The COVID-19 pandemic has revealed the importance of an agile analytics program, and that the need to act and react quickly will likely continue long after the coronavirus is eventually brought under control.
To build an agile analytics program, meanwhile, takes an organizational strategy, and according to speakers during a recent webinar hosted by MicroStrategy, two keys to that strategy are a deployment in the cloud and a platform that enables self-service analytics.
"There's a need to succeed in an environment where there are significantly more unknowns than any of us have ever operated with," said Hugh Owen, executive vice president and chief marketing officer at MicroStrategy.
Before the pandemic, organizations had long-term plans outlining expectations and roadmaps for the next three years, or next five years.
The pandemic, however, rendered those plans meaningless and forced organizations to discard rigid preparations and become nimble.
"Nobody anticipated the changes to work, the changes to markets, the changes to how we execute, and those changes continue on," Owen said. "How organizations continue to meet and exceed goals in an environment where so much is unknown is by investing in modernizing their approach to analytics and investing in platforms that are built with agile in mind."
In order to build an analytics platform with "agile in mind," it has to be deployed in the cloud, Owen said.
"A cloud infrastructure allows customers to meet needs they don't yet know they have," he said.
Hugh OwenExecutive vice president and chief marketing officer, MicroStrategy
A cloud infrastructure provides speed, compute power and security that on-premises analytics deployments can't match.
Public clouds such as AWS, Google Cloud and Microsoft Azure are updated constantly with the latest technology, and users reap the benefits of those updates immediately and without having to do any maintenance of their own.
On-premises infrastructures have to be built and maintained internally by IT staff, taking up extensive amounts of IT professionals' time that could otherwise be spent on analytics projects aimed at improving the business.
In addition, organizations using SaaS versions of analytics platforms receive updates from their vendors as soon as they're ready, and the updates don't require any maintenance. Meanwhile, cloud security concerns have abated and public clouds have generally been shown to be more secure than on-premises deployments.
The result of a cloud deployment, therefore, is an analytics program that enables organizations to develop and complete analytics projects using the latest capabilities while also freeing data workers from tedious maintenance tasks.
The result is more agility than an on-premises deployment, according to the independent analytics vendor.
"Data creates a competitive advantage when leveraged, positioned and distributed in effective and impactful ways and can create the difference between success and failure in the market," said Bill Reidway, vice president of solutions management at MicroStrategy.
And increasingly, organizations are recognizing that the most effective and impactful ways to position and distribute data are with a cloud-based or hybrid analytics deployment, he continued.
According to Reidway, a McKinsey survey showed that one out of every three organizations accelerated their migration to the cloud over the past year.
In addition, a report from Synergy Research Group in March 2021 showed that spending on cloud infrastructure services increased 35% in 2020 to $130 billion, compared with a decline of 6% to less than $90 billion spent on on-premises data center hardware and software.
In 2019, spending on cloud and on-premises infrastructures were nearly even, and on-premises spending outpaced cloud spending every year before 2019.
"The more time organizations spend on developing high-quality and impactful analytics content, the better off they're going to be," Reidway said.
Regarding the cost of migrating to the cloud, he added that cloud deployments have the potential to reduce analytics expenses by 30% to 35% and pay for themselves within 18 months.
Business intelligence vendors have recognized the importance of the cloud in creating an agile analytics program with -- among others -- MicroStrategy introducing a SaaS version of its platform just before the start of the pandemic, Qlik making its cloud version a priority beginning in the spring of 2020 and ThoughtSpot overhauling its platform to become cloud-first starting in the fall of 2020.
In addition to a cloud infrastructure, self-service analytics can help make an organization truly agile.
When data experts -- trained data scientists and data analysts -- are the only employees working with data, timing can be a challenge.
When coding knowledge is required to work with data, and only data experts are allowed to build and delve into the reports, dashboards and models that lead to data-driven decisions, the rest of the organization is forced to wait until those data experts disseminate their findings.
While sometimes that might only be a matter of hours, depending on how many data experts are employed and how many requests they receive, it could also take weeks or perhaps months to get to any given project and its resulting insights.
Within those weeks or months, however, whatever information was needed at one time may become outdated.
Now, BI platforms are beginning to enable business users to query data without requiring them to write code, and use augmented intelligence and machine learning capabilities to do some data modeling and assist in the interpretation of data.
"You want to promote self-service and give power and freedom to the end user," said Patrick Archer, a staff sales engineer at MicroStrategy. "Modern applications should empower end users through self-service so they can conduct analysis on their own."
That's particularly important with so many people now working remotely and unable to collaborate or ask questions as easily as they could in an office environment, he continued.
"With users and teams now dispersed, it's actually a good opportunity to offload certain tasks," Archer said. "You need to still have governance and a user structure, but promoting self-service and promoting people to take action on their own can enhance productivity and efficiency."
The concept of self-service analytics is not new, and vendors have enabled some level of self-service analysis for years.
True independence, however, has not yet been achieved, and it's estimated that only about a quarter to one-third of employees within most organizations use analytics. But advancements in AI such as natural language processing and data storytelling could change that and make the vision of self-service analytics more fully realized, according to Mike Leone, an analyst at Enterprise Strategy Group.
"It's all about empowering the end user," he said.
Meanwhile, to be truly agile, business users enabled by self-service analytics on cloud-based platform need to be able to access and receive insights in real time no matter where they are, according to Owen.
Whether they're out in the field on a mobile device, at their desk working in a business application like Salesforce or SAP, or having a conversation on collaboration platform like Slack, they need to be able to have access to data and analytics to make business decisions at that exact moment.
"There's a set of innovative new ways of delivering intelligence to an individual every minute of every single day that empowers them to be much more effective than they've ever been before," Owen said. "That allows them to meet the higher standards that have been set in response to the work conditions that have changed over the last couple of years."
Enterprise Strategy Group is a division of TechTarget.