A recent study by World Wide Worx shows the slow adoption of artificial intelligence solutions in Southern Africa.

A lack of business intelligence skills is certainly one barrier, but increasing studies are also showing that poor quality data and a lack of local (South African) data sets are also factors that drive up the risk of AI failure, and are inhibiting adoption.

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Shaping the Global Perspective

An O’Reilly survey on trends in AI and ML shows that this renewed interest in data quality and data governance is a global phenomenon and is driven by top executives.

Similarly, a 2019 survey by Precisely showed that, whilst some 70% of the survey respondents felt that their business leaders had enough insights to inform business decisions, other recent industry statistics suggest that only 35% of senior executives have a high level of trust in the accuracy of their Big Data Analytics.

Data Quality Takes Center Stage

If you consider that nearly 50 percent of the respondents indicated both that: 1) their organizations lack a standard data profiling or data cataloguing tool; and 2) that they personally had previously experienced un-trustworthy or inaccurate insights from analytics due to lack of quality, then there appears to either be a disconnect or a difference in perspectives around organizational data quality.

More telling, 75% of the respondents cite data quality as a high or growing priority in their organizations.

This aligns with other industry reports that 84% of CEOs are concerned about the quality of the data they’re basing decisions on. With greater emphasis placed on the ability to respond quickly to customers, to rapidly innovate, and to gain new competitive insights, just having good quality operational data is no longer good enough.

Unveiling the Data Dilemma: Trust and Profiling

The top challenges are neither new nor surprising: many varied sources of data (70%), applying governance processes to measure and monitor data quality (50%) and volume of data (48%) are the top three.

Industry expert Michael Stonebraker noted the first as the “800-pound gorilla in the room” at this year’s Enterprise Data World conference.

Elevated Priorities

75% of the respondents noted large data volume as a barrier to data profiling to gain insight into the data quality issues and subsequently to ensure the quality of the data being used. Whether stored in the data lake or in the Cloud, roughly 20% of the participants cited the quality of that data as “Fair” or “Poor”. Without the ability to gain effective understanding or insight, or to address data quality, it’s no wonder that the recent study by Dimensional Research reports that nearly 80% of AI initiatives have stalled due to data quality issues.

In conclusion, the journey towards AI integration in Southern Africa is impeded by a lack of trust stemming from data quality concerns and the dearth of reliable local datasets. By acknowledging these challenges and committing to robust data governance, organizations can pave the way for a future where AI thrives, driving innovation and success.

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To learn more about why real-time data quality is becoming increasingly important, check out our blog post on User Demand for Real-Time Insight Driving Demand for Real-Time Data Quality

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