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Striking a balance between data analytics and data privacy

Published datePublished: Sep 30, 2020 ViewsViews: 4088
Deepak Sinha

Deepak Sinha

Deepak is a hands-on Technology Leader with expertise in designing software solutions for various successful projects for multinationals. He has total experience of 15+ years and has worked on all the phases of application development and has good experience in open source and mobile technologies. He is passionate on new Technologies and has strong interest on Machine Learning, Artificial Intelligence etc.
Striking a balance between data analytics and data privacy

At the beginning of the decade, case was being made out for organizations to adopt a data driven business model to retain the competitive edge. This was because big data and analytics promised to transform business performance. As we stand at threshold of the next decade, data has become even more important, as evident in a survey undertaken by NewVantage Partners’ 2019 Big Data and Executive Survey.

Findings of the survey indicate that companies are acknowledging the need for a data driven business model:

  • 92% respondents reported acceleration in pace of big data and AI initiatives
  • 88% reported a greater urgency to invest in big data and AI
  • 55% companies reported big data investments in excess of $50MM
  • 68% organizations had Chief Data Officer (CDO) roles as compared to just 12% in 2012

However, these data driven initiatives have the potential to go against data privacy issues if not taken care of proactively. The ever-evolving data privacy regulations across the globe are forcing enterprises to look at ways in which they can use key data without any conflict with privacy compliances.

The 2018 Cambridge Analytica scandal was a watershed moment in the history of data privacy when people realized what could happen to their personal data in the wrong hands. The scandal also brought to light the role of businesses in ensuring data security measures for storing and providing access to customer data. Records of 87 million Facebook users had been harvested by simply hosting a quiz.

The interesting point was that only a couple of thousands of people had taken the quiz. But Cambridge Analytica could access the personal record of all the “friends” of Facebook users who had taken their quiz. The systems to maintain data privacy were not in place.

In this article we will discuss why data driven approach is essential for businesses and how they can strike a balance between data analytics and data privacy. Let’s dive straight in.

Why do businesses need a data driven approach

The data driven approach is no longer a luxury; it is becoming a strategic necessity that can make or break companies’ chances of future growth. Data driven businesses have access to meaningful operational data in all business-critical areas, which strengthen decision making as well as process innovation. Both crucial to business growth.

Collection and analysis of data is nothing new for businesses. Customer feedback forms, telephonic or snail mail surveys, customer interviews, etc. have been around for centuries. But Internet has taken all forms of data collection to another level. And Internet of Things keeps churning out data every second, without sometimes even the customer being aware of it. Humans are generating 2.5 quintillion bytes of data every day.

Using data and insights provided by it to make an informed decision is called data-driven decision making. Past data can provide estimated projections for the future, which can be used to validate a decision being taken.

Businesses may collect the data using survey responses, user testing launch a new product or service to a selected group of customers. The decision of which data to include and how, depends upon business goals and data available.

Only 41.4% of CDOs considered their chief responsibility to be managing and leveraging data as an enterprise data asset. Probably the overwhelming amount of data makes it difficult for them to analyze into real actionable insights.

Traditionally business decisions were made on the basis of gut instinct and past experience. This approach to business decision making has two major flaws. One, gut instinct does not always prove right. Two, not every business has access to experience at all times. If they rely only on experience, what would happen when the senior most leader is no more available to guide.

Data driven decision making can be adopted by any organization, irrespective of its age and expertise. Data can serve as the benchmark against which the efficacy of current processes can be verified. Data can also be used to finetune the current processes and reliably predict how effective they can be. Also, data is more logical and tangible as compared to gut instinct or experience. By using data to take decisions, you are effectively removing the subjective elements from decisions, which makes you more confident of the outcomes.

Taking a data driven approach to business decreases expenses, as found in a survey of Fortune 1000 executives. 49% of the organizations claimed their data projects to be successful and reported an actual decrease in expenses.

Is data privacy a hurdle in adopting data driven model

Making decisions on the basis of hard evidence in the form of data needs a major cultural, technological and mind set change. Established organizations have internal barriers culturally as well as practically (think legacy infrastructure and traditional ways of operating). But this should not mean they cannot transform their situation. If you think of it, organizations that are decades old have that much more amount of data at their disposal.

Is data privacy a hurdle in adopting data driven model

Still they struggle to exploit it because they are unable to transform data into usable, actionable insights. Forrester reports that between 60% and 73% of data within an enterprise goes unutilized for analytics. This could be due to siloed data, poor reliability and a lack of analytical talent. The data may be spread in different formats across different themes and cannot be compared using the Legacy modernization company.

The primary requirement of established organizations is to link data to business-critical impact. The data and insights generated by them must be easily accessible, inter-pretable and actionable whenever required. This can be made possible if organizations employ requisite technology and stop thinking in terms of “we have always done it this way.” And this is something businesses must resort to because the true power of data is in its use.

Once the cultural challenges are taken care of, two major operational challenges come up in creating a data-driven approach to business:

  • Evolving data privacy regulations
  • Increasing consumer awareness

Privacy can broadly be defined as access to individually identifiable personal data.

The GDPR defines personal data as any information relating to an identified or identifiable natural person. It further clarifies that an identifiable natural person is a person who can be identified directly or indirectly, in particular by reference to an identifier such as a name and identification number location data and online identifier or to one or more factors specific to the physical and physiological, genetic, mental, economic, cultural or social identity of that natural person.

Data can be misused online in multiple ways, from banking frauds to identity theft. Users are increasingly aware of importance of keeping their private data safe. They are also aware of their legal rights to data privacy. Enterprises collecting, storing and processing data had it much easier initially. But as people became more concerned about who has access to their private data, governments have woken up to the extent to which private data can be misused in unscrupulous hands.

Data privacy and security guideline and regulations are being drafted and made into law all over the world. GDPR and CCPA are the two most prominent ones but other countries have their own laws. The Data Protection Act 2018 is the UK’s way of making organizations collecting private data responsible for keeping them safe rather than the individuals who provide that data.

Enterprises need to be compliant to privacy regulations of the territory where they operate as well as where their customers reside. These compliances not only pose operational challenges but increase costs as well.

Customers are aware of the pitfalls of data breach. So, they prefer products and services of organizations that are transparent about their data collection, storage and analysis processes. Consumers also want to know who has access to the insights generated by their data.

Customers are just one click away from changing allegiance. So, enterprises need to be transparent about their privacy policies for retaining customer loyalty and acquiring new customers.

Balancing the need for data analytics and privacy

Contrary to widespread belief that data analytics and data privacy are contradictory, it is possible to strike a balance between them. Let us see how.

A CGOC study found that just 57% of organizations train their staff on data protection compliance, and out of those only 25% are regular in their training and audits. This indicates a disjointed approach towards data privacy compliance. The study further reveals that 50% of respondents think that internal staff and practices are the biggest threat to data security as compared to just 38% who think external hackers are the biggest threats.

Data privacy compliance can be achieved only if enterprises do away with their siloed operations and there is continuous collaboration among its IT, security, legal and marketing departments. The focus here must be on utilizing the right tools and technologies to ensure data privacy without increasing business overheads.

People handling private data must be sensitized to the perils of data theft because one weak link in the security chain, from data collection and storage to analysis and distribution of insights, can prove dangerous.

It is possible to protect data being used for Data Analytics projects. The most important and easiest way of doing this is anonymizing data. Most Data Analytics projects are designed to identify patterns and Trends from huge volumes of data. It is not necessary to identify an individual with their personal data to get this analysis. So, anonymizing data can ensure data privacy while still being useful for getting insights.

In fact, anonymizing data renders data protection regulations like GDPR and CCPA invalid. Because these regulations apply on data that can identify an individual. When the individual cannot be identified through the available data, there is no need to implement these data privacy regulations.

This absolves the organization using anonymized data of most of the privacy compliance requirements. All they need to ensure is that the data remains has been collected legally and it remains anonymized during storage and sharing.

If the data is truly really anonymized, it is not even subject to the rights of data owners. This means they cannot request access to their data, ask the data to be removed, or object to processing it in a certain way. Come to think of it, using really anonymized data would be the ideal situation where the business potential of data can be unlocked without compromising privacy of individuals.

Brand trust has never been more important for businesses than it is now. Customers have always been fastidious about which products they choose or which services they use. Recently, Accenture conducted a study that quantified the impact of trust. The study revealed that a USD 30 billion retail company experiencing a material drop interest stands to lose USD 4 billion in future revenue. And, 54% of the companies surveyed experienced a material drop interest over the last two years. So, it’s not a matter of if but when a trust issue will crop up.

Be honest about your data privacy policies

Being honest about your data privacy policies can be an effective way of winning trust of the customers. Building customer trust can also ensure your data quality because people are more honest about their personal data with organizations they trust. Privacy can be used as a differentiator to establish brand loyalty and engage with customers on equal footing.

How TechAhead helps in developing privacy compliant apps

The team of app design and mobile application development experts approach app design with data privacy first approach. They have developed 100+ standalone or integrated data analytics projects that are fully compliant with data privacy regulations.


Data-driven approach to business helps an organization retain competitive edge as it gives access to meaningful operational data across all functions. These data can be used to generate insights that support and strengthen the decision-making process. Businesses have been collecting and analyzing data for ages with the help of feedback forms, telephonic interviews, snail mail, customer interviews, etc. But Internet has made it very easy to collect data globally because it is easier, faster and cheaper to reach customers for feedbacks.

Data driven decision making is informed decision-making, which makes businesses more confident of their future. Taking a data driven approach to business also decreases expenses as claimed by executives in a survey of Fortune 1000 companies.

However, these data driven initiatives have the potential of going against data privacy issues if data collected is not shared, analyzed and distributed responsibly. As people and governments become aware of what could happen to personal data in the wrong hands, data privacy regulations are evolving across the globe. GDPR and CCPA are just two cases in the point.

Besides evolving data privacy regulations, businesses also need to deftly handle the increasingly aware consumer. Consumers are aware of their legal rights to their personal data, even if it has been collected by a business. They know they can demand access to their data anytime and object to how it is processed to generate Insights. All constraints can put restrictions on the amount of data that can be collected and analyzed by organizations.

But data and the insights that it generates is so critical to businesses that a middle path has to be found — a balance between maintaining data privacy and generating useful insights through data analytics. Any organization planning to collect user data and using it for insights must comply with all data privacy regulations wherever it operates and where its customers reside.

Another effective way of maintaining data privacy is anonymizing the data collected, so that persons cannot be identified based on data being used for analytics. If data is truly anonymized, even the provisions of privacy regulations like the GDPR become invalid because they apply only to personally identifying data.

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