One of the biggest misconceptions in business is believing your data is accurate and useful for basing decisions on.

In reality you are looking at a set which has been collated from a range of inputs that have been collected together.

The 'data' is never right or wrong – it is the set that you have assembled (or that someone else has assembled for you) and it is only as good as the assembly process.

Set theory was first developed by Geog Cantor in 1874 but languished without a practical application. The first such application was provided by Ted Codd at the IBM Research lab in 1970 with his seminal paper that described the rules for how relational databases would work.

Some far-sighted educationalists started introducing set theory into the math curriculum as early as 1967, which was controversial at the time with most parents failing to understand how important and pervasive set theory would become.

There are many things that can go wrong when joining data components together. Having a healthy database will ensure fully connected data will allow for the creation of robust sets. The content of the set is controlled by filtering, the assumptions of the collator, and the presence of conditional data - did the person constructing understand all the nuances hidden in the data?

Data can become unhealthy in many ways for example where data entry forms allow ambiguous data entry or allow data that should be stored in one field to be stored in another. Does your system allow records to be saved with incomplete data? How often do your business processes and incentives discourage front line staff from entering all the data that you will need to construct robust sets for reporting and analysis?

Do your report writers and designers ever calculate what the final set should look like before constructing their sets and then compare actual vs predicted, or do they just build a report and hope that it's 'right'?

Does your system store spatial data in a poor relational schema which encourages unhealthy set building? How valid are the datasets that you are relying on? Data only makes it through if it is fully connected - how much of your data is simply hidden from you when you are making your decisions?

There is plenty that can go wrong when compiling a set of data even when the data is in a healthy condition. Much more can and will go wrong when it is not. To make sound decisions for your business you need to have healthy data.

What processes do you have in place to understand the health of your data? Chances are, if you haven't got these processes in place already, then it's not going to happen without outside help. You need to be able to quantify the health of the data in your system. You need to be able to identify problem data areas so that preventative strategies can be put in place. This stops the problem growing. You then have the option of rectifying existing problems should you choose, prioritising on data that affects critical decisions and processes.

Finally you need to be able to measure whether the health of your data is improving or deteriorating over time. By being able to link unhealthy data to the actual costs, losses and poor KPI performance that the business is exposed to, you can demonstrate a clear business case to take positive action.

Which should you choose?, ...when?, ...and why?

'No-one ever got fired for buying IBM'. A perennial risk-management mantra. The other side of this coin is the implication that you'll be obtaining the very best mediocrity that money can buy. You'll be pleasantly surprised if you receive excellence but you're not really expecting it.

There are Pro's and Con's – and the choice depends on the fit you need.

When you buy big, you're basing your purchase on reputation. You are tapping into an organization with large resources that will be able to deliver. You are engaging an organization that can offer you a service that they have done many times before, and they know just what to do to 'crank the handle' to churn it out. The level of comfort this provides is certainty.

When you buy boutique, you're going to be engaging a specialist. Someone with a penchant for a particular technology, and/or someone with significant experience in a single vertical market segment. You're engaging someone with more intensive knowledge in who is going to produce the right results, quickly and effectively. They are not going to apply the one size fits all solution to your business problems.

More to the point they are a specialist. With the larger corporates you are likely to have the actual work undertaken by a more junior person. The senior partners are more often doing the sales work, engaging the client and overseeing the account rather than using their extensive expertise to deliver the results. The extra distance between the specialist and the coal-face means that insights that they might have detected may be obscured through the inexperience of the practitioner doing the work combined with the routinized approach to the analysis.

The boutique specialist will likely understand your industry already and you aren't going to be paying the consultant top hourly rates for you to educate them! They will know what questions to ask, and they come with a point of view. A boutique consultant should tell the client exactly what the problem is, without sugar-coating, or worrying about the egos of the people involved. You're paying to have the problem fixed, not massaged out of view.

Where a consultant discovers that part of the problem domain is outside of their expertise, a good consultant will engage others to augment the knowledge gap. The difference here is that a boutique consultant will engage another specialist whereas a corporate entity is more likely to look in-house for those skills and find the closest match.

People engage consultants for a variety of reasons, but the most common are:

• They lack the expertise to undertake the work
• They lack the resources to do the work
• They require external assistance to make a tough decision

Before you engage a consultant, whether that be big or boutique:

• Decide whether you are committed to fixing the problem
• Don't set overtight deadlines if you don't really need them

If you know the answer you want already, but you want to instruct an 'independent' company to make the recommendation, then buy big. No one is going to argue with their reputation, but more importantly boutique companies aren't there to rubber stamp solutions, particularly if the solution doesn't match their point of view. Boutique companies stand and fall on their integrity and they are unlikely to take you on as a customer.

Often companies seeking the services of consultants set very tight timeframes, both to respond to proposals and to undertake the work. The tight timeframes are usually related to meeting internal KPIs only. Big consultancies are geared up to meet these tight timeframes and boutique ones will struggle for such a timely response. The reality is, that once a recommendation is delivered the customer rarely acts on it promptly. Setting tight timeframes might tick your boxes and keep your boss happy, but if your goal is to get to the root of the problem and generate custom solutions in areas where specialist expertise is an essential component, then give yourself and your preferred consultant time to generate the best outcomes for you.

The race for self-driving cars began in the 1920’s. At least that’s what Wikipedia leads us to believe with its broken links and citations. But it is likely to be true on some level. Autonomous cars are separated into 5 classifications from level 0 - issuing warnings, like a reversing proximity beep, to level 5, no human interaction except setting the destination and starting the system. We have been aware of the approach of the class 3-4 self-driving car for half a decade, but it seems that the time is nigh for those in the driver’s seat to get a product to market. End of bad car puns. 

Some manufacturers like GM are looking to have their automated cars on the road and fully functional by 2016 but they will only be available to hapless employees willing to put their lives on the line for their employer. Some companies are focusing on a 2018 for release of their cars, while most are pushing this out to 2020 and even 2030. Perhaps there is some reluctance to be the first company to release the tech for fear of being the industry scapegoat. No-one wants to see their brand included in the first autonomous death headline regardless of who’s at fault.

Good for the environment bad for the wallet

Shared Autonomous Vehicles (SAV’s) for carpooling are some of the driverless vehicles set to replace conventional cars. At just 5% penetration it is estimated that one SAV would replace 11 cars on the road. To put this into layman terms, if 200,000 SAV’s existed in NZ the rate of car ownership could drop from 3,100,000 to 900,000. This might be generous at best however the environmental implications are positive nonetheless. Unfortunately the outlook for public and commercial transportation is bleak, especially if you are a driver. The demand for tech personnel is almost certainly going to increase, but it will be disproportionate to jobs lost. In a fashion similar to automated checkouts, the number of staff needed to service, upgrade and manage these units will be significantly less than the number of people displaced by the technology.

If the demand for cars and ownership drops, then the cost to produce them would certainly increase. So too might the cost of servicing vehicles as the number of privately owned vehicles declines. So how is the automotive industry going to prevent self-cannibalisation? While there is a lot of talk of SAV’s and driverless cars replacing driven ones, the truth is that the economic model suggests that they will only be competitive at best. And by 2032 it is expected that 50% of new cars, not all cars, will be autonomous. So there is still a long future ahead for current conventional cars, drivers and oil.

Another factor that will reduce uptake of these vehicles is cost. One source talks about the cost of the self-driving Prius from the YouTube advert being greater than the price of a Ferrari 599. Why? A Prius is $24,000 USD, add LIDAR, sensors and a GPS array at roughly $75,000, $10,000 and $200,000 respectively and you begin to understand how limited the first round buyers’ market might be. Although a quick google shows that these costs are reducing, with some GPS arrays starting at only $8,000, once you add on the cost of development you begin to understand that self-driving cars may be out of the question for many of us in the foreseeable future. It might also be the primary reason this tech is taking so long to reach the market.

NZ’s View

Realising that autonomous cars will arrive somewhat soon, the NZ government quickly noted that law in NZ has not been written with these brain-less chaperones in mind. What this means is a change in legislation around the requirements for autonomous vehicles and the rules which govern them. Ministry staff have already been sold hosted by Nissan and Google and shown their driverless vehicles and the Ministry of Transport has basically said, perhaps more eloquently, ‘NZ is the perfect test ground for driverless cars, send the NZTA an email if you wish to test drive yours’. While changes to legislation are currently only occurring for testing driverless cars, we can still expect a change in the law surrounding the ownership, importation and use of autonomous cars soon.

Implications for GIS

As the government does what it does best and gets buried in paperwork, hardware and software are being developed within the driverless vehicle realm that embrace geospatial technologies. It is the development of this GIS-ware that could help continue the industry growth that has been occurring and predicted to continue for some time.

Routing efficiency, traffic management, electric energy and other technological breakthroughs will be the source points for innovation and competitiveness for the driverless car market. Infrastructural changes will also be determined by the adoption of these new technologies. And the vast volumes of spatial data being collected by these new mobile sources will provide more sources for collecting big-data. Sorry for using the b-word. Google’s self-driving car fleet has racked up an impressive number of automated miles (700,000 by 2014), during which it has been collecting vast amounts of data. As more autonomous cars hit the road more data will be captured, perhaps simply to keep google street view updated, but probably for so much more.

As well as innovation within the geospatial-driverless car sector, we can expect these breakthroughs to transfer directly into other GIS related industries. Development of cheaper, more reliable, smarter, source and sensor technology will have an effect on the number of business who can enter the geospatial market and the types of analysis that can be performed. And continual growth of the industry will ensure that new business's find niche untouched positions. 

- Cody Kinzett

The race for self-driving cars began in the 1920’s. At least that’s what Wikipedia leads us to believe with its broken links and citations. But it is likely to be true on some level. Autonomous cars are separated into 5 classifications from level 0 - issuing warnings, like a reversing proximity beep, to level 5, no human interaction except setting the destination and starting the system. We have been aware of the approach of the class 3-4 self-driving car for half a decade, but it seems that the time is nigh for those in the driver’s seat to get a product to market. End of bad car puns. 

Some manufacturers like GM are looking to have their automated cars on the road and fully functional by 2016 but they will only be available to hapless employees willing to put their lives on the line for their employer. Some companies are focusing on a 2018 for release of their cars, while most are pushing this out to 2020 and even 2030. Perhaps there is some reluctance to be the first company to release the tech for fear of being the industry scapegoat. No-one wants to see their brand included in the first autonomous death headline regardless of who’s at fault.

Good for the environment bad for the wallet

Shared Autonomous Vehicles (SAV’s) for carpooling are some of the driverless vehicles set to replace conventional cars. At just 5% penetration it is estimated that one SAV would replace 11 cars on the road. To put this into layman terms, if 200,000 SAV’s existed in NZ the rate of car ownership could drop from 3,100,000 to 900,000. This might be generous at best however the environmental implications are positive nonetheless. Unfortunately the outlook for public and commercial transportation is bleak, especially if you are a driver. The demand for tech personnel is almost certainly going to increase, but it will be disproportionate to jobs lost. In a fashion similar to automated checkouts, the number of staff needed to service, upgrade and manage these units will be significantly less than the number of people displaced by the technology.

If the demand for cars and ownership drops, then the cost to produce them would certainly increase. So too might the cost of servicing vehicles as the number of privately owned vehicles declines. So how is the automotive industry going to prevent self-cannibalisation? While there is a lot of talk of SAV’s and driverless cars replacing driven ones, the truth is that the economic model suggests that they will only be competitive at best. And by 2032 it is expected that 50% of new cars, not all cars, will be autonomous. So there is still a long future ahead for current conventional cars, drivers and oil.

Another factor that will reduce uptake of these vehicles is cost. One source talks about the cost of the self-driving Prius from the YouTube advert being greater than the price of a Ferrari 599. Why? A Prius is $24,000 USD, add LIDAR, sensors and a GPS array at roughly $75,000, $10,000 and $200,000 respectively and you begin to understand how limited the first round buyers’ market might be. Although a quick google shows that these costs are reducing, with some GPS arrays starting at only $8,000, once you add on the cost of development you begin to understand that self-driving cars may be out of the question for many of us in the foreseeable future. It might also be the primary reason this tech is taking so long to reach the market.

NZ’s View

Realising that autonomous cars will arrive somewhat soon, the NZ government quickly noted that law in NZ has not been written with these brain-less chaperones in mind. What this means is a change in legislation around the requirements for autonomous vehicles and the rules which govern them. Ministry staff have already been sold hosted by Nissan and Google and shown their driverless vehicles and the Ministry of Transport has basically said, perhaps more eloquently, ‘NZ is the perfect test ground for driverless cars, send the NZTA an email if you wish to test drive yours’. While changes to legislation are currently only occurring for testing driverless cars, we can still expect a change in the law surrounding the ownership, importation and use of autonomous cars soon.

Implications for GIS

As the government does what it does best and gets buried in paperwork, hardware and software are being developed within the driverless vehicle realm that embrace geospatial technologies. It is the development of this GIS-ware that could help continue the industry growth that has been occurring and predicted to continue for some time.

Routing efficiency, traffic management, electric energy and other technological breakthroughs will be the source points for innovation and competitiveness for the driverless car market. Infrastructural changes will also be determined by the adoption of these new technologies. And the vast volumes of spatial data being collected by these new mobile sources will provide more sources for collecting big-data. Sorry for using the b-word. Google’s self-driving car fleet has racked up an impressive number of automated miles (700,000 by 2014), during which it has been collecting vast amounts of data. As more autonomous cars hit the road more data will be captured, perhaps simply to keep google street view updated, but probably for so much more.

As well as innovation within the geospatial-driverless car sector, we can expect these breakthroughs to transfer directly into other GIS related industries. Development of cheaper, more reliable, smarter, source and sensor technology will have an effect on the number of business who can enter the geospatial market and the types of analysis that can be performed. And continual growth of the industry will ensure that new business's find niche untouched positions. 

- Cody Kinzett