Simon Rolph, CEO and Founder of Such Sweet Thunder

April 2021 Data Quality: Is AI really the future of data quality?

Simon Rolph, CEO and Founder of data management firm, Such Sweet Thunder explores artificial intelligence’s current role within the data management space and its potential, as businesses look to bounce back towards a brighter, more positive future.

For years the technology community has debated the viability and effectiveness of artificial intelligence (AI). On one side of the argument, you have those that place AI on somewhat of a pedestal and herald it as the next stage of our technological revolution. On the other hand, you have the sceptics who see AI as nothing more than machine learning (ML), suggesting that true artificial intelligence and the ability to create a machine to “think for itself” is best reserved for Science Fiction.

Regardless of which side you sit on, the fact is that AI is being used across countless sectors and being implemented in ways that reduce the need for humans to carry out tasks manually. Merging this technology with data management is not a new concept. In fact, AI, or machine learning, can be used for several different management duties. In particular, the technology can play a significant role in ensuring organisations have access to quality data. AI’s future within data management seems promising, from identifying data patterns to removing duplicate or correcting inaccurate data points.

The importance of quality data
There are several aspects to consider when defining data quality, including; its consistency, accuracy, integrity, and completion. Yet, no matter how you choose to define quality data, the implications of having good or bad data quality are apparent.

Data management is a crucial aspect of business efficiency, helping organisations stay connected with their customers or teams and make well-informed, business-critical decisions. However, these resolutions can only occur when an organisation has access to good quality, complete data. Without it, many of these decisions would mostly be guesswork and the equivalent of a CEO making a company-wide commitment based on their horoscope.

Therefore, organisations need to ensure they retain high-quality data by implementing an effective data management strategy. This allows businesses to improve their data quality, ensuring that the information they hold is current, accurate and can be quickly processed and analysed. To put it simply, good data is data that meets the organisation’s requirements and is expected, free from errors and inconsistencies.

However, data that does not meet these specifications is considered bad data and can contribute to data losses, missed opportunities, security breaches, and business and personal information theft. Research suggests that poor data quality could be costing businesses more than 30% of their revenue. Around 20% of company databases contain poor quality data, with a mere 3% of business leaders considering their organisation’s data quality acceptable.

The significance of holding good quality data is widely known across the business community. Yet, based on these statistics, it seems that obtaining accurate data to enable smarter decision-making is easier said than done. One of the most noteworthy challenges many businesses may face when it comes to managing their data could be that even in 2021, we are still relying on CIOs and CTOs to manually manage an organisation’s data.

A report by Snaplogic found that 42% of data management processes that could be automated are delivered manually. As a result, the report found that 93% of the IT decision-makers surveyed said they would need to improve the way their organisation collects, manages and stores their data. What’s more, with many companies choosing to manage their data manually, they also leave themselves open to one of the biggest challenges within the IT sector – Human Error. According to research from Insurance Firm Gallagher, 3.5 million businesses across the UK have suffered from a breach of security or cyberattacks from negligence with 60% claiming it down to human error.

AI and the current data landscape
To overcome the roadblock that comes from human error and data mismanagement, many organisations have already started looking at ways to better utilise existing technologies, with artificial intelligence being one term that is thrown around the most. However, as some critics would highlight, artificial intelligence is nothing more than automation tools using machine-learning technologies to carry out mundane, repetitive tasks in its current state.

Yet, no matter how you perceive this technology, the truth is that implementing it within your data management strategy will only improve the quality of the data your organisation has. More companies are looking toward automation tools that enable businesses to collect, store, manage and analyse quality data.

AI has become increasingly sophisticated within the past few years, enabling businesses to identify data patterns; and remove duplicated and self-correct insufficient data. Yet, we are still just scratching the surface with this technology and its data management capabilities. We can expect to see it continue to evolve and establish itself as a key management tool.

Gartner predicts that automation will reduce manual Data Management tasks by 45% by as soon as 2022; enabling businesses to save time and money, enabling CTOs and CIOs to spend their time on business-critical tasks.

Data is one of the most significant resources for an organisation, however, with the current climate rife with uncertainty globally, it has never been more critical for companies to protect and leverage their greatest assets. With human error playing a pivotal role in data quality and mismanagement, more and more organisations are turning to the latest technologies to let automation and AI analyse their data and make the most of what they have.

The future of AI and data quality
While AI is a long way off from reaching its full potential, we can assume that it will have a lasting impact within the data management industry. In fact, Gartner recently published a report that suggested faster, smarter and more responsible AI was a key trend that data and analytics leaders should be focusing on. As the technology becomes more robust and able to carry out more complex tasks, AI could be in the position to revolutionise how organisations manage data and have access to the quality data needed to make business-critical decisions.

The report states: “By the end of 2024, 75% of enterprises will shift from piloting to operationalising AI, driving a 5X increase in streaming data and analytics infrastructures.”

During the current pandemic, AI has been a paramount tool for data analysts as they utilise the technology to predict, prepare and respond to the global crisis. AI techniques such as Machine Learning and Natural Language Processing are utilised to provide significant insights and predictions on how the Coronavirus is spreading and determining how successful the Government’s countermeasures have been through each lockdown or tier system.

No matter which side of the argument you may sit with regards to AI, the unprecedented landscape has meant that the technology has been placed at the forefront of peoples’ minds.

As the world looks towards emerging beyond COVID-19, artificial intelligence will play a key role in resetting businesses. When people return to the workplace, many organisations will look at ways they can most effectively bounce back to pre-COVID times. We can expect that many will place a significant amount of this responsibility on AI to collate, analyse and manage their data.

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