More than ever before, organizations of all sizes are turning to data to inform strategy and drive growth. In fact, 82 percent of business leaders say their organizations are increasingly using data to drive critical and automated decision-making at scale. It’s little wonder that IDC forecasted global revenues of nearly $151 billion for big data and analytics practices in 2017.

The data explosion we’re seeing today will not let up anytime soon as businesses continue to adopt data-reliant technologies such as artificial intelligence, blockchain, augmented/virtual reality and robotics, to name just a few. Naturally, at the same time the extent of the damage that can be caused by inaccurate or manipulated information grows. Incorrect or falsified data threatens to compromise the insights that companies rely on to plan, operate, and grow.

This data veracity challenge is one that most businesses have yet to come to grips with. In our Technology Vision for Oracle 2018, 79 percent of the business executives we spoke with agreed that organizations are basing their most critical systems and strategies on data – yet many have not invested in the capabilities to verify the truth within it. If we’re to fully harness data for the full benefit to businesses and society, then this challenge needs to be addressed head on.

Oracle, for example, is continuously working to secure the Oracle Cloud Platform and manage its Data as a Service (DaaS) sources by qualifying and cleansing over one trillion terabytes of data (nearly half of the world’s data runs on Oracle databases), anonymizing it before generating and regularly updating a portfolio of more than five billion consumer and business profiles.

In the past year the company unveiled its Autonomous Database, which further maintains data purity by – as the name implies – offering total automation and thereby vastly reducing human error. Steps like these are critical, as data services and websites rely on DaaS to properly analyze their data and provide holistic views of customers.

The three tenets of data veracity

To address the data veracity challenge, businesses should focus on three tenets to build confidence: 1) provenance, or verifying the history of data from its origin throughout its life cycle; 2) context, or considering the circumstances around its use; and 3) integrity, or securing and maintaining data.

Organizations can meet all three tenets by combing existing data science and cybersecurity capabilities into one data intelligence group. This new combined team would:

  1. Ensure the right data is being used throughout decision support systems and processes. This task also involves investigating ways stakeholders might manipulate data for their own benefit.
  2. Imbed and enforce data integrity and security throughout the organization. Adapt existing investments in cybersecurity and data science to address data veracity issues.
  3. Assess data within context – recognizing when data presents findings that don’t fit with accepted knowledge. Some companies are beginning to use data science capabilities to flag data that deviates from a known broader context or accepted employee behaviors.

We recently worked with a European bank that launched a big data program via Oracle technologies, designed to convert data to business value. To ensure data veracity, the company created a data intelligence practice to capitalize on and transfer knowledge and best practices across the organization, identified experts for each area and removing information silos. The new IT architecture provides a seamless, flexible, reliable solution while also freeing staff to concentrate on building relationships and innovation.

As businesses and governments become increasingly reliant on data, they have a duty to ensure the data they use is accurate, relevant and secure. Without these efforts, securing the trust of consumers will become increasingly difficult and the huge benefits that data analytics at scale promises our communities may not be fully realized. All organizations should therefore act now and implement specialized technologies, processes and teams for robust data governance.

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