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The term Big Data is commonly used to describe the ever increasing abundance of information being gathered, stored, and analysed in todays world. Whilst this explanation provides some insight into the meaning behind the popular buzzword, Big Data is truly defined by its characteristics, and what stands it apart from historic data analytics.

DEFINING BIG DATA We often associate the 4 Vs with Big Data, although more accurately this is really the 5 Vs, as we shall see.

Volume refers to the vast quantity of data now being accumulated each second, and stored for future use. It has been calculated that the volume of data created since the beginning of time, up until 2008, will soon be generated every minute. This means Zettabytes of data. A study at the beginning of 2014 noted that we are now accumulating data at the rate of 2.4GB per second.

This is hard to comprehend, but a good visualisation is to imagine a truck turning up at your house every hour and unloading 8640 1TB external backup drives into your home. This reoccurs each hour, with none removed, and this allows some understanding of the volume of data we refer to.

Velocity is the speed at which this data is created, mined and moved around. Think social media, and in-memory analytics for rapid interrogation and recall of information for strategic benefit.

Variety is what differentiates this data mining from standard analytics. We are now not only storing text, but images, video, and all manner of unstructured data. This data cannot be indexed through standard methods and common database practice. A new approach to index and recall is required to handle the unstructured data.

Currently the volume split of data being created each day is thought to equate to around 20% structured, or classic data we are used to dealing with in tables, and

80% unstructured - images, video, social commentary, etc.

Veracity is the integrity of the data stored, and how it is used. A key point to keep in mind when analysing data is that social content is most often opinion and conjecture, not hard unequivocal facts.

Value is the 5th V. Whenever we perform data analytics, the key driver must be to generate, add, or present value in what we are doing.

AUTOMOTIVE: VEHICLE ANALYTICS The automotive sector is one where the implementation of sophisticated analytics was almost inevitable. Whilst engineering practices have advanced, and the technology within our cars has been steadily improving, there have been no major advances over the past 100 years when it comes to maintenance of the vehicle.

For the most part this still operates on a system where a driver takes their car to the dealer when there is a suspected problem. The dealer analyses the car, determines the problem, and then fixes it. A computer now does a lot of analysis, but it is still dependent on a system where a fault must first occur.

With technology advancing at the paces we are now seeing, this is no longer good enough we as consumers demand more.

Automotive Analytics Driving in the Cloud

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VEHICLE DATA MONITORING Sensors are everywhere, and we encounter them all the time, but are now so used to them that they blend into our everyday life without direct impact or interference. They monitor doors to allow automatic opening, they measure and control air temperature, they save us energy through dimming and switching off lighting when there are no occupants in a room. Sensors are already embedded into our lives.

Within a typical modern car, there are hundreds of sensors, and within some of the more advanced cars the sensors total thousands, on par with that of early Boeing 737 aircraft.

What we must remember is that these sensors are, for the most part, generating structured data. That is prescribed information based upon a known state or expected or permitted outcome. For example a sensor monitoring the health of a headlight bulb will know that it is reporting whether the bulb is working or not. If it is not working, it cannot report that the reason is because a stone came through the glass and took out the bulb.

If you think of most components of a car, and use physics to boil things down to their most base principles, the sensors are all reporting known potential outcomes. This is even true of systems that we are in awe of on recent luxury cars such as variable speed based on traffic avoidance.

The sensors in this scenario are reporting current speed, distance between vehicles, and closure rate, to make corrections to the speed of our vehicle and maintain the safe gap.

When different sensor data is brought together, we achieve swarm intelligence, as together they help to interpret scenarios and events being experienced by the vehicle and its occupants in order to understand causality and resultant impact.

So how can this data and the many thousands of sensors advance the motoring industry, and how can the consumers experience in this sector evolve into what is expected in a modern technology focussed world?

We can now store this incredible volume of data, we can harness it through advanced analytics, and when combined with the evolution of telecommunications, 3G, 4G, 5G and so forth, we have a way of letting others access the data.

This breaks the cycle of user reporting problem and opens the doors to proactive intervention.

PRE-EMPTIVE MAINTENANCE With sensors collecting data on the wear status of crucial components of the car such as brake pads, discs, fuel filters, we can now proactively monitor the status of the vehicle. This data is fed back to the dealership where steps can be taken to notify the owner should wear reach the point that mandates some action be taken.

Taking this to the next level, algorithms can monitor patterns across vehicles to identify potential issues on a range, or model, before actual problems present themselves. This is where we truly start to see the power of data analytics. Engineers can analyse how components wear and interact when placed in real driving conditions.

Currently manufacturers perform thousands of miles of testing in artificial environments, and on test tracks, but nothing can reflect the true value of real world road experience spanning all geographic locations within which these cars are to be sold.

The knowledge the manufacturers can build based on these data feeds is extraordinary, and starting to become a reality now. Several vendors are getting involved in this space, including BMW, Audi, and Toyota.

The interesting wild card in the group is Tesla. With their all-electric cars they have certain advantages and an edge over their combustion engine counterparts. They are already starting to capitalise on this through proactive overnight maintenance of their vehicles at peoples homes.

To give an example, where the engineers are receiving data streams about handling and motor response, they are able to gather this data, simulate changes, and then if deemed necessary, push a patch to the

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vehicles. Because the vehicles are connected by Wi-Fi to our homes, subject to agreement, this means patches to fix and improve our cars handling, performance, and economy, can be downloaded to the vehicle overnight.

This is utilising Big Data analytics to improve a product that is already in use, and is a good example of what the market is referring to as Innofusion, or the melding of different innovative technologies and scientific disciplines to harmoniously improve a process, action or known experience.

But maintenance is not the only aspect of the automotive industry to benefit from Big Data, as other areas start to realise the power of the consumer crowd, and their voices in several areas of the industry.

CONSUMER LED DESIGN This is an area where we are seeing an explosion through sites such as Kickstarter and Indiegogo, with the crowd dictating the design or end product. Coupled with the power of data analytics, trawling unstructured data across forums and social media sites, you can begin to create consumer targeted manufacturing.

Audi have been doing this for some time now, and started with crowd sourced input into the design of their audio systems within their cars. When they update the in-car entertainment strategy, they look to the forums and invite input into what is important to the drivers of their cars when it comes to audio entertainment. Audi have successfully done this now for several generations of audio equipment.

But it doesnt end there. Many manufacturing industries are constantly scouring social commentary and major forums for information and views on their products, and the automotive sector is starting to leverage this power. The idea of harnessing the power of collective design is a very real and powerful marketing and design tool, often heralded as The Crowd as the designer or Crowd as the architect. Whilst this may not be 100% accurate, and the degree of input taken in can vary from company to company, any manufacturer of consumer based devices and products that does not tap into this pool of ideas is orchestrating their own demise.

So with Big Data contributing to not only the design of our future vehicles, but the pre-emptive maintenance of tomorrows cars, we are beginning to see a more

seamless integration of services across the present automotive sector - a synergy across all the required independent building blocks that make up our daily experience as car owners and drivers.

INTEGRATED SERVICES With the dealer networks now able to keep a closer eye on the cars operations, and pre-empt potential problems or risks, closer ties can be made to recovery operations, insurance companies, and other important participants in the car ownership chain. We are moving closer to the point where recovery trucks could be en-route to your predicted breakdown location before the breakdown occurs.

This sounds a little bit too much like the film Minority Report, but why not? Imagine a time when a red light flashes on your dashboard, the engine stops and you pull over on a country road. Within a minute you receive a call to inform you that all is in hand, and a recovery truck is already en-route with a firm ETA. Back at the dealership, they have already run diagnostics on the data, identified the failed components, and an order for the parts is in progress.

You are picked up by the truck, and taken home. Meanwhile the dealer contacts you to confirm that your car is en-route to the dealer, and will be ready for collection by a provided data.

This sounds great, if not a little sci-fi, and potentially a little big brother, but this is nearer to a reality than you may think. Several components of this vision are already in place, with manufacturers beginning to look at partnerships and closer ties with insurance companies and repair organisations, to steadily move us towards a more seamless ownership chain.

DATA SECURITY With all of this data being stored and recalled for analysis, there must be assurances of anonymity for the users providing this data. The crucial importance of lessons learned so far cannot be forgotten.

How this anonymity is maintained and adhered to by the corporations presents several dilemmas from commitment to enforcement. This is an area of data gathering and analytics that needs serious discussion, as current data protections legislation is not fit for our rapidly accelerating and globally connected world.

Big Data is an important part of our evolution, but care is needed to ensure that data analytics does not betray our privacy for corporate gains.