maybe a little), but rather how Smartwatches and other wearable technology devices have the potential to benefit organisations and their workforces.

We conducted some research recently which showed that four in ten people would use a piece of wearable technology provided by their employer even if it means giving them access to the personal data collected via the device.  The take up rises to over half (56%) if the information collected is used by employers to improve things such as working hours, stress levels and where they can work from.

Some workers still have an understandable “big brother” reaction to sharing any of their personal data with their employers, but our research shows that the majority of people can be persuaded if they can see clear personal or workplace benefits.

For organisations with a younger workforce, the opportunities seem greater as only two in ten millennial workers told us they wouldn’t be willing to share data from a wearable device in exchange for work benefits.  And if the data is anonymised and shared at an aggregate level, rather than being personalised, the willingness was greater still.

So the technology exists and the appetite is there amongst groups of employees, which means we need to think creatively about how leaders of organisations can leverage wearables.  Can the data available help us to keep our workforces happy, can it identify health risks, can it make our organisations more efficient or more productive?

Providing employees with wearable devices could be a novel way for organisations to get a better understanding of their workforce, but they could also be a powerful tool to affect working patterns, benefits packages, resource planning and much more.

The key to success for both organisations and their employees will be getting the deal spot on and overcoming the trust barriers by setting clear rules about how the data is acquired, used and shared.

If you would like to discuss these issues, or the impact of emerging technology or data and analytics on your industry, then contact our Data & Analytics team.

by Anthony Bruce Pharmaceutical and Life Sciences Leader

06 April 2015

How Safe is Your Walk?

Use of navigation tools, such as Google Maps to ensure you reach your destination swiftly is a familiar concept; however do you ever pause to consider the safety of the route that is suggested?

When you are familiar with an area, you may choose an alternative route to avoid ‘rough’ areas. However, when you are not familiar with the area, this is not conceivable – yet. Researchers at Boston University and the University of Pittsburgh are designing a method to direct people on a choice of routes, often with a trade-off between safety and speed via the use of ‘SafePath’ algorithms. This research is focussed on Philadelphia and Chicago as those cities have their crime data openly available.

The concept of a safer route to your destination is not new; the SketchFactor app relies on crime data along with flags which users have posted. However, the flags can indicate an opinion of how ‘sketchy’ an area is which is rather subjective to say the least!

The ‘SafePath’ algorithm is much more objective regarding risk; it uses openly available crime data and considers time, location, and importantly, the type of crime. The crime data allocates relative risk ratings to be given to each location and hence route. It discounts the routes which are longer than the deemed safest path or less safe than the shortest path. This leaves a few of the options in between to choose from, ultimately the decision you make from these will depend on your risk appetite. At this stage it may be a while before this turns into something available for public consumption; however it is easy to see how this could be built into existing navigation tools.

In the UK, our crime statistics are also openly available, so as cities make more data available and researchers figure out new ways of analysing it, we will have more ways of improving our lives. For example Yahoo!lab have looked into sending people on the most scenic route! Its potential doesn't stop there. Understanding the effect of traffic patterns on local businesses could help a city plan major construction to minimise economic impacts. In addition, simulations of natural disasters, created from real-life data of people’s movements, could in the future help design better evacuation routes.

If you would like to discuss these issues, or the impact of emerging technology or data and analytics on your industry, then contact our Data & Analytics team.