AI Weekly: Data analytics keeps attracting investment through the pandemic

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.

Much of these analytics workloads are 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 desires 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 its over 40 different agencies. The city wanted to take the 240 million records they create daily and use it to correlate along with other city data to gain insights. Specifically, they 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 combine. 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 that the City of Los Angeles is now able to evaluate 100 million threats each month using analytics, and to share the data throughout all 40 of its agencies.

Benefits and barriers

Challenges for some organizations continue to present barriers to adopting data analytics, however. Respondents to the Lemongrass survey reported pegged security and compliance as the top issues facing enterprises when moving legacy systems to the cloud. Separate research by Alatian 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, suffered from a lack of awareness 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 without 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 next-year applications.

Successful migrations to the cloud can lead to a wealth of benefits, surveys show. For example, according to OpsRamp, the average savings from cloud migration come to around 15% on all IT spending. Small and medium businesses benefit the most, as they spend 36% less money on IT that way. Moreover, 59% of companies report an increase in productivity after migrating apps and service 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 would be inhibited.”

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Thanks for reading,

Kyle Wiggers

AI Staff Writer