AI Weekly: Data analytics keeps attracting investment through the pandemic

AI Weekly: Data analytics keeps attracting investment through the pandemic

Organizations are investing more in data analytics during the pandemic, according to a paper from West Monroe. A recent survey by KPMG agrees, with C-suite executives reporting that data and analytics platforms are the most common new technology to be adopted and 21% of respondents reporting their company was piloting AI and machine learning solutions.

Driving the trend is a growing realization that AI can help enterprises maximize returns on investments in tech by mining the most insights from data. For example, AI can be used to spotlight which parts of a website customers are likely to pay attention to or help retailers understand what their customers are likely to purchase.

“A positive trend to come out of the pandemic was that organizations recognized that data was so important,” Amazon VP Sandy Carter told VentureBeat in a phone interview. “A lot of them came to the realization that they needed more insights in order to make the right decisions.”

How analyzing data can help

Indeed, most organizations have to wrangle countless data buckets — some of which have long gone underused. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends. The opportunity cost of this unused data is substantial, with a Veritas report pegging it at $3.3 trillion by 2020. That’s perhaps why organizations have taken an interest in solutions that ingest, understand, organize, and act on digital content from multiple digital sources.

These analytics workloads are largely being processed in the cloud, which offers flexibility in how — and when — they can be executed. IDG reports that the average cloud budget is up from $1.62 million in 2016 to a whopping $2.2 million today. A Lemongrass survey found that IT leaders were motivated to migrate systems by their need to secure data, maintain data access, save money, optimize storage resources, and accelerate digital transformation.

Carter gave the example of Splunk, which worked with the City of Los Angeles to build a data analytics solution for over 40 different agencies. The city wanted to take the 240 million records those agencies create daily and use that data, along with other city data, to gain insights. Specifically, city officials wanted to evaluate cyber threats.

Hacks and breaches have surged in the past year, due in large part to the pandemic. Canalys found that more records were compromised from March 2020 to March 2021 than in the previous 15 years combined. In one scary example, in February hackers attempted to poison a Florida city water supply by remotely accessing a server. And in 2015 and 2016, cyberattacks caused large-scale power outages in Ukraine.

With the help of Splunk and Amazon Web Services (AWS), Carter says the City of Los Angeles is now able to evaluate 100 million threats each month using analytics and share the data throughout all 40 of its agencies.

Benefits and barriers

Some organizations continue to encounter barriers to adopting data analytics, however. Respondents to the Lemongrass survey pegged security and compliance as the top issues facing enterprises when moving legacy systems to the cloud. Separate research by Alation and Wakefield Research found that data quality issues contributed to failed implementations of AI and machine learning.

Carter says that one AWS customer, U.S. Citizenship and Immigration Services, was largely unaware of what AI and data might bring to the table. The U.S. Department of Homeland Security agency, which administers the country’s naturalization and immigration system, expressed an eagerness to apply machine learning models to its data but had no specific goals in mind. With the help of Amazon, the agency determined which insights might be useful to its employees, like the percentage of likely no-shows to appointments and the number of applications in the next year.

Successful migration to the cloud can lead to a wealth of benefits, surveys show. For example, according to OpsRamp, the average savings from cloud migration comes to around 15% on all IT spending. Small and mid-sized businesses benefit the most, as they spend 36% less on IT that way. Moreover, 59% of companies report an increase in productivity after migrating apps and services to the cloud, Microsoft says.

“If you’re going to use analytics and you’re going to use machine learning, the cost structure, on-demand, and agile nature of the cloud makes sense,” Carter said. “A lot of organizations … hustled to get their data to the cloud because they knew that without building, storing, and managing that in a data lake, use of analytics and machine learning would be inhibited.”

For AI coverage, send news tips to Kyle Wiggers — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI channel, The Machine.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

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