Article by Peter Vakkas, Accenture’s technology lead for Australia and New Zealand
Businesses today are more data-driven than ever before.
It is estimated that businesses comb through approximately four to five billion data elements a day.
As a workforce, we no longer have the time or the ability to check everything personally but at the same time we can’t afford to make decisions based predominantly on our experience and instincts.
An organisation’s success will depend on how it uses this huge volume of data to drive the most optimum decisions.
Accenture’s Technology Vision 2018 report found that 82% of executives believe their organisations are increasingly using data to drive critical and automated decision-making.
Yet, as artificial intelligence (AI) is used to make more business-critical decisions, inaccurate or manipulated information threatens to compromise the insights that companies rely on to plan, operate, and grow.
So, as businesses continue to use more and more data, how can they improve the accuracy and legitimacy of their data?
When we think of data management, veracity or trust is not always top of mind, but this is all about to change.
While the power of insight through data is known, recent events like the Facebook data breach and impending GDPR laws on consumer privacy are all signalling a focus on the trust element of data.
Businesses are investing heavily in technologies that can help them maximise data-driven insights, but they also need to invest in what’s going into them.
Ensuring the integrity of data is one of the most important challenges in the digital economy. By failing to ensure data integrity, organisations leave themselves open to a new kind of vulnerability - a threat that’s critically overlooked.
A recent Accenture study found 97% of business decisions are made using data that its own managers consider being of unacceptable quality, resulting in business insights and decisions that are of questionable value at best or just “bad” at worst.
This doesn’t need to be the case, the risks of poor data veracity can be managed. Organisations can address this new vulnerability by building confidence in three key data-focused principles:
How can organisations be sure whether their information accurately reflects their reality?
How can organisations guarantee that their data hasn’t been corrupted by malfunctioning assets or tampered with by outside forces?
Grading the veracity of intelligent data can be achieved by refocussing an organisation’s existing strategies: embedding and enforcing data integrity and security throughout the organisation, while adapting existing investments in cybersecurity and data science to address data veracity issues.
These existing capabilities within an organisation’s cybersecurity and analytics practices provide a foundation on which to build a new ‘data intelligence’ practice, which would 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 complicit in incentivising data manipulation.
The new practice will help to build data veracity, and uncover business practices encouraging manipulated data by setting standards for acceptable risk, based on business priorities and implications of automated decisions.
These initiatives will help businesses be confident in their insights, while remaining alert to new potential threats.
Organisations also need to build a strong digital identity foundation as part of their digital transformation. Beyond simply identifying people, businesses will also need to be able to identify devices and processes.
The ability to do so will enable companies to track which system supplied the data, which process created it and whether the data received is as expected.
By merging the separate areas of security operations and anomaly detection, organisations can ensure the integrity, provenance and context of its data, creating a solid foundation for data veracity.
To effectively grade and verify data, organisations need to acquire an understanding of the “behaviour” around data.
This data behaviour can be determined through its origins, whether through a data trail created by online shopping, or sensor network reporting temperature readings for an industrial system.
By building the capability to track and record this data behaviour, organisations can provide cybersecurity and risk management systems with a baseline of expected behaviour around data.
These baselines will equip companies with the insight to detect data tampering that precedes poor decisions.
Data veracity isn’t just about minimising threats. By making these investments, companies will generate more value from their data, establish trust and build a strong foundation for the success of other digital transformation initiatives.
Data has become the key for success of digital companies, fuelling complex business decisions that generate continued growth.
It’s therefore crucial that businesses can ensure the integrity of its data, which drives AI systems to make unbiased decisions; renders Internet of Things applications reliable and robotics systems productive.
Trustworthy data is essential to the operation of these smart technology systems and to the overall success of the intelligent enterprise.
In a world where trust in organisations is diminishing, businesses have a responsibility to evaluate their approach to information governance.
To tackle the issue of data veracity, businesses must assess their data management process and determine whether it’s just another cost item on the IT department or an integral part of how value is created.
How organisations respond to this question will determine their ability to compete, be relevant and build trust in tomorrow’s business environment.
Regardless at which point on its digital transformation journey a company may find itself, the time for data veracity is now.