- January 27, 2020
- Posted by: Abhimanyu Sundar
- Category: Analytics, Big Data, Big Data Analytics, GDPR Compliance, Machine Learning $ Deep Learning
The European Parliament, Council and Commission created a regulation which toughens and combines data protection for people inside the EU – This regulation is called GDPR. It is a single set of rules which are created in order to govern as to how personal data is used. This is done regardless of the source and across all uses.
GDPR protect the personal privacy laws pertaining to the data rights of the EU citizens. GDPR is not restricted to organizations inside the EU alone, on the contrary, any organization with customers in the EU will be affected.
The way companies handle personal data will change forever with the introduction of GDPR. Europe’s data protection rules have undergone a huge change with GDPR being introduced. GDPR replaced the 1995 Data Protection Directive. The internet is growing at a rapid pace. Digital content has increased at an unimaginable rate. This has led to loads of personal data being held digitally. With so much personal data out there, the need for an enhanced data protection regulation arose and hence, GDPR. What GPR does is, it empowers individuals to gain access and control over the information held on them. While empowering individuals, GDPR also holds organizations accountable for the way thy handle and store personal data. Companies will be required to have the latest documentation and communication when it comes to data protection.
Irrespective of the size of the business, data is always generated. If a website, a social media presence and a payment gateway exist, data definitely exists. This data can be collected on their customer, webpage navigations, user experience and lot more. However big or small a company is, the need for big data analytics to analyze its data is a must.
The use of advanced analytics gives you better insights. When the analytics provide you with custom market and business intelligence, the resulting insights will help make informed and better decisions.
Machine performance and human performance can be tracked. Deliver routes can be optimized. Recruitment can be made simpler. All of this can be done with advanced analytics. Be it any department of any business, operational efficiency is bound to become better with Big Data Analytics.
These are just a few ways as to how analytics is changing the way businesses function.
GDPR has a huge impact on advanced analytics because of the nature of how advanced analytics functions and the data which is collected and analyzed.
When we talk about ‘Big Data’, a large chunk of it comprises of personal data. The use of personal data has a huge impact on data protection, data privacy, individual privacy rights and more so. These rights are further empowered by GDPR. Does this mean that the end is near for Big Data Analytics? Not really!
GDPR and its related regulations do not aim at confining Big Data Analytics but rather, offer a structure for effective regulation. Big Data Analytics and data protection are objects that can enhance each other rather than stop each other from flourishing.
Not all of big data is personal data and only the personal data part of it is covered by GDPR. Big data analytics like weather data and so on are examples of non-personal data. For the purpose of analytics, personal data can be anonymized, rendering the data to not come under the data protection regulations.
Keeping that in mind, a lot of big data is personal data. This data can be used individuals directly or with a combination of datasets. Therefore, data protection is a must in this space. There are 3 different areas to consider here:
Does the use of the personal data prove to be intrusive to the individual?
The use of people’s data for big data analytics – is it within the scope of what they reasonably anticipate?
The transparency of the organization about how it is processing personal data – how transparent can the organization be?
When we talk about personal data being used for big data analytics, there are a few types of personal data. It may be ‘new types’ for the analysis which may include ‘observed data’, derived or inferred data’. New types of data are consciously provided by the user. This type of data can be produced using analytics methods and ML algorithms or through different cookies and sensors.
A blog post like this cannot ensure that all the areas under GDPR compliance are covered. However, here are a few ways by which you can be prepared:
This can be a painstaking task. You need to decipher and pin point as to where your customer data originated from and where it is stored currently. In an organization which is big, you may have to trace the path that the data has taken through various systems.
Once the data is acquired by you, what happens to it? How is this data being used and for what purpose? How is the data transformation happening? What processes does this data undergo?
During the time of data collection, you need to know if any consent was asked for and granted. As stated above, under GDPR regulations, consent can be revoked at any point in time. You must keep track of whether consent has been revoked after being granted. Another key area is to make sure that the analytics model in use can filter out the data for which consent has not been given. The model at the same time should be adaptive to account changes in consent.
The analytics platform in use should definitely be integrated with the security system of the organization. This must be done so that the access to the customer data can be monitored effectively. During the entire stage of the analytics cycle, the data must be held securely and not just during the data collection stage. Additionally the customer data must also be encrypted.
This is one of the trickiest part of the entire cycle. Knowing where the data goes and monitoring what happens to it at each stage of the analytics cycle is extremely challenging. Each process must be closely monitored. This will ensure that ongoing GDPR compliance is maintained.
Being GDPR compliant is no easy task. The use of the personal data must be reported, audited and accounted for. When it comes to a large enterprise, this will be a logistic challenge. Another method to achieve this is to develop your systems to provide a thorough audit trail which is required to be GDPR compliant.
Test Data Management is a challenge when complying with GDPR.
Here are a few guidelines that can be followed to ensure that GDPR compliance is maintained for your Test data –
GDPR and Big Data Analytics are not two objects that are poles apart. They are two separate entities that can push each other to newer heights. Only this time it will be with a lot more regulation when it comes to personal data protection and personal data privacy laws.