The Rise of Privacy and the Rejection of Big Data

For years now, data has been seen as a sort of currency in many sectors – elevated by its power to make processes and applications more efficient, fuel innovation, and drive long-term decision-making and forecasting.

However, a countermovement is steadily growing in influence – one that rejects the increasing reliance on big data, citing concerns about privacy, data security, accuracy, reliability, and ethics. This has caused some observers to take a step back and ask an almost heretical question: when it comes to predicting consumer and business trends, is bigger really better?

Big Data in Question

The term “big data” refers to the use of extremely large data sets that can be analyzed to reveal patterns, trends, and relationships, particularly those having to do with human behavior and interactions. In many industries, these practices have been ubiquitous and hugely influential. At the same time, the use of big data has been questioned for a range of reasons – some related to doubts about its effectiveness and others prompted by fear of unintended consequences or the prospect of bias, unfairness, and abuse.

Concerns about privacy and data security have become especially widespread, provoking responses from enterprises, from governments, and from the grassroots. Many consumers routinely use privacy tools like VPNs and encrypted messaging apps to protect their personal information. Governments around the world – most notably in the European Union – are taking bold measures to protect individuals' privacy rights. Technology companies and other enterprise players are also adapting to increased consumer concern by revising their data collection and usage policies – though sometimes with mixed results.

Software Developers’ New Focus on Privacy

Software developers, too, are finding a new focus on building software that protects user data and respects privacy. In fact, the global data privacy software market is projected to grow from $2.36 billion in 2022 to $25.85 billion by 2029, at a CAGR of 40.8%.

The drive for new privacy and protection technologies for large enterprises has been driven partly by a demand for better ways to protect businesses as they adopt new IoT and cloud technologies, and partly by stringent measures taken by governments globally to protect data both within and across borders. Together, these and other forces are reshaping the privacy software landscape and underscoring the need for change.

Bias and Ethical Concerns

With much large-scale data collection now handled through automation and AI, ethical concerns about inherent biases that distort those processes – and can lead to misuse of the data collected – are coming to the fore. Too often, such technologies are neither neutral nor transparent as they process data and prioritize results, and the data collected may itself be skewed. Using such data to inform policy decisions or predictions can reinforce biases, entrench prejudices and stereotypes, and generate outcomes that are harmful and unjust.

Reliability and Accuracy

All data is not created equal – and one key concern in the use of extremely large data sets is the quality of the data they contain. Ensuring data quality means ensuring its accuracy, completeness, consistency, timeliness, and relevance. Best practices contributing to data quality include data profiling, which examines data structure and content to assess quality; data validation, which entails checking data against established rules and criteria to verify accuracy and consistency; and data cleansing to correct, remove, or replace data that is wrong or incomplete.

Unintended Consequences (Correlation vs. Causation)

It’s one thing to use, say, changes in search engine queries to spot trends and identify shifts in popular attention. It’s another thing entirely to draw conclusions and make policy decisions based on such data – especially if those decisions are enmeshed in confusion about the difference between correlation and causation. Without a clear understanding of how data relates to causation, the obsession with using big data to generate “actionable insights” can lead to costly mistakes – sometimes with devastating consequences.

Application Scenarios

The rejection of big data has important consequences for stakeholders in cybersecurity, data science, public policy, and other areas. For example:

• Cybersecurity:

  Big data has opened a Pandora’s box of cybersecurity issues, starting with data security. As large organizations pivot to cloud solutions for data storage and analytics, the risks of big data collection and storage – from data breaches to concerns about consumer anonymity and gaps in data-masking – may outweigh the benefits.

• Healthcare:

  Digitization of patient and health data is transforming healthcare business models as well as clinical and operational processes. These changes have driven the creation of adaptive technologies to protect stakeholders and patients – and provided new opportunities for developers to create privacy-focused healthcare technology to prevent breaches and protect information.

• Data Science:

  For obvious reasons, the impact on this sector will be particularly strong. Nonetheless, data science will continue to thrive, although major shifts are expected. For data scientists, concerns about privacy, security, and governance is a serious matter. The more data you collect, the harder it is to protect it, so data scientists must take both transparency and protection into account. Federated machine learning, homomorphic encryption, and explorations of AI’s limits are efforts to address such concerns.

• Marketing:

  These days, most analysis and information used to create targeted marketing campaigns comes from big data. However, given the increasing concern about consumer privacy and new government regulations, the marketing sector will be affected to a significant degree. These concerns affect marketing campaign creation (including SEO, PPC, and social media), data anonymization, and everything from the use of AI in digital marketing to the transfer of key data to partners and vendors.

Business Value

Both government and the private sector face the challenge of balancing data-driven innovation and economic growth against the increasingly obvious need to protect individual rights, privacy, and data security. By prioritizing privacy, organizations can build trust with customers and differentiate themselves from competitors who don’t address these concerns.

One of the main ways businesses can create trust with partners, clients, and consumers is to prioritize privacy and clearly address concerns about data collection and use – including the creation of new software that addresses these concerns. Given today’s increasingly privacy-savvy consumers, companies that make it a priority have a real opportunity for growth.

The overall success of any software company hinges on being able to forecast, prepare, and adapt to trends, developing innovative solutions that address cutting-edge concerns. Adopting strategies to make better informed decisions, prioritizing data quality over quantity, transparency about how data is collected and used, and elevating holistic and human insights are all essential.

Beyond that, businesses should keep in mind that the rejection of “big data” isn’t a rejection of data itself – nor is it just a passing trend. At its core, it’s simply a call for more ethical data practices recognizing the value of human expertise and embracing transparency.

Contact HCLSoftware today for data protection, digital transformation, and more!

HCLSoftware boasts a number of solutions to improve enterprise security and data collection that address privacy concerns such as Sametime, Connections, Leap, Volt MX, and BigFix.

Learn more by contacting us online or calling one of our global offices today.