There is a pervasive challenge confronting businesses across all industries: dependence on poor-quality data. Besides holding organisations back from embracing emerging technologies, the cost of bad data also impacts the bottom line, with research showing that enterprises building AI programmes on poor-quality data are losing, on average, six per cent of their global annual revenues, equivalent to 406 million USD.
This poor data quality is the result of ineffective data processes, such as siloed data. A staggering 68 per cent of organisations still struggle with the basic task of accessing and cleansing data into a format that is usable for analysis and more advanced use cases such as building LLMs and AI.
The challenge of data inaccessibility is particularly prevalent in more established enterprises relying on traditional on-premises servers. If these companies are to undergo successful digital transformation and keep pace with innovation, they must make their databases work for them, rather than against them.
The untapped potential of legacy databases
Organisations pre-dating the cloud-native era hold vast troves of valuable data in legacy databases, including important customer records, financial and inventory data and even regulatory information. However, without the technological capabilities to move, operationalise and make use of the data in real-time, the insights lie dormant – causing organisations to miss out on opportunities for operational improvements, product innovation or customer-centric growth.
Within these organisations, data engineers are tasked with working tirelessly to keep data flowing between databases, sources and analytics engines, but with the pipelines transporting the data breaking multiple times a month, data talent is fighting a losing battle. When the flow of data is interrupted, employees – not just data workers but marketing teams, sales and even the C-suite – are forced to make business decisions based on outdated or inaccurate insights. The opportunity cost of this kind of disruption is only eclipsed by the financial cost – for example, Thomson Reuters loses $1m when its pipeline goes down.
Legacy infrastructures also lack the modern safeguards required to comply with data privacy, security and governance requirements, exposing organisations to the risk of data breaches, fines from the ICO and reputational damage.
Enter automation
Improving data access is in everyone’s interest in the organisation, none more so than CDOs, who tend to have a relatively short tenure of around 30 months and will therefore look to make an impact upon joining their organisations. Whatever digital transformation efforts they set their sights on, making data accessible, reliable and secure will be central to their activity.
A recent survey by MIT Technology Review conducted for Fivetran further underscores this, showing that 82 per cent of C-suite executives now prioritise data integration and data movement solutions for long-term foundational AI success.
This is where automation comes in. As enterprises move to the cloud to simplify their architectures, automated data movement can help them replicate business-critical databases, so that they can take advantage of more advanced operational and analytical cloud use cases.
Historically, replicating these databases had a major impact on business systems – slowing everything down to a crawl. Today, automated, high-volume data pipelines can not only give decision-makers real-time access to this data in the systems they need it; the same technology can also automate the maintenance and repair of data pipelines – freeing up data engineers to focus on value-added tasks instead. These capabilities make database replication low-impact and low-latency – leading to more reliable data and faster business decision-making.
The role of data governance
Unfettered access to data is key to business applications running smoothly – but that doesn’t mean all staff need access to all the data. In fact, ‘data democratisation’ is about giving access to the right people, to the right data, at the right time.
It is important that there are strong data governance policies and processes following the path of data as it moves – for example, that personally identifiable information (PII) is masked and that only those with the right permissions can see it. Building and managing these features manually is a gargantuan and potentially error-ridden undertaking.
As data protection regulations vary around the world, enterprises trading internationally will need to adopt data movement tools that grant them the ability to set up automatic guardrails for data movement and scale them as regulatory landscapes evolve.
Keeping a competitive edge
The future waits for no one. Companies are already maximising the opportunities offered to them by cloud computing, Machine Learning and AI. In fact, according to Fivetran’s latest research, nine in ten organisations are using AI/ML methodologies to build models for autonomous decision-making, and a quarter of organisations report that they have already reached an advanced stage of AI adoption where they utilise AI to its full advantage with little to no human intervention.
To compete, more established businesses need to kick their data processes into higher gear and focus on modernising legacy infrastructures, so that vital data is more readily accessible and reliable enough to underpin key business decisions. Leveraging automation in database replication today will help businesses unlock greater profitability and scalability, without security considerations keeping CDOs up at night.
Stephen Mulholland is Regional VP EMEA at Fivetran. He is an experienced SaaS leader with intricate knowledge of Enterprise and Commercial Operations, Alliances & Business Development, Start-Ups & Pre IPO Scale-Ups.