Over the last several years, data breaches and ransomware attacks have become increasingly grim realities across all industries, affecting companies of every size regardless of the sensitivity of their data. The Identity Theft Resource Center reported a record 1,862 data breaches in 2021 alone, a 68% jump from 2020. At the same time, ransomware attacks overwhelmed corporate IT teams: Cybersecurity firm SonicWall flagged 623 million ransomware attacks last year — up 105% year over year — including an 1,885% increase in attacks on government targets and 755% on healthcare.

As data has become the lifeblood of modern organizations, and cyberattacks continue to threaten data and customer trust, data management and security have become primary concerns. A recent Reltio survey of top insurance leaders found that companies are facing formidable data-related challenges: 31% of respondents identified data complexity as a top issue, while 27% cited data silos and app proliferation, and 22% flagged cybersecurity concerns. Asked what was most important to their digital transformation efforts, 38% pointed to data security and privacy. 

Some of these challenges result from global shortages of technology talent. Following the “Great Resignation,” IT teams have been stretched thin and stressed out — constantly on high alert for new threats from global hackers. This has limited their ability to focus on important but less-immediate data issues. 

Organizations are also wrestling with shifting consumer purchase preferences, including growing demand for individually customized, Amazon-style services. To improve customer experiences, companies now walk a tightrope, leveraging more personal data as data protection and privacy standards become more stringent. 

Meeting data privacy and security challenges will demand superior data management, including adopting more automation and intelligent operations. Organizations without in-house capabilities will turn to a modern master data management approach, which can protect data and improve its accuracy, usefulness, and regulatory compliance through refinement and streamlining in real-time.

Data Security and Management Challenges in the Modern Hybrid Cloud Era

One major consideration for data security is its geographic location. Across industries, data is partially or completely transitioning from on-premise storage and processing to clouds. Due to its lack of local supervision, cloud data demands better security than on-premise systems — and may indeed be better protected. Yet 66% of IT professionals surveyed by PwC consider security a major challenge to cloud adoption.

Companies considering cloud adoption also must avoid creating low-quality data or data silos. Today’s organizations use many apps to create data, but most apps are unconcerned about data quality and lack data management capabilities. The data they generate is often fragmented, and teams can actually waste time and money fixing serious problems. 

A trusted, agile data foundation is critical for truly transformational business initiatives, enabling operational and analytical systems to effectively achieve their purposes. Organizations armed with timely, accurate, fully connected, and enriched views of data can implement large-scale systems, such as omnichannel experiences, collaborate with partners to bring new products and services quickly to market, or easily comply with users’ data privacy preferences. Without high-quality data as a foundation, successful implementation will be an uphill battle.

Growing Web of Data Privacy Laws

Companies relying on digital footprints for marketing or other activities now face significant challenges. Privacy laws and third-party tracking are rapidly evolving.

Organizations now must comply with numerous data privacy requirements, ranging from regional laws, such as California’s Consumer Privacy Act (CCPA), to multi-national regulations, such as the EU’s General Data Protection Regulation (GDPR). The absence of a comprehensive American national privacy standard has led states to pass consumer privacy laws. According to Reuters, data privacy bills are pending in nearly half of U.S. states and territories, with Virginia, Colorado and Utah, recently following California in passing comprehensive acts.

While these laws are designed to protect individuals’ personal data, they also increase data management complexity for corporations, and well-intentioned efforts to comply with laws may inadvertently increase data risks. As additional regions adopt data privacy laws, companies will be forced to adjust, further straining their data teams and likely increasing their data complexity.

Organizations, therefore, need a big-picture strategy that accommodates every jurisdiction where their customers are, ensuring compliance with everything from global to local privacy laws. A master data management (MDM) approach — a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets — can help ensure that these needs are met by connecting all internal, external, and third-party data.

MDM brings customer data together to create a single, unique profile with a universal ID for each individual. This ID works across applications, establishing a consistent user experience, detecting fraud, and streamlining compliance. Modern MDM technology builds the foundation for all consumer engagements — across web browsers, mobile apps, e-commerce stores, email hosts, digital ads, contact centers, and beyond.

Tomorrow’s Data Privacy and Security Challenges

Since data privacy and security rules are only tightening, wise organizations must prepare for upcoming changes and challenges. This is the moment to strategically invest in data, ensuring it’s generating business value while meeting foreseeable compliance and information security standards. Put another way, companies without clean, connected data are taking a big risk.

Looking ahead, organizations must plan for data privacy and consent management, as well as fraud detection. This means improving the protection of sensitive data, updating old systems, creating a holistic view of customers, and minimizing fraud response delays. 

Risk management practices can go beyond ensuring compliance with data privacy and financial regulations, reducing fraud and losses from claims. That said, risk management practices all depend on access to comprehensive, reliable, and insight-ready data. 

The following five actions will enable organizations to better manage their data:

1. Conduct a data risk assessment. A Gartner survey blamed poor data quality for an average of $15 million in annual losses per company. Datanami’s survey of 500 data professionals revealed that 77% have data quality issues, and almost all said those issues are impacting company performance, including reduced understanding between teams. Less than half said they have high trust in their company’s data; 13% said they have low trust in that data.
2. Consider accuracy, completeness, and time when building out a data collection and implementation. Absent any one of these traits, data cannot enable products or services to be quickly improved for clients. 
3. Collaborate with MDM experts. Choose third-party or in-house experts to improve data collection and enhance implementation of data trends, incorporating key indicators to reduce fraud and improve risk monitoring. 
4. Connect the customer experience with MDM. The siloed nature of customer data potentially reduces revenue and leads to increased churn. Enterprises that identify customers in real time and assign them a single, universal profile can create clean, consistent experiences that satisfy customers across all touch points.
5. Evolve through cross-referencing data. Improve visibility into potential threats and fraud activities and continuously learn from findings.

Companies now spend billions of dollars on digital transformation to stay competitive, including improving information security and regulatory compliance. Success will ultimately be measured in three ways: eliminated security risks, improved data quality, and reduced business costs.