2024 makes a turning point for organizations.
With generative AI being the ‘greatest technological advancement since the internet’, there is an impending pressure on businesses to get on the AI bandwagon as fast as they can. Data engineers, information managers, and business intelligence teams are racing against time to integrate AI into their day-to-day operations. However, amidst all the hoopla, a key challenge is being overlooked:
Data quality remains a major concern. Up to 75% of AI initiatives fail due to poor data quality, rendering these investments ineffective.
To harness the full potential of AI, businesses must first address their data quality challenges. According to Sara Hanks, the Director of Continuous Improvement at Wabtec Corporation, “While data quality is important to build AI, AI can play a role in improving data quality.”
By adopting key data quality trends of 2024, organizations can ensure that the data they feed into AI models is accurate, consistent, and reliable. Moreover, this foundation of high-quality data will empower businesses to be truly data-driven
Here are our top five data quality management trends predictions for 2024!
Trend 1: No-Code AI to Solve Dirty Data Challenges Faster & With More Accuracy
The adoption of no-code data quality tools has been slow primarily due to legacy practices. It’s baffling to hear some organizations still expect their brightest minds to spend 80% of their time manually cleaning and consolidating data. In the age of big data where a small business can generate anywhere from 10MB to 1 GB data per day, it no longer makes sense for teams to spend hours on data quality tasks. That’s where no-code AI platforms can be leveraged. Offering user-friendly interfaces, and AI-powered data cleaning and matching abilities, these tools take away the grunt work of data quality.
No-code AI makes it possible for tech and business teams to deal with dirty data without having to use multiple tech stacks or code builds. Platforms like WinPure, Open Refine, Akkio offer no code data cleaning, data matching, and data preparation without requiring a single line of code, saving businesses up to 80% of their time. This shift towards no-code data quality has several beneficial outcomes. Data engineers can move away from ‘cleaner and fixer’ roles to more strategic roles, businesses can build data governance frameworks, resolve complex data quality issues faster – and consolidate data from disparate or isolated sources with minimal effort.
Additionally, no-code AI tools empower business users to take ownership of their data, enabling them to cleanse and prepare data for analysis without relying on IT. This fosters a culture of data-driven decision-making, where business users can directly access and utilize high-quality data to drive informed decisions.
Trend 2: AI Entity Resolution Will Revolutionize Data Management & Analytics
AI entity resolution transcends traditional entity resolution methods by integrating advanced machine learning algorithms and natural language processing to identify, link, and consolidate data entities across diverse datasets. For example, as AI learns data patterns from large volumes of data, it can automatically detect if ‘John Smith’ and ‘Jonathan S. Smith’ are the same person. This capability not only speeds up the resolution process significantly, from days to minutes but also improves over time through continuous learning, adapting to new data and variations with increasing precision.
The impact of AI ER is profound; from more accurate fraud detection to meeting complex regulatory compliance effectively, from in-depth customer analytics to tailored marketing strategies – companies can accelerate data-driven innovation faster, and with greater efficiency. As data continues to grow in volume, variety, and velocity, AI-ER stands as a revolutionary technology amongst other data quality trends of 2024, making it an indispensable function for organizations aiming to harness the true power of their data in the years ahead.
Trend 3: Data-as-a-Service for Small Teams & Businesses
Small-to-mid-sized businesses, such as online e-commerce marketplaces, agencies, tech startups, and B2B platforms struggle with poor data quality due to limited resources, technology, and expertise. The lack of data management and relevant expertise causes these businesses to make poor decisions off the back of poor data, leading to legal, financial, and reputational losses.
DaaS emerges as the much-needed solution for small teams and businesses that need help with data quality but don’t have the resources to invest in expertise or infrastructure. DaaS empowers business users or one-man-data teams to use a cloud-based platform to clean, standardize, dedupe, and consolidate data, without the need for programming expertise.
This shift to DaaS platforms frees up valuable resources, allowing small teams and businesses to focus more on core operations and strategic growth. Moreover, because DaaS platforms offer scalable solutions, businesses with tighter budgets (such as startups) can still work on their data quality without the burden of having to outsource to third-party consultants.
In essence, DaaS transforms the data management landscape for SME businesses – turning data from a cumbersome challenge into a manageable asset.
Trend 4: Data Governance Becomes a Key Priority to Meet AI Challenges
As companies embark on AI initiatives, they are opening themselves up to GDPR and compliance risks. Data governance is a priority now that AI has crawled its way into day-to-day operations in departments like marketing, sales, and business development. To ensure these departments are not at risk, organizations have begun to prioritize data governance frameworks.
Over the years, data governance has turned from an IT function focusing on cataloging and managing data storage, to becoming a necessary business strategy encompassing data quality, privacy, and compliance.
Now with the emergence of AI, along with stringent data protection laws like the GDPR, companies are under heavy regulatory pressure to implement robust data governance policies. Poor data quality is no longer just a technical issue, it is also an issue that can lead to serious security risks, customer data privacy violations, and compliance with external stakeholders.
Businesses that have not implemented data governance or data quality parameters will have to do so before investing in an AI initiative.
Trend 5: Real-Time Data Quality Monitoring as an Emerging Technology
With AI and ML initiatives in the pipeline, companies need real-time data quality monitoring. Unlike traditional batch or periodic checks, this technology continuously scrutinizes data streams, instantly flagging anomalies and inconsistencies before they wreak havoc on downstream processes.
Imagine catching typos in a stock quote seconds after they appear, or detecting sensor malfunctions in an automated factory line before production grinds to a halt. This proactive approach not only bolsters trust in data-driven insights but also minimizes downtime and safeguards against costly errors. 2024 promises to be the year this technology leaps into the mainstream, empowering businesses to harness the power of clean, reliable data in real-time, ensuring smooth operations and agile decision-making.
2024 promises a dynamic shift in the dqm space
The data quality landscape in 2024 promises a dynamic shift. Businesses are moving beyond siloed approaches, embracing trends that democratize and elevate data hygiene. Firstly, no-code solutions empower citizen data champions to identify and address inconsistencies, while AI entity resolution acts as a data whisperer, unearthing hidden connections and enriching insights. Secondly, Data-as-a-Service delivers tailored solutions on-demand, making quality accessible to all. Finally, robust data governance ensures responsible data stewardship, building trust and minimizing risk. Buckle up, because 2024 is the year data quality sheds its burden and becomes a strategic advantage, propelling us towards a data-driven future fueled by accessibility, automation, and boundless potential.