Cambridge Analytica is quite in news, but for all wrong reasons. Mass data theft from Facebook for illegal purposes by this company, has highlighted the grey areas of data analytics.
Big Data is actually big! But is it big enough to defy the basic norms of ethics? Not really.
However, incidents like this make us question the ways in which Big Data works. Thanks to various social media networks, user data is exposed to all sorts of parties. Big Data not only allows analysts to collect, analyze, and draw insights, it can also allow access to data that is a threat to an individual’s privacy. While most of the users on social media are aware that the so called privacy doesn’t exist anyways, but that doesn’t mean that the data is available to fulfil malicious intents of the people in power.
IDC predicts that by 2020, more than 1.5 billion people, or approximately a quarter of the world’s population, will be affected by data breaches
Data analytics has to be a much responsible operation with sound governance in place. Everybody involved in the value chain right from the users, the social media networks, the third parties, the analytics firm as well as their clients must share this responsibility jointly. While undertaking huge analytical projects or other Big Data initiatives, organisations must take a three-pronged approach to governance based on Authorised Usage, Data ownership and Integrity.
By ethical use of data we mean data practises aimed at gaining trust of users, showcasing integrity of the organization as well as minimizing instances of misuse of data.
But ethics is a subjective term and there is a lot of scope for its interpretation and implementation. What is ethical for one organisation may not be ethical for the other. The definition is bound to change based on geography, people involved, time, situation etc. That said, technology is evolving at a neck break speed and some protocols and rules are urgently required to check such security breaches.
Source: IBM Big Data Hub
Some of the suggested ways that can be effective in enhancing the ethical usage of Big Data are:
1) Decide-Is it beneficial for all?
Collection of user’s personal information and using to target ad campaigns is one of the main aims of certain data analytics firm. However, the firms must realize whether it is adding any value to the customers or the client company’s image. For eg: Your favourite pizza joint analyzing your eating preferences and remembering your choice of toppings and pizza crust the next time you place an order, is surely flattering. However, a travel company tracking personal messages shared between you and your friend and offering you a tour package without you even approaching them is kind of intrusive and damages the image of the firm.
2) Conduct due-diligence while collecting and sharing the data
Many times imposters create fake id and extract personal user data posing as students or researchers. The social media company and analytics firm both should verify the authenticity of the third-party dealers and others with whom they are sharing any kind of confidential information.
3) Penalties for internal misuse in place
Many a times it has been observed that the misuse has been caused by insiders of the analytics firm or the social media networks. In such cases strong internal controls need to be installed and whenever an employee is found guilty strict action must be taken and penalties must be imposed. This is the only way to make everyone accountable. Also all insiders must be provided adequate training on the policies and guidelines to safeguard the Big data ethics at all times.
4) National and international regulations for what kind of data can be collected
Now this will probably require a lot more time. It is preferable if global standards for securing privacy are set, so that there is uniformity in defining privacy breaches. There may be various privacy laws and regulations across nations however they are diverse and each law has a different version of what is acceptable and what is not. It will be in interest of all if socially beneficial applications of big data is more clearly defined.
5) Privacy policies by third party need to be simplified
Whenever we sign up on a social medial network or any other application on the internet, we need to give our acceptance to the privacy policies of the websites by clicking a simple “I agree” or “Do not accept” options. However, the privacy policies are so verbose and complicated that none of us would actually read through it completely, particularly at a time when all we want is to set up our account quickly. Hence, it is imperative that the privacy policies are made simpler and easily readable so that the users are aware of what all their data is being exposed to. Also, the privacy settings in the account need to be made less confusing and more user friendly.
6) Define clearly what data is actually required:
Organisations or Big Data practitioners must define beforehand what and how much data is really required for business and what data they can really do without. By clearly outlining the data requirements, the analysts can take a focused approach and stay away from collecting excessive information that might be just wasteful. To the point data means lesser scope for misuse.
7) Crisis strategy in case of data breach
Data breach hack crisis is something even some of the biggest of organisations have faced. There is need to be a crisis management strategy in place for Big Data practitioners as well as the companies storing huge amount of user data. All the information related to the stakeholders, business and user data needs to be protected to the core. Being proactive is the best strategy for crisis management of this type. Also each stakeholder needs to be made aware about the protection policies.
The list of practices is not an exhaustive one, but they provide a foundation on which stronger practices can be based to mitigate risks and enhance the value of Big Data. We know that the future of Big Data is bright, therefore Big Data ethics must be considered an integral part and given emphasis while creating policies, guidelines and procedures. This is the only way to ascertain that the real value of Big Data is realized in future without causing any security or privacy breaches at large.
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