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The importance of data in a digital transformation context is known to everyone. Actually getting control and properly governing this new oil does not happen automatically. In this article we have summarized the top 5 Data Governance mistakes and also give you a number of tips on how to avoid them.

1. Data Governance is not business driven

Who is leading your Data Governance effort? If your initiative is driven by IT, you dramatically limit your chance of success. A Data Governance approach is a company-wide initiative and needs business & it support. It also needs support from the different organizational levels. Your executive level needs to openly express support in different ways (sponsorship but also communication). However this shouldn’t be a top down initiative and all other involved levels will also need to be on board. Keep in mind that they will make your data organization really happen.

2. Data Maturity level of your organization is unknown or too low

Being aware of the need for Data Governance is one thing. Being ready for Data Governance is a different story. In that sense it is crucial to understand the data maturity level of your organization.  

There are several models to determine your data maturity level, but one of the most commonly used is the Gartner model. Surveys reveal that 60% of organizations rank themselves in the lowest 3 levels. Referring to this model, your organization should be close (or beyond) the systematic maturity level. If you are not, make sure to first fix this before taking next steps in your initiative. You need to have these basics properly in place. Without this minimum level of maturity, it doesn’t really makes sense to take the next steps. You don’t build a house without the necessary foundations. 

Source : Gartner (Oct 2017) https://www.gartner.com/newsroom/id/3851963

3. A Data Governance Project rather than Program approach

A substantial amount of companies tend to start a Data Governance initiative as a traditional project. Think about a well-defined structure, the effort and duration is well known, the benefits have been defined, … When you think about Data Governance or data in general, you know that’s not the case. Data is dynamic, ever changing and it has far more touch points. Because of this, a Data Governance initiative doesn’t fit a traditional focused project management approach. What does fit is a higher level program approach in which you could have defined a number of project streams that focus on one particular area. Some of these streams can have a defined duration (i.e. implementation of a business glossary). Others (i.e. change management) can have a more ongoing character. 

4. Big Bang vs Quick Win approach

Regardless of the fact that you have a proper company-wide program in place, you have to make sure that you focus on the proper quick wins to inspire buy-in and help build momentum. Your motto should not be Big Bang but rather Big Vision & Quick Wins.

Data Governance requires involvement from all levels of stakeholders. As a result you need to make everyone clear what your strategy & roadmap looks like.

With this type of programs you need to have the required enthusiasm when you take your first steps. It is key that you keep this heart beat in your program and for that reason you need to deliver quick wins. If you don’t do that, you strongly risk losing traction. Successfully delivering quick wins helps you gain credit and support with future steps.

5. No 3P mix approach

Data Governance has important People, Process and Platform dimensions. It’s never just one of these and requires that you pay the necessary attention to all of them.

  • When you implement Data Governance, people will almost certainly need to start working in a different way. They potentially may need to give up exclusive data ownership … All elements that require strong change management.
  • When you implement Data Governance you tilt your organization from a system silo point of view approach to a data process perspective. The ownership of your customer data is no longer just the CRM or a Marketing Manager but all the key stakeholders involved in customer related business processes.
  • When you want to make Data Governance a success you need to make it as efficient and easy as possible for every stakeholder. This implies that you should also thoroughly think about how you can facilitate them in the best possible way. Typically, this implies looking beyond traditional Excel, Sharepoint, Wiki type solutions and looking into implementing platforms that support your complete Data Governance community.



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The Dutch bank Rabobank has implemented a creative way of using customer data, without having to request permissions. If you are one of their customers and they use your data with internal tests to develop new services, there is a chance that you will get a different name. With special software data is pseudonymized and they do so with Latin plant and animal names.

Your first name will become i.e. Rosa arvensis, the Latin name of a forest rose, and your street name i.e. Turdus merula, the scientific name of a blackbird. It is a useful solution for the bank to be somehow in line with the General Data Protection Regulation (GDPR) that takes effect on the 25th of May. When developing applications or services, analyzing data or executing marketing campaigns based on PII (Personally Identifiable Information) type of data, companies require to have an explicit consent. In order to be able to do this after May and without getting your consent, the bank uses data masking / pseudonnymization techniques.

 

Explicit consent & pseudonymization

With the new privacy law the personal data of citizens are better protected. One of the corner stones of the GDPR is the requirement to get an explicit consent and linked to that the purpose. Even with a general consent, companies do not get a carte blanche to do whatever they want to do with your data. Organizations must explain how data is used and by whom, where they are stored and for how long (more info about GDPR). Companies can work around these limitations if they anonymize / pseudonymize this PII type of data because they can still use and valorize this data but without a direct and obvious link to you as a person. You as a person become unrecognizable but your data remains usable for analysis or tests.  


Why scientific animal and plant names?

‘You can not use names that are traceable to the person according to the rules, but suppose it is a requirement to use letters with names, you have to come up with something else,” explains the vendor that delivered the software. “That’s how we came up with flower names, you can not confuse them, but they look like names for the system. Therefore, it is not necessary for organizations to change entire programs to comply with the new privacy law”.° 

Note that data anonymization/ pseudonymization technology does not require you to use plant and animal names. Most of this type of implementations will convert real to fictitious names and addresses that even better reflect the reality and perhaps better also match the usage requirements (i.e. specific application testing requirements). Typically substitution techniques are applied where a real name is replaced with a another real name.

 

Take aways

Pseudonymization vs anonymization

Pseudonymization and anonymization are two distinct terms that are often confused in the data security world. With the advent of GDPR, it is important to understand the difference, since anonymized data and pseudonymized data fall under very different categories in the regulation. Pseudonymization and anonymization are different in one key aspect. Anonymization irreversibly removes any way of identifying the data subject. Pseudonymization substitutes the identity of the data subject in such a way that additional information is required to re-identify the data subject.  With anonymisation, the data is cleansed for any information that may be an identifier of a data subject. Pseudonymisation does not remove all identifying information from the data but only reduces the linkability of a dataset with the original identity (using i.e. a specific encryption scheme). 

 

Pseudonymization is a method to substitute identifiable data with a reversible, consistent value. Anonymization is the destruction of the identifiable data.

 


Only for test data management?

You will need to look into your exact use cases and determine what techniques are the most appropriate ones. Every organization will most likely need both. Here are some use cases that illustrate this: 


Use caseFunctionalityTechnique
Your marketing team needs to setup a marketing campaign and will need to use customer data (city, total customer value,  household context, …).Depending on the consent that you received, anonymization or pseudonymization techniques might need to be applied. Data Masking
You are currently implementing a new CRM system and have outsourced the implementation to an external partner.Anonymization needs to be applied. The data (including the sensitive PII data) that you use for test data management purposes will need to transformed to data that cannot be linked to the original.  Data Masking
You are implementing a cloud based business application and want to make sure that your PII data is really protected. You even want to prevent that the IT team (with full system and database privileges) of your cloud provider has no access to your data.Distinct from data masking, data encryption translates data into another form, or code, so that only people with access to a secret key or password can read it. People with access but without the key will not be able to read the real content of the data. Data Encryption
You have a global organization also servicing EU clients. Due to the GDPR, you want to prevent  your non-EU employees to access data from your EU clients.Based on role and location, dynamic data masking accommodates data security and privacy policies that vary based on users’ locations. Also data encryption can be setup to facilitate this. Data Masking
Data Encryption
Your have a brilliant team of data scientists on board. They love to crunch all your Big Data and come up with the best analysis. In order to do that, they need all the data you possibly have. A data lake also needs to be in line with what the GDPR specifies. Depending on the usage you may need to implement anonymization or pseudonymization techniques.Data Masking

 

Is Pseudenomization the golden GDPR bullet?

Pseudonomization or anonymization can be one aspect of a good GDPR approach. However, it is definitely not the complete answer and you also will need to look into a number of other important elements:

  • Key to the GDPR is consent and the linked purpose dimension. In order to manage the complete consent state you need to make sure that this information is available to all your data consumers and automatically applied. You can use consent mastering techniques such as master data management and data virtualization for this purpose.



  • Data Discovery & Classification

    The GDPR is all about protecting personal data. Do you know where all you PII type of data is located?  Data discovery will automatically locate and classify sensitive data and calculate risk/breach cost based on defined policies.


    Data Discovery & Classification

  • Data Register

    A data register is also a key GDPR requirement. You are expected to maintain a record of processing activities under your responsibility or with other words you must keep an inventory of all personal data processed. The minimum information goes beyond knowing what data an organization processes. Also included should be for example the purposes of the processing, whether or not the personal data is exported and all third parties receiving the data.

    A data register that is integrated in your overall data governance program and that is linked with the reality of your data landscape is the recommended way forward.




° Financieele Dagblad

Also in need for data masking or encryption?

Would you like to know how Datalumen can also enable you to use your data assets in line with the GDPR?

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