Human Resources: Human Data .. Keith Harrison-Broninski

mickyates Big Data, Ideas, Innovation, Knowledge Leave a Comment

The flipside of processes is data. In recent years, the emphasis has swung away from the former and towards the latter, with the emergence of buzzwords (buzzphrases?) such as Big Data, but the reality is that the two are symbiotic and it doesn’t make a lot of sense to try and do something with one unless you also give consideration to the other.

In the process world, we now have a fairly stable set of standards, techniques, and tools. The data world, however, is not there yet – in fact, many people are not even sure what data is, let alone how it relates to the processes that create, manipulate and use it. So let’s a take a look at the key aspects of data, and see how they relate to the specific focus of this Column, human processes.

Data is usually considered as the foundation of a pyramid with multiple layers, as in the common formulation Data-Information-Knowledge-Wisdom (https://en.wikipedia.org/wiki/DIKW_pyramid). However, not only is the “knowledge component of DIKW generally agreed to be an elusive concept which is difficult to define” but, to me at least, the Wisdom component is poorly named since anecdotal concepts of wisdom often do not relate to specific facts but rather to a skill in using any facts.

So here I propose a more elaborate model that replaces the Knowledge and Wisdom elements of DIKW with easily understood concepts that relate directly to facts. Not only does my version, DIADEM, make explicit the connection to human processes, but it has the further advantage of being pronounceable 🙂

The elements of DIADEM are described below, with examples given in italics beneath each element by reference to my current project Town Digital Hub (http://bit.ly/tdh-psycap). All statistics are imaginary, and the example is chosen deliberately as light hearted so please don’t take it too seriously. No offence is intended to any expert hoofers out there!

Data – discrete, objective facts or observations, which are unorganized and unprocessed and therefore have no meaning or value because of lack of context and interpretation. 37 people have reviewed a local exercise class, with the average rating being 3 stars out of 5.

Information – organized or structured data, which has been processed in such a way that the information now has relevance for a specific purpose or context, and is therefore meaningful, valuable, useful and relevant. The average rating from men of the exercise class is 2 stars, and from women 4 stars. Statistics from the provider of the class show that men attend an average of 2 sessions before dropping out and women 12. Other fitness classes have approximately equal ratings and persistence for men and women.

Answers – Identification of, and responses to, specific questions of contextual importance. 
How does this exercise class differ from others in the area? 
It focuses on movement to music rather than flexibility or strength.

Deductions – Conclusions drawn from answers. It may be advisable for the providers of the class to (a) clarify in promotional material that the emphasis is on movement to music, so as to lower the dropout rate (b) target the class directly at women, so as to focus on the currently most successful demographic.

Effects – Identification of actions that would make effective change.
 There are cardiovascular health benefits from vigorous sustained exercise, for which movement to music is ideal, especially for people unable to take exercise outdoors or without access to specialized equipment. So there may be benefits to providing such an exercise class in a form that more men would enjoy.

More – The feedback loop that tells service providers what additional data they should start collecting, and encourages people to share more personal data. There are various possible sources of the poor ratings from men. For instance, some men may be embarrassed about what they feel to be their poor coordination, especially in front of women. There may also be issues related to choices of music and styles of movement. To determine whether it would be worth starting a men only movement class, men interested in doing more physical exercise could be surveyed via the website to identify their preferences, and asked if they mind this data being used to develop a new local service.

The DIADEM model not only clarifies how data differs from the uses to which it is put, but also explains exactly how to go about doing so – i.e., the human processes that are required to collect data and do something useful with it.

Acknowledgement: Thanks to Professor Mick Yates for a discussion of facts and their uses that was key to formulating the DIADEM model.

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First published in BP Trends

Keith Harrison-Broninski is a consultant, writer, researcher and software developer working at the forefront of the IT and business worlds. Keith wrote the landmark book “Human Interactions: The Heart And Soul Of Business Process Management”, described by reviewers as “the overarching framework for 21st century business technology” and “a must read for Process Professionals and Systems Analysts alike”. Keith founded Role Modellers, whose company mission is to develop understanding and software support of collaborative human work processes, the field Keith pioneered with his work on Human Interaction Management. For more information about Keith, see http://keith.harrison-broninski.info.

The Data Privacy Paradox

mickyates Big Data, Customer, Ideas 1 Comment

As published in Digital Marketing Magazine last week.

In the wake of the Panama Papers scandal, it seems that everywhere you look there is some kind of story about data privacy. Whether it’s big-time leaks or personal data scares, the security of our personal data is constantly in question.

Last year a report conducted by the Digital Catapult found that the majority of British consumers did not trust organisations with their data and did not appreciate the benefits of sharing personal data.

Another survey, by Intel Security, discovered that just 13 per cent of the public trusts cloud providers to secure sensitive data. As a nation we are skeptical of how businesses use our data and the methods they use to store it.

Yet, every one of us is demanding more personalised services, which can only be provided if we relinquish our hold over private data. You call a customer service helpline, you expect the agent to know you and the details of your account. If you get offers from retailers, you expect them to be relevant. This demand presents us with a ‘Data Privacy Paradox’, as although we expect these services we are incredibly reluctant to knowingly offer up our data.

This is particularly true within the health sector. If you undergo hospital treatment, you expect everyone involved to have the right information about you. Imagine being taken unconscious to A&E, and being given a drug injection which you are allergic to. It might have been given with the best possible intent, but imagine if the paramedics don’t know your allergies because “personal privacy” concerns are stopping the appropriate databases being joined up in the ambulance.

This is not the case in all industries. Some insurance companies are already offering discounts or special offers if you allow them access to your driving habits. Take your fitness data gathered from your wristband, for example. It doesn’t sound too bad. But would you offer up access to your DNA code if it could help forecast likely illnesses in future? As they say in the financial ads, your premiums might go down or up…

On the other hand, if medical and health planners had a view of a population’s aggregated DNA profile, they could use this to predict future services needs, doctor specialty training and the like.

This was attempted by the NHS’s care.data initiative, a centralised programme that anonymously analysed patient data and made this available to insurance companies and drug producers to help predict trends to aid the research of new drugs and adjust general policies towards certain diseases. However, many people were incorrectly led to believe that their personal medical data was shared for the benefit of corporate pharmaceutical companies and insurance providers.

The issues came largely from a lack of constructive education about the benefits of aggregation. Instead of it being seen as a way of better understanding disease trends and thus planning the best possible NHS services, it was hyped in the press as drug companies making money off GP data and personal privacy being invaded. Education goes a long way.

In fact, aggregated data is often more useful than individual data. Analysis allows aggregators to predict macro trends, which is the only way to run any enterprise or business at the strategic level.

So where does that leave us with the Data Privacy Paradox?

Personal privacy is not usually breached at the point of collection, or when it is anonymised and aggregated. Privacy is breached when the data is actually used on an individual basis in ways that the individual does not want it to be used. We have long accepted data being collected about us, from the traditional population census to retailer loyalty cards. And don’t forget that the UK has the highest use of surveillance cameras in the world. We just ignore being photographed, in the belief that it will help the police catch the bad guys.

One thing is clear: data collection is everywhere and is gathering pace.

EU Data Protection Reform (DPR) will go a long way to protecting individual rights, stating that we must all have easier access to our own data (to understand what data is held about us and by who). We should have clearer information on how our data is processed and used; and there must be a right to data portability, making it easier to transfer our personal data between providers.

So, when setting our regulatory framework, what should be considered?

First, the use of data should be transparently declared – no unreadable service agreements. The UK Kite Mark could be a very interesting approach to make the issues clear.

Second, the data should be used in ways that are clearly beneficial to the individual that the data is collected from, and this should be declared.

Third, aggregated use of personal data should also be declared, with its benefits clearly stated.

Think of these three points as two-way education – for both the business and the individuals whose data is being collected.

Ultimately, there are no easy answers here, but there are definitely steps to be taken to handle the Data Privacy Paradox in ways that are beneficial to everyone concerned. Fundamentally, that starts with the benefits to the individuals from whom the data is collected in a fair and open exchange.

by Mick Yates, as Director of Partner Strategy at Starcount, the predictive insight company