February 26, 2018
Tech Vision 2018 Trend 3 —Data Veracity
By: Teresa Tung

Today’s business is more data-driven than ever. But inaccurate and manipulated information threatens to compromise the insights that companies rely on to plan, operate, and grow.

Data is a big-time business generator. In 2017, IDC forecasted global revenues of nearly $151 billion for big data and data analytics practices, up 12 percent from the year before.i However, the days of simply collecting information and archiving it onsite or in the cloud are gone.

Now with autonomous, data-driven decision-making increasing across industries, companies need to assess data integrity and data quality to ensure they’re basing decisions on trusted information. This is the premise of our Data Veracity chapter in the Accenture Technology Vision 2018, our annual tech trend report.

82% of executives state their organizations are increasingly using data to drive critical and automated decision-making, at unprecedented scale.

Source: Accenture Technology Vision 2018 survey

A prime example comes from the oil industry. If hackers access data sources and attempt to shut down an oil rig for a few days, it causes business disruption and lost productivity. But what if those same hackers covertly alter the data, and the oil company spends months drilling in the wrong places? That’s an enterprise-level existential threat.

Siemens is helping oil and gas companies address this issue by partnering with data security vendors to embed intelligence into the sensors installed in industrial equipment.ii The sensors take an aggregate look at the data fed to them to develop a baseline for normal behavior. With this baseline, oil and gas companies can compare deviations and have a better chance of detecting cybersecurity threats and intrusions into their systems.

Business today is more data-driven than ever. Data integrity ensures we’re making decisions on trusted information: #TechVision2018


Grading the truth of data

Fortunately, companies don’t need to accept the risks of poor data veracity. The first step of data governance is to ensure that the right data is being used throughout decision support systems and processes by following three data-focused tenets:

  • Provenance, or verifying the history of data from its origin throughout its life cycle

  • Context, or considering the circumstances around its use

  • Integrity, or securing and maintaining data

Some of the most foundational elements to grade the veracity of intelligent data revolve around ramping up a company’s existing efforts: embedding and enforcing data integrity and security throughout the organization, while adapting existing investments in cybersecurity and data science to address data veracity issues.

Secondly, businesses can build a “data intelligence” practice with the charter of grading the truth within data. This group can work to create, implement, and enforce standards for data provenance, context, and integrity. By making investments in the capabilities to verify the truth of their data sources, companies will generate more value from their data and build a strong foundation for the success of other digital transformation initiatives.

Angles of data analysis: behaviors and manipulation

Grading data will also require developing an understanding of the “behavior” around it. Whether it’s a person creating a data trail by shopping online, or sensor networks reporting temperature readings for an industrial system, there’s an associated behavior around all data origination.

Companies must build the capability to track this behavior as data is recorded, used, and maintained. With this understanding, they can provide cybersecurity and risk management systems with a baseline of expected behavior around data. These baselines will empower companies to detect data tampering that predicates poor decisions.

A data intelligence practice must also work to uncover and address the factors contributing to the creation of false data in the first place. Uncomfortable but true: if a business depends on data collection, they are potentially incentivizing data manipulation.

Individual instances of manipulated data may have minimal impact, but a bevy of deceptions can skew business outcomes. Researchers at the University of Warwick have studied the way some rideshare drivers organize simultaneous sign-offs to cause a shortage of drivers, and trigger surge pricing.iii Knowing that they’re participating in systems managed by algorithms, these drivers are trying to make the system work in their favor—at the expense of rideshare company’s efficiency.

In the same way, every company should consider the ways their processes are incentivizing stakeholders to game their data-driven systems.

High stakes of data quality

As data powers decisions and may be shared and even monetized in partner and external marketplaces, companies will also compete on data veracity. Going beyond contractual agreements, businesses can build-in automated systems to track and monitor how data is used, who consumes it, and for what purposes.

A customer, for example, may be willing to allow her bank to share personal transaction data with other partner businesses so long as it is beneficial to her as well (e.g., receiving a lower rate for insurance). Beyond allowing customers to opt-in, companies that provide provable transparency will create confidence for consumers and business partners to share more data of mutual benefit.

In summary, data is the lifeblood for digital companies, fueling complex business decisions that drive sustained growth. Ensuring the veracity of this data is a cornerstone for technology systems to make autonomous, data-driven actions. Data integrity is what helps drives AI systems to make unbiased decisions; it’s what makes Internet of Things sensors dependable, robotics systems productive and quantum computing powerful. This is what’s at stake with data veracity and why trustworthy data is essential to intelligent enterprise success.

Is your company confident in the data that powers your revenue? Do you have a baseline for data behavior or ways to incentive truth?

To learn more about this IT trend, I encourage you to:

  • Read the Accenture Technology Vision 2018 overview and trend highlights

  • View the essential slide shares, videos and infographics

  • Share your thoughts at #techvision2018

  • Reach out to us to put these innovation-led ideas to work in your enterprise.

i Goepfert, J. (n.d.). Worldwide Semiannual Big Data and Analytics Spending Guide. Retrieved October 13, 2017, from http://www.idc.com/getdoc.jsp?containerId=IDC_P33195
ii Lo, C. (2017, August 14). How an AI-driven industrial immune system could protect oil & gas from cyber attacks. Retrieved August 24, 2017, from http://www.offshore-technology.com/features/featurehow-an-ai-driven-industrial-immune-system-could-protect-oil-gas-from-cyber-attack-5897949/
iii Jones, N. (2017, August 2). Uber drivers are gaming the system and even going offline en masse to force “surge” pricing. Retrieved August 8, 2017, from http://www2.warwick.ac.uk/newsandevents/pressreleases/uber_drivers_are/

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