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Trans-sector strategies – an empirical approach.

New goals and processes needed for a smart society

As the discussion continues about the need for intelligent networks and ‘smarts’ in virtually everything it becomes obvious that we must move away from the decision-making processes that have brought us to the point of financial crisis, environmental crisis and to the monopolistic and dogmatic regimes that have developed in the telecoms sector.

Around the world debates are heating up in the search for new and better ways to find solutions for these crises. There is more or less universal agreement that a linear continuation of the past will lead to more problems and, eventually, utter chaos and destruction.

Economic and social multiplier effects

Concentrating here on telecoms, both in the USA and Australia the telecoms debate has begun to move towards a trans-sector approach. FttH infrastructure investments (telecoms and smart grids) should be deployed in such a way as to create a social and economic multiplier effect for a whole range of sectors that use it independently of each other (open networks) for a new range of applications in healthcare, education, energy, water, transport and community services, as well as for entertainment and high-speed Internet access.

This is a radical change from the current dogmatic approach, which assumes that if we leave telecoms to the vertically-integrated (semi-) monopolies this multiplier effect will magically happen, in accordance with free-market economics.

For most people it is clear that to receive these trans-sector benefits – which are measured not just in literally hundreds of billions of dollars but, far more importantly, in very significant social, environmental and life-style benefits – we need to be far smarter than we have been during the time leading up to the disastrous situations mentioned above.

The transition to this new regime could take 10-15 years or longer, but at the end of that time we should be moving towards the smart communities, smart cities, smart buildings and similar smart concepts that are presently under discussion.

Why did we get it so wrong in the first place?

Given that wrong decisions have obviously been made in the past it is important to identify how this has happened. It would seem that either we didn’t input the right data in the first place, or that we developed the wrong set of goals. BuddeComm believes that the latter is most likely the case.

There have been many indications that the far right schools of economics have a great deal to answer for. These people exert enormous influence, in the USA in particular. At the same time our overly legalistic approaches – especially in telecoms – are another possible indication of where our decision-making processes went off the rails.

With the telecoms industry at a crossroads worldwide we now have a unique, once in a lifetime, opportunity to get the goals and processes right.

Massive new investments are needed to move the industry forward. Australian and America have both launched a broadband stimulus package. There are obvious differences because of size, scale, etc but in this analysis we will concentrate on the high-level policy differences.

Differences between Australian and American FttH approaches

Australia’s decision to leave their ‘broadband backwater’ was based, in part, on an understanding of the trans-sector value of near-universal connectivity. Similarly, in now assessing the benefits of this decision an empirical, trans-sector approach to measurement is the only one likely to yield the breadth and depth of knowledge needed to maximize the value of the investment. Before going into social and economic measurements (and into writing a business model) Australia has set a clear goal with a clear set of testable hypotheses – perhaps at this stage not all that well-defined, but at least with clear political backing.

A worrying element is that the US approach (as evident in the FCC FttH research study) is not structured around a similar set of clear goals. The possible goals being explored in the USA are ones upon which Australia has already agreed, in broad terms at least. The Australian government has made a political decision that a national FttH network is in the country’s interest; it aims to provide trans-sector benefits and it has therefore made the trans-sector goal central to its plans.

Australia can now go straight into a process that measures these trans-sector benefits as critical components of the FttH plan – it doesn’t first have to make an argument for this. So all the data that needs to be collected for both the social models and the business models for this infrastructure investment will be trans-sectoral from the outset.

The USA, on the other hand, is still taking a rather ‘old school’ theoretical approach towards its FttH plan. While the Obama Team in its early days indicated its support for a trans-sectoral approach it now appears that a case must first be built to support the need for the infrastructure to deliver trans-sector benefits.

This half-hearted attitude will give the old school lawyers and economists all the room in the world to continue their linear approaches to the old telecoms environment, which are driven, not by national or broader social and economic interests, but solely by the desire to maximize their own profits. And it is obvious that the current vertically-integrated telcos and cablecos have a vested interest in maintaining their own lucrative status quo.

Such a debate – which, as we know, will be mainly driven by lawyers – has been closed off by the Australian government and, that being the case, the incumbents in Australia are taking a totally different approach. They (both telcos and cablecos) have concluded that a trans-sector approach – based on an open network policy – will lead to a bigger telecoms pie overall, which will offer more opportunities. And so they are now, for simple economic reasons, putting their support behind a trans-sector approach.

This would never have happened if the government had not set a clear goal.

Trans-sector requires intelligent approach towards measurement

Nevertheless, despite the differences in approach, an empirical methodology around the collection, measurement and interpretation of socio-geographic data is needed in both Australia and the USA to support their FttH processes.

Once the decision has been made that FttH infrastructure is in the national interest it becomes much easier to change the rather inflexible and silo-based measurement and decision-making procedures of the past into intelligent (smart) ones, aimed at creating a new system that is viable from both a social and economic perspective.

The problem we see in the USA is that a lack of a clear (policy) goal makes it difficult to ask the right questions, and to know what to measure. It will also be rather difficult to compare the various submissions, which will be written according to completely different (opposing) goals depending on the interpretations and the positions the submission author represents (eg, national interests vs. vested interests).

Once the high-level policy goals (hypotheses) are defined, what needs to be tested becomes evident. Without those clear hypotheses the door is wide open for an undermining of the actual process and its outcomes. Submissions that are based on unambiguous hypotheses will be more constructive in nature and will provide far more valuable and credible information that can be used to measure the right models needed to move forward.

This makes it possible to take a far more granular trans-sectoral approach to the actual socio-economic measurements that need to be taken, which then makes it possible to establish detailed correlations between these sectors. When this is added to knowledge-based systems intelligent predictions can be made about the requirements of services within certain areas, and, in fact, even on a personal basis. This is totally in line with the interactive and personal nature of the capabilities of an FttH infrastructure.

As a result critical information can be supplied to:

Government agencies for a range of social, environmental and economic issues;
Networks operators for the design and topology of their infrastructure; and
Providers of these end-user services.

A well-structured and intelligent measurement process – known as behavioral economics – can generate predicted behaviors of patients, customers, staff and communities and can lead to significant service improvements. Governments can be far more precise in predicting where certain services are going to be needed, thus increasing productivity, efficiency and avoiding waste. Finding where the gaps are, or will be, for certain services becomes easier and when this is linked to the new electronic mapping tools it becomes possible to drill down deeply to where the efforts are needed the most.

Increasingly organizations involved in these data systems are personalizing their services further by getting the end-customers to actively participate. At BuddeComm we tried out a predictive data healthcare service from pkc.com. It can link up to 100 different sectors into its database. This database is linked to a knowledge database that, according to the input data, can come up with predictions that can be used to advise the patient and their carers. Such systems can also be operated by healthcare providers, who can go through the process together with their patient.

Massive increase in efficiency, productivity and customer satisfaction

It is not too difficult to extrapolate the concept behind this example to energy, transport, education and so on – not to mention the thousands of commercial applications that can be used. Already we are seeing these predictive data services used in mobile networks, websites, search engines and so on. Profiling is another technique used by pkc.com to enhance their predictions. This allows for far more intelligent lifetime-value propositions, loyalty services, customer service, and quality control.

The basic notion for all sectors is that it is essential to collect high quality data on inputs and outcomes, and then, using proven analytical methodologies to analyse the data, to turn it into information and put it to use. This increases the value of anything you measure many times over. It does, of course, help to have lots of information. Small data sets can overturn even the best hypothesis, since they do not represent a large enough population to corroborate or reject the premise.

With the right sort of data made available it becomes possible to calculate the various economic benefits. Demand forecasts based on intelligent socio-geographic data, as well as in relation to the impact of certain applications on the network (capacity demand), are other important outcomes. The demand for skills and services can be predicted and linked to job creation, training, etc.

Increasingly local and state governments are looking at broadband for economic development purposes, and predictive data can become very useful in this sector also, to actually put some figures into the mix. Furthermore, all of the systems provide services that can follow actual developments and compare them with the predictions. This not only provides accountability but maximizes the end results as well, since finetuning can take place in an intelligent way rather than adjustments being made with a stab in the dark.

Other benefits of these systems relate to job retention, customer satisfaction and in general the wellbeing of the ecosystem in which the organization participates.

These predictive data tools are going to transform customer service, but the results will only be as good as the company that is behind the actual service. As we have seen in the past, more data does not necessarily mean a better service. The organization must maintain top-level commitment to use the predictive data to the advantage of their customers, staff and the community at large.

Privacy is paramount

Privacy is paramount in these services, but fortunately there are many ways to address this issue.

Most countries have long-established government-based data research institutions (Census Bureau USA, Australian Bureau of Statistics) which have strict rules and procedures in place that can be benchmarked. They are also an excellent source of a great deal of good quality basic data that can be used in the intelligent data processes mentioned above.

Within these intelligent measurement systems none of the data needs to be personally tagged and all personal data remains in the ownership of the customer. Nevertheless strict legal privacy rules will need to be applied as these predictive services become more prevalent.

However, in principle, the social and economic benefits significantly outweigh the negatives. In healthcare for example, according to the Australian National E-Health Transition Authority, 80% of people are in favor of an electronic patient record system, which would allow for potentially helpful interaction with other data and medical knowledge databases to assist people in the management of their healthcare.

Unlike advertising, marketing, telco and other commercial organizations that gather data, it is in the interest of these data companies to provide that 100% guarantee. While others might receive a slap on the wrist but continue to operate their core business more or less unpunished, the credibility of these specialized companies would be at stake if they were to misuse data.

Paul Budde

Trans-sector strategies – an empirical approach.

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