Information Strategy and the EU Data Protection Reform

20

October

2016

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The European Union is implementing a Digital Single Market Strategy to take away barriers for businesses to use and benefit from information. The Data Protection Reform will provide businesses with numerous benefits and opportunities. Firstly, data protection rules will be the same across Europe. Secondly, businesses can benefit from the new right to move their personal data from one service to another. And lastly, smaller businesses will benefit from a simplification of the regulatory environment.

Unification of data protection rules in Europe removes a barrier for businesses to use and benefit from (personal) data. Before the reform, different rules were applicable across Europe. Because there are no real borders when using the internet, businesses had to deal with all these different set of rules. After the reform, an EU business can expand across Europe, without having to change the way they handle personal data. This removes a (legal) obstacle for businesses to use information internationally. For non-EU businesses it will become easier to engage in trade with EU businesses or citizens and to use the information they gather from EU citizens. They are no longer required to comply with 28 different sets of rules for each European country but only need to comply with the unified EU rules. The reform will bring businesses benefits of an estimated €2.3 billion per year.

Businesses can also benefit from the right of EU citizens to move their personal data from one service provider to another. This enables the citizen to switch more easily between services and give starting businesses the opportunity to compete with established, bigger companies. There can be quite the barrier preventing users of services, like Facebook, to switch to another social media platform because it would require the user to start over with personalizing the service. That is why new companies face a barrier. This barrier can be lifted by this new right.

Because of the unification of rules, they can be quite complicated. It would be unfair to small businesses if they had to match all the rules bigger business face. That is why small companies face a smaller administrative burden. They do not have to appoint a Data Protection Officer, it is less likely that they will face the obligation to carry out Data Protection Impact Assessments and are exempt from the duty to put together documentation on their data processing activities.

 Sources:

http://ec.europa.eu/justice/data-protection/reform/index_en.htm

http://ec.europa.eu/justice/data-protection/document/factsheets_2016/data-protection-factsheet_01a_en.pdf

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Digital Transformation Project – Implementation of a CRM system for the Law Faculty Association Rotterdam

13

October

2016

5/5 (13)

Group 33

The Law Faculty Association Rotterdam (LFR) is the study association for students of the Erasmus School of Law. The over 3500 members are the core of the organization. Currently, LFR has no efficient way of managing and analyzing data that is created within the organization. This is disadvantageous for two main reasons. Firstly, without a system to collect data, the LFR-board must base their decisions on experience and intuition. Those decisions tend to be worse decisions than data-driven decisions (McAfee & Brynjolfsson, 2012). This is especially true for LFR because every year the entire board is replaced. Secondly, without data it is difficult to understand and meet the needs of stakeholders. The needs of law firms, for example, are to find the students that meet their standards for potential employees. Using additional data of members, like their interests, LFR will be able to provide a better match between students and firms, and thus create more value for both the members and the law firms.

A CRM system can be a key asset of a firm, and proper implementation can result in important strategic benefits (Colgate & Danaher, 2000; Rigby & Ledingham, 2004). The current CRM environment cannot be seen as an optimal CRM system, as it does not comply with the needs and wants of LFR. A main flaw of the system is that it cannot store and analyze the data generated by the activities of members on the website. If this were possible LFR could, for example, offer specific members specific services based on their past participation in LFR activities.

Therefore, it is recommended for LFR to implement a strategically aligned LFR system in order to better align the business and IT strategy. For a successful implementation of the LFR system, it is recommended to follow the steps below:

  • Identify the customers for which the relationship needs to be improved. The most apparent customer groups are the members, the faculty and the law firms. However, other possible customer groups should be taken in consideration.
  • Analyze customer-organization relationship value analysis from the perspective of both the LFR and the customer. What information can be used to increase customer value in order to increase customer equity? LFR should consider the information that can be collected now and what information should be collected as well.
  • Organize the information needed in the following information types: information of the customer, information for the customer, and information by the customer. This gives LFR more insight in how the information can be used and how the information can be used to create value.
  • Collaborate with a Software as a Service CRM provider to implement the CRM system and to connect it to others systems within the LFR IT environment.
  • Evaluate the impact of the CRM system on the customer relationship after implementation. Because in practice, CRM systems often fail to meet expectations, the system should be evaluated repeatedly from an early stage onwards to ensure the alignment of the business and IT strategy.

References:
Colgate, M. R., & Danaher, P. J. (2000). Implementing a customer relationship strategy: The asymmetric impact of poor versus excellent execution. Journal of the Academy of Marketing Science, 28(3), 375–387.
McAfee, A. & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review 90(10) 60-68.
Rigby, D.K. & Ledingham, D. (2004). CRM done right. Harvard Business Review, 82(11), 118-112, 124, 126-129, 150.

Image:

https://www.salesnet.com/wp-content/uploads/2016/01/Dollarphotoclub_92112876.jpg

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Quantified Self and Information Asymmetry in Health Insurance

4

October

2016

5/5 (2)

With today’s technologies it is easier than ever to track and analyze your body, mood, diet and just about everything you can imagine. Tracking and analyzing data about yourself is referred to as the ‘Quantified Self’. The information can be used to make an inference about your health and how your decisions affect your health. Potentially, this data can also be used by healthcare insurers, to decrease the information asymmetry and moral hazards in the health insurance industry.

At first sight, no. In the Netherlands, it is not allowed to charge different premiums for the same package, regardless of personal differences like health or age. So it seems that the concept of Quantified Self cannot be used at all.

Healthcare insurer Menzis however has a clever way of dealing with this problem. With the ‘SamenGezond’ program customers of Menzis can save points by living healthy. The program accepts a wide range of activities that are rewarded with points. A run that is tracked with Runkeeper is an example of Quantified Self data that can award the customer with points. These points can used as a currency in the Menzis webshop. In this webshop a wide range of products and services can be bought: from portable speakers to a relaxing day in a welness resort.

In a way, this is not different at all from charging different premiums to customers with different health levels. The baseline premium could be high but, as long as the products in the webshop are products that the customer needs anyway, can be lowered by living healthy and saving points. To give a more accurate representation of customer health Menzis could expand the data that earn the customer points. With smart wristbands the activity level, sleep quality and quantity and even the heart rate of the customer could be used to reward points.

It is important to consider that the healthcare system of the Netherlands is based on social solidarity. With that in mind it would be unfair to charge those with a genetic health disadvantage or the poor higher premiums. But it also means, in my opinion, that it is unfair that those who invest in making healthy choices every day face a higher premium due to individuals that are not making those decisions. The data resulting from the Quantified Self could be used to align the health insurance premium with the actual health of the applicant.

What do you think? Is the Quantified Self a solution for the information asymmetry and moral hazard in the health insurance market?

Sources/Interesting Links:

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Ted Talk on the Quantified Self

Menzis SamenGezond progam

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