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Data and analytics vendors have slowly been adding automation capabilities to their platforms over the past couple of years, and in the second half of 2022, automation is likely to become a major BI trend.
Many vendors have effectively enabled organizations to automate much of the cumbersome data management process. In addition, automated machine learning (autoML) has been an emphasis of late.
Before vendors like Alteryx, among others, added automated data management capabilities, data had to be manually loaded, cleansed and organized so it could eventually be analyzed and used to inform decisions. And before other vendors like Qlik added autoML capabilities, data scientists had to manually oversee every aspect of the development and training of machine learning models.
Both processes were onerous, dominating the time data engineers and data scientists spent working with data and limiting the time they could actually examine data models and conduct analysis.
Now, however, automated data management capabilities and autoML enable data workers to do more than just prepare data and train models.
Yet at the BI level when data consumers visualize and analyze their data and take subsequent actions, there remains little automation. Analysis and action remain largely a human endeavor. But BI won't remain so forever, and a trend in analytics that may emerge during the second half of 2022 is the advancement of automation at the BI level.
"Every other aspect of [information technology] has been affected by automation, and it's coming for BI now," said Donald Farmer, founder and principal of TreeHive Strategy.
Industry insiders also predict BI trends over the final months of the year will include an emphasis on data security and governance as organizations enable more employees to work with data, an acknowledgement of the importance of people analytics amid the Great Resignation, and a gulf to emerge between data-driven organizations and those that have yet to make effective use of their data.
Donald FarmerFounder and principal, TreeHive Strategy
Process automation leads to efficiency.
And as it relates to analytics, automation has mostly been about automating processes in order to reduce the burdens of repetitive tasks on employees and free them to do the types of analysis that lead to insight and organizational growth.
In addition to data management and machine learning, anomaly detection is another area in which automation has already gained momentum, with vendors such as Sisu automating the process of discovering anomalies in data and explaining why they occurred.
Data governance is still another part of the analytics lifecycle benefiting from automation with organizations able to program guidelines about who can access what data and to what degree they can use that data.
Beyond efficiency, process automation leads to accuracy.
While not foolproof, machines tend to reduce the risks of human error, which can mount when someone has to do the same thing over and over again and perhaps suffers a lapse in concentration.
The combination of efficiency and accuracy, meanwhile, results in cost savings. Organizations don't have to allocate resources to perform repetitive tasks and they don't have to pay the price caused by human error.
But when it comes to viewing data and making sense of it, the work largely remains a human task.
Data can be automatically uploaded into dashboards and data models, but people are the ones looking at those dashboards and models and interpreting their meaning, then using that interpretation to inform decisions that can affect their entire organization.
Eventually, that will change and at least some of the insight generation will be automated.
"The next trend that might start to get interesting is automation," Farmer said. "Automation is coming to BI, without a doubt. That's been a little slow -- and we have things like autoML -- but automation of the entire BI process is really becoming a big topic."
Some vendors are already attempting to automate analysis and insight through emerging technologies like data storytelling, which is the automated generation of narratives about data so that business users can read an interpretation that is essentially an explanation of the data they're viewing.
For example, Tableau recently released Data Stories, a tool developed as a result of Tableau's 2021 acquisition of Narrative Science. Yellowfin, meanwhile, first introduced Yellowfin Stories in 2018 and has updated its capabilities since.
But the BI trend of automation won't just stop at the generation of insights. It will progress to the next step of turning that insight into action, according to Boris Evelson, an analyst at Forrester Research.
In a 2022 report titled "The Future of Business Intelligence" he co-authored with fellow Forrester analysts Srividya Sridharan, Cinny Little and Fayzan Sabri, Evelson wrote that organizations should begin to automate operational decisions.
For example, the approval of a credit card application or insurance claim can be automated using existing technologies, he noted. Because the approval and denial of credit card applications and insurance claims are based on a specific set of data criteria, they are ripe processes for automation.
Like the analysts, Dan Sommer, senior director and market intelligence lead at Qlik, expects more automation as part of the BI process.
In particular, by enabling applications and analytics platforms to communicate via integrations, organizations will be able to automate decisions and actions.
"Application automation will add the last mile of analytics," Sommer said. "Application automation … helps interweave and trigger sequences of events -- for example from your dashboard -- with or without human involvement."
For example, a Salesforce dashboard could be programmed to automatically send an email to sales representatives who need to know certain information at a certain moment.
"Those sequences and actions are only limited by your own imagination," Sommer said. "[Automation] will really help with tactical actions to close that last mile from insight to action."
Security and governance
While automation at the insight level is one BI trend gaining momentum as 2022 progresses, an added emphasis on data security and governance measures is another.
The COVID-19 pandemic heightened the need for analytics.
Circumstances -- both from an economic and health perspective -- suddenly started changing quickly in the months after the onset of the pandemic, requiring organizations to act and react quickly. And that led to a surge in the use of analytics that has continued as events such as the war in Ukraine and economic conditions like rising inflation and a falling stock market have resulted in continued uncertainty.
That surge in the use of analytics, however, sometimes came before organizations had a chance to put in all the safety guardrails necessary to ensure the proper use of their data.
Data governance is a critical aspect of analytics. It balances risk while enabling users to confidently work with data, and without a proper data governance framework, organizations are at risk of violating regulations and data breaches.
Many organizations are now behind schedule on data governance, and will make that an emphasis as 2022 progresses, according to Sommer.
"Largely driven by necessity, organizations needed to make drastic moves to keep the lights on," he said. "Overall, this is positive, as it's driven transformations at organizations to become more modern and data-driven while overcoming inertia, red tape and regulations. [Now,] security and compliance teams are playing catch-up, because not doing so can carry consequences."
One grocery store chain in Sweden, where Sommer resides, suffered the consequences of a lack of data security.
Coop Forum suffered a ransomware attack in 2021 and had to close 500 of its more than 800 stores for several days when the attack shut down its point-of-sale system cash registers and its self-service checkout software stopped working.
Other organizations, meanwhile, have had to pay millions of dollars for regulatory violations.
For example, Amazon paid $877 million in 2021 for violating Europe's General Data Protection Regulation, while Google, Facebook and Marriott are other organizations that have run afoul of GDPR guidelines.
"Regulations are now conflating data management, data privacy, data security, as well as identity and access management," Sommer said.
Meanwhile, a rising trend is that BI vendors are prioritizing data security and governance as well.
Data management vendors like Alation, Collibra and Informatica have long made data security and governance capabilities part of their platform. Now, with more organizations recognizing the value of self-service analytics, traditional BI vendors are adding similar capabilities.
Many have expanded beyond being strictly BI vendors and added data integration and data management tools to offer a full-featured data and analytics platform. Qlik added such capabilities through acquisitions, while others like Tableau and Tibco have done so mostly through product development.
And now, they are beginning to prioritize data security and governance.
In December 2021, Tableau's platform update featured centralized row-level security to enable data administrators to easily set parameters on individuals' access to data. Earlier in 2021, Microsoft Power BI received a security enhancement. And security and governance feature prominently on Qlik's roadmap, with the vendor aiming to provide industry-specific frameworks that will enable organizations to more easily establish security and governance guidelines.
"The more organizations embed analytics, trigger actions, and share APIs and data, the more they need to protect against [data security and governance] failures," Sommer said.
One of the surprising developments over the past couple of years has been the "Great Resignation," the higher-than-usual number of employees voluntarily leaving their jobs in search of a better quality of life.
As a result, people analytics is becoming more important than ever and will be a major BI trend throughout the latter half of 2022 as organizations attempt to hold onto key personnel and reduce overall turnover, according to Cindi Howson, chief data strategy officer at ThoughtSpot and host of The Data Chief podcast.
When making use of data, organizations generally focus on financial numbers. Less top-of-mind is data about employees and monitoring their satisfaction.
But organizations that fail to keep track of how their employees feel -- especially with events like the pandemic, social justice movement, war in Ukraine and struggling economy psychologically affecting people -- risk losing employees at higher rates than organizations that prioritize employee well-being.
"People analytics historically is the forgotten child within a data and analytics team," Howson said. "In the Great Resignation, you can't do that. It's about taking care of your people. And with the social injustices of 2020 continuing, it's important to look at diversity and inclusion, not just from a point of where you stand today but the leading indicators."
Meanwhile, all the BI trends, both current trends such as enabling users to action directly from their insights and the ones that will shape the near future like automation and an emphasis on security and governance, will accelerate a growing data disparity phenomenon.
That is the increasing gap between the organizations that are truly data-driven and those that continue to rely on basic instinct to make key decision, according to Sumeet Arora, chief development officer at ThoughtSpot.
Even people analytics will play into that gap.
Organizations that use data to learn more about employee satisfaction will be able to take more proactive action to retain personnel and reduce costly turnover. Meanwhile, those that don't actively communicate with employees to understand their feelings risk having to fill positions over and over and repeatedly spend on the recruitment and training of new employees.
"As everyone gets empowered with data, inherently there will be autonomous organizations that will be differentiated from organizations that don't make the jump," Arora said. "The ones that have this are the organizations that will be more autonomous, and they'll thrive more than the others. I think that is going to really play out."