Privacy

Privacy and Confidentiality Concerns in Interenterprise Data Sharing

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Exchange of data between enterprises – aka interenterprise data sharing – is becoming a hot topic in the era of digital transformation. As told in the post Data Quality and Interenterprise Data Sharing this approach is the cost-effective way to ensure data quality for the fast-increasing amount of data every organization has to manage when introducing new digital services.

McKinsey Digital recently elaborated on this theme in an article with the title Harnessing the power of external data. As stated in the article: “Organizations that stay abreast of the expanding external-data ecosystem and successfully integrate a broad spectrum of external data into their operations can outperform other companies by unlocking improvements in growth, productivity, and risk management.”

The arguments against interenterprise data sharing I hear most often revolves around privacy and confidentiality concerns.

Let us have a look at this challenge within the two most common master data domains: Party data and product data.

Party Data

The firm CDQ talk about the case for sharing party data in the post Data Sharing: A Brief History of a Crazy Idea. As said in here: The pain can be bigger than the concern.

Privacy through the enforced data privacy and data protection regulations as GDPR must (and should) be adhered to and sets a very strict limit for exchanging Personal Identifiable Information only leaving room for the legitimate cases of data portability.

However, information about organizations can be shared not only as exploitation of public third-party sources as business directories but also as data pools between like-minded organizations. Here you must think about if your typos in company names, addresses and more really are that confidential.

Product Data

The case for exchanging product data is explained in the post The Role of Product Data Syndication in Interenterprise MDM.

Though the vast amount of product data is meant to become public the concerns about confidentiality also exist with product data. Trading prices is an obvious area. The timing of releasing product data is another concern.

In the Product Data Lake syndication service I work with there are measures to ensure the right level of confidentiality. This includes encryption and controlling with whom you share what and when you do it.

Data governance plays a crucial role in orchestrating interenterprise data sharing with the right approach to data privacy and confidentiality. How this is done in for example product data syndication is explained in the page about Product Data Lake Documentation and Data Governance.

The Role of Data Discovery in Data Management

Henrik Gabs Liliendahl4 Comments

Data discovery is a term probably most mentioned in relation to business intelligence and data science. I this context data discovery can be seen as a more experimental and preliminary activity that can lead to a more continuous and integrated form of reporting and predictive analysis when hidden data sources, relationships and patterns are identified.

However, data discovery is useful in other data management disciplines as well.

Data Discovery

With the increasing awareness of data security, data protection and data privacy – and the regularity compliance enforced in this space – it is crucial for organisations to know what kind of data that flows and are stored within the organization. While you may argue that this should be available in already existing documentation, I have yet to meet an organization, where this is the case. And I come around a lot.

Data discovery is also a component of test data management and tool vendors package their offerings in this space with capabilities for data masking, data subsetting and data discovery in order to answer questions as:

  • Where are the data elements that should be masked when using production data in test scenarios without violating data privacy regulations?
  • How can you subset (minimize) test data sets derived from production (covering several databases) and still have proper relationships covered?

Within Data Quality Management, Data Governance and Master Data Management (MDM) data discovery also plays a role similar to the role in data reporting. We can use data discovery to map data lineage, find potential data relationships where data matching, data cleansing  and/or data stewardship might help with ensuring data quality and business process improvement and explore where the same data have different labels (metadata) attached or the same labels are used for different data types.

A Data Management Mind Map

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This blog is about Data Quality 3.0, Product Data Syndication Freedom, Multienterprise MDM – and many more data management topics.

These topics and the many more data management topics I have been around looks like the mind map below:

Data Management

If I can be of any help to you in the data management realm, here are some Popular Offerings.

Diversities in Civil Registration

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Citizen Registry

The way governments around the world has organized their Master Data Management (MDM) is quite different. When it comes to registering citizens, the practice varies a lot as described in the post Citizen Master Data Management.

I have lived most of my years in Denmark where our national ID is unique and used for everything by public agencies and also a lot by private companies. Some years ago I lived in the United Kingdom, where the public agencies (and my bank) had no clue about who I were, when I came, what I did and when I left.

Recently the World Economic Forum has circulated some videos on LinkedIn telling about how stuff is done differently around the world. The video below is about the Danish civil registry (which by the way is similar in other Scandinavian countries):

What do you think? Would this public MDM and data quality practice work in USA, UK, Germany or where else you live?

The Link Between Privacy and Product Data

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Do we as a consumer need to be told what to buy? Or do we rather want to be told what we are buying?

This theme was examined in a previous post titled You Must Supplement Customer Insight with Rich Product Data.

Not at least on the European scene with the upcoming General Data Protection Regulation (GDPR) there are limits to how far you can go in profiling your (prospective) costumers. And I am sure those people will value more you are telling them the complete story about your products, rather than guessing what products (from your range) they might need.

As a consumer, we want the facts about the products to make a self-service purchase. We want to be able to search for and navigate precisely to a product suitable for a specific use. We want the facts in a way, so we can compare, perhaps using a comparison service, between different brands and lines. We want to know what accessories goes with what product. We want to know what spare parts goes with what product.

By the way: Business buyers want all that too. And a person being a business buyer is a person (data subject) in the eyes of GDPR too.

For providing complete and consistent product data you as a (re)seller need to maintain high quality product data and if your product portfolio is just above very very simple, you need a Product Information Management (PIM) solution and, if you have trading partners, you need a PIM-2-PIM solution to exchange product information with your trading partners.

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Party Master Data and the Data Subject

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Within the upcoming EU General Data Protection Regulation (GDPR) the term data subject is used for the persons for whom we must protect the privacy.

These are the persons we handle as entities within party Master Data Management (MDM).

In the figure below the blue area covers the entity types and roles that are data subjects in the eyes of GDPR

Data Subjects

While GDPR is of very high importance in business-to-consumer (B2C) and government-to-citizen (G2C) activities, GDPR is also of importance for business-to-business (B2B) and government-to-business (G2B) activities.

GDPR does not cover unborn persons which may be a fact of interest in very few industries as for example healthcare. When it comes to minors, there are special considerations within GDPR to be aware of. GDPR does not apply to deceased persons. In some industries like financial services and utility, the handling of the estate after the death of a person is essential, as well as knowing about that sad event is of importance in general as touched in the post External Events, MDM and Data Stewardship.

One tough master data challenge in the light of GDPR will be to know the status of your registered party master data entities. This also means knowing when it is a private individual, a contact at an organization or an organization or department hereof as such. From my data matching days, I know that heaps of databases do not hold that clarity as reported in the post So, how about SOHO homes.

Your General Data Protection Roadmap

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Being ready for the EU GDPR (European Union – General Data Protection Regulation) is – or should be – a topic on the agenda for European businesses and international businesses operating with an European reach.

The finish date is fixed: 25th May 2018. What GDPR is about is well covered (perhaps too overwhelmingly) on the internet. But how do you get there?

Below is my template for a roadmap:

GDPR Readiness RoadmapThe roadmap has as all programs should have an as-is phase, here in concrete as a Privacy Impact Assessment covering what should have been done, if the regulation was already in force. Then comes the phase stating the needed to-be state with the action plan that fills the gaps while absorbing business benefits as well. And then implementation of the prioritized tasks.

GDPR is not only about IT systems, but to be honest, for most companies it will mostly be. Your IT landscape determines which applications will be involved. Most companies will have sales and marketing applications holding personal data. Human Resource Management is a given too. Depending on your business model there will be others. Remember, this is about all kind of personal data – that includes for example supplier contact data that identifies a person too.

The skills needed spans from legal, (Master) Data Management and IT security. You may have these skills internally or you may need interim resources of the above-mentioned kind in order to meet the fixed finish date and being sure things are done right.

By the way: My well skilled associates and I are ready to help. Get in contact:

Where GDPR Still Becomes National

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EU GDPRThe upcoming application of the EU General Data Protection Regulation (GDPR) is an attempt to harmonize the data protection and privacy regulations across member states in the European Union.

However, there is room for deviance in ongoing national law enforcement. Probably article 87 concerning processing of the national identification number and article 88 dealing with processing in the context of employment is where we will see national peculiarities.

National identification numbers are today used in different ways across the member states. In The Nordics, the use of an all-purpose identification number that covers identification of citizens from cradle to grave in public (tax, health, social security, election and even transit) as well as private (financial, employment, telco …) registrations have been practiced for many years, where more or less unlinked single purpose (tax, social security, health, election …) identification numbers are the norm most places else.

How you treat the employment force and the derived ways of registering them is also a field of major differences within the Union, and we should therefore expect to be observant of national specialties when it comes to mastering the human resource part of the data domains affected by GDPR.

Do you see other fields where GDPR will become national within the Union?

The Sunny Side of GDPR

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Happy SunDon’t panic about GDPR. Don’t neglect either. Be happy.

Recently Ditte Brix Andersen of Stibo Systems wrote a blog post called Preparing for GDPR – Burden or Opportunity?

As Ditte writes, the core implication of GDPR is: “Up until now, businesses have traditionally ‘owned’ the personal data of their customers, employees and other individuals. But from May 25th, 2018 individuals will be given several new personal data rights, putting the ownership right back in to the hands of each individual”.

I agree with Ditte that the GDPR coming into force can be seen as an opportunity for businesses instead of a burden. Adhering to GDPR will urge you to:

  • Have a clear picture about where you store personal data. This is not bad for business too.
  • Express a common understood idea about why you store personal data. Also very good for business.
  • Know who can access and update personal data. A basic need for risk handling in your business.
  • Document what kind of personal data you handle. Equally makes sense for doing your business.
  • Think through how you obtain consent to handle personal data. Makes your business look smart as well.

In fact, after applying these good habits to personal data you should continue with other kind of party master data and all other kinds of master data. The days of trying to keep your own little secret, even partly to yourself, versions of what seems to be the truth is over. Start working in the open as exemplified in the concept of Master Data Share.

What is a Master Data Entity?

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What is a customer? What is a product? You encounter these common questions when working with Master Data Management (MDM).

The overall question about what master data is has been discussed on this blog often as for example in the post A Master Data Mind Map.

Master Data

The two common questions posed as start of this blog post is said to be very dangerous. Well, here are my experiences and opinions:

What is a customer?

In my eyes, customer is a role you can assign to a party. Therefore, the party is the real master data entity. A party can have many other roles as employee, supplier and other kinds of business partner roles. More times than you usually imagine, the party can have several roles at the same time. Examples are customers also being employees and suppliers who are also customers.

From a data quality point of view, it does not have to matter if a party is a customer or not at a certain time. If your business rules requires you to register that party because the party has placed an order, got an invoice, paid an invoice or pre-paid an amount, you will need to take care of the quality of the information you have stored. You will also have to care about the privacy, not at least if the party is a natural person.

Uniqueness is the most frequent data quality issue when it comes to party master data. Again, it is essential to detect or better prevent if the same party is registered twice or more whether that party is a customer according to someone’s definition or not.

What is a product?

Also with products business rules dictates if you are going to register that product. If you are a reseller of products, you should register a product that you promote (being in your range). You could register a product, if you resell that product occasionally (sometimes called specials). If you are a manufacturer, you should register your finished products, your semi-finished products and the used raw materials. Most companies are actually both a reseller and a manufacturer in some degree. Despite of that degree practically all companies also deals with indirect goods as spare parts, office supplies and other stuff you could register as a product within your organisation in the same way your supplier probably have.

What we usually defines as a product is most often what rather should be called a product model. That means we register information about things that are made in the same way and up by the same ingredients and branded similarly. A thing, as each physical instance of a product model, will increasingly have business rules that requires it to be registered as told in the post Adding Things to Product Data Lake.