Poor data quality is hindering AI adoption
A new report reveals that although 84 percent of IT leaders say a Configuration Management Database (CMDB) is essential to driving decision-making and operations, a majority feel their current systems lack the data quality, accuracy and completeness they need, hindering the ability to maximize enterprise AI implementations.
The study from Device42 polled IT leaders across industries including finance, healthcare, government, and technology, and finds that over 50 percent of respondents use CMDBs, monitoring tools, or manual discovery processes, to gain insights about their infrastructure, yet 58 percent report a lack of confidence in their visibility.
Less effort, better business outcomes with data quality and governance
Achieving business success and making informed decisions requires high-quality data that organizations can trust. However, achieving data quality can be complex and time-consuming, forcing data and analytics leaders to choose between supporting business outcomes and ensuring compliance with data privacy and regulatory needs. Here’s how organizations can achieve business outcomes and compliance with regulations and privacy policies.
Data is often referred to as the new oil for a good reason. In today’s digital age, we are generating and collecting data at an unprecedented pace. As Sir Tim Berners-Lee said, "This precious resource will last longer than the systems themselves." IDC projects that by 2025, we will hit 175 Zettabytes of data. However, the challenge for businesses is how to turn this raw data into valuable insights. This is where data quality and governance come in.
Why Data Quality is critical for business
Since the explosion of technology in the last few decades, data has been increasingly positioned as a silver bullet that can fix all the trials and tribulations of the modern world. For those giant tech companies who amassed mass amounts of (mostly) third-party data, data was the new oil -- sold in barrels to any company wanting to find and scale an audience. But KPIs on data effectiveness became increasingly viable, businesses began to question the amount of data they’d bought.
Parallel to this, governments and consumer rights groups became aware of the increasing volume of unwanted noise being thrown at potential clients and customers. Businesses, both B2B and B2C, became liable for data missteps -- case in point with Meta being fined 17m euro for what amounted to bad data housekeeping.