Over 1/3 of AI and Analytics Projects in the Cloud Fail Due to Data Quality, New Data Reveals
New research from Trifacta uncovers the obstacles to analytics adoption and C-suite priorities
SAN FRANCISCO — Jan. 23, 2020 — Trifacta, the global leader in data wrangling, today released its “Obstacles to AI & Analytics Adoption in the Cloud” report, which reveals key challenges that are hindering organizations from modernizing their analytics processes by leveraging the cloud.
The research, which surveyed 646 data professionals across different industries and titles, examines how organizations are handling the accelerating transition of data to the cloud, the obstacles of data cleaning for analytics and the time constraints they face when preparing data for analytics, AI and machine learning (ML) initiatives. A closer look shows how these challenges are inhibiting the overall success of these projects, the ability to improve efficiencies when working with data, execute on new initiatives and accelerate decision making.
Data Inaccuracy is Inhibiting AI Projects
The time-consuming nature of data preparation is a detriment to organizations: data scientists are spending too much time preparing data and not enough time analyzing it. Almost half (46%) of respondents reportedly spend over 10 hours properly preparing data for an analytics and AI/ML initiative while others spend upwards of 40 hours on data preparation processes alone on a weekly basis. Although data preparation is a time-consuming, inefficient process, it’s absolutely vital to the success of every analytics project. Some of the leading implications of data inaccuracy result from miscalculating demand (59%) and targeting the wrong prospects (26%). Decisions made from data would improve if organizations were able to incorporate a broader set of data into their analysis, such as unstructured third-party data from customers, semi-structured data or data from relational databases.
C-Suite Has Taken Notice
Simply put, if the quality of data is bad, analytics and AI/ML initiatives are going to be worthless. While 60% of C-suite respondents state that their company frequently leverages data analysis to drive future business decisions, 75% aren’t confident in the quality of their data. About one-third state poor data quality caused analytics and AI/ML projects to take longer (38%), cost more (36%) or fail to achieve the anticipated results (33%). With 71% of organizations relying on data analysis to drive future business decisions, these inefficiencies are draining resources and inhibiting the ability to glean insights that are crucial to overall business growth.
Rise of AI and ML Push Cloud Adoption
The benefits of the cloud are hard to overestimate in particular as it relates to the ability to quickly scale analytics and AI/ML initiatives, which presents a challenge for today’s siloed data cleansing processes. There are many reasons for widespread cloud migration with 66% of respondents stating that all or most of their analytics and AI/ML initiatives are running in the cloud, 69% of respondents reporting their organization’s use of cloud infrastructure for data management, and 68% of IT pros using the cloud to store more or all of their data — a trend that’s only going to grow. In two years from now, 88% of IT professionals estimate that all or most of their data will be stored in the cloud.
“The growth of cloud computing is fundamental to the future of AI, analytics and machine learning initiatives,” said Trifacta CEO Adam Wilson. “Unfortunately, the pace and scale at which this growth is happening underscores the need for coordinated data preparation, as data quality remains one of the largest obstacles in every organization’s quest to modernize their analytics processes in the cloud.”
Trifacta conducted a global study of 646 individuals who prepare data. The survey was conducted between Aug. 20, 2019, and Aug. 30, 2019, in conjunction with ResearchScape International.
Download a free copy of the 2020 Obstacles to AI & Analytics Adoption in the Cloud report here.
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Trifacta is the global leader in data wrangling. Trifacta leverages decades of innovative research in human-computer interaction, scalable data management and machine learning to make the process of preparing data faster and more intuitive. Around the globe, tens of thousands of users at more than 10,000 companies, including leading brands like Deutsche Boerse, Google, Kaiser Permanente, New York Life and PepsiCo, are unlocking the potential of their data with Trifacta’s market-leading data preparation solutions. Learn more at trifacta.com.