Self-Service Analytics And The Illusion Of Self-Sufficiency

Author

Brent Dykes

November 17, 2016

fountain_drink

Similar to soda fountain machines, self-service BI platforms enable more people to easily access information they need on their own. However, self-service analytics doesn’t mean companies are excused from the responsibility of managing and maintaining these analytics systems over time. (Photo/Brent Dykes)

After years of business users being dependent on busy, understaffed IT teams for all their reporting needs, a shift has occurred. Tired of the inflexibility, slow turnaround and unintuitive tools, business teams are turning away from traditional business intelligence (BI) tools to self-service analytics. Even organizations that have relied heavily on spreadsheets are realizing they need a better analytics platform—one that doesn’t lead to a patchwork data environment that is increasingly messy, unreliable and unsustainable.

The new self-service BI platforms are essential to businesses that seek to place data at the core of their operations, decision-making and optimization efforts. Not only does it free up analysts to focus on more strategic work than reporting, but it also enables more business users to access the data they need when they need it. This democratization of data across an organization opens up new opportunities that simply wouldn’t be possible with traditional BI tools. By 2020, Gartner boldly predicts that self-service BI platforms will make up 80% of all enterprise reporting.

As someone who works in this industry (full disclosure I work for Domo), I’ve seen business people get excited about the power and ease-of-use that self-service analytics tools introduce. However, there’s one important misconception that must be addressed and better grounded in reality. From time to time, some business owners, executives and managers come to believe (or hope) that all you need is the technology and nothing more. They mistakenly expect self-sufficiency from these new BI tools instead of self-service.

To explain the difference between self-sufficiency and self-service, I’ll use a soda fountain analogy. When I was growing up, it was common for employees to dispense the soda drinks you ordered at fast food restaurants. Today, many fast food chains allow customers to fill their own drinks from self-service soda fountains. Similarly, with traditional BI platforms, IT teams typically prepared and dispensed reports to business users who requested them. However, with self-service analytics, business users now have more information at their fingertips than ever before.

While soda fountains are now more freely accessible to restaurant patrons, that doesn’t mean they are self-sufficient. Once the soda machines are installed and connected to water and electricity, the work is not done. Someone needs to ensure there’s an ample supply of carbonation and syrup available, and that the flavors are correct and clearly marked. An employee also needs to ensure customers have the necessary cups, lids and straws as well as adequate ice. In addition, the soda fountains must be regularly maintained and cleaned, or they may become unusable if they’re jammed or unsanitary.

Likewise, someone needs to ensure useful information flows out of your self-service BI tool on a consistent basis. Business users expect to receive relevant data from these tools, which means somebody must ensure the metrics and reporting options evolve to meet the changing needs of your organization. People also need to trust the data, or they won’t use it to inform their decision making. As a result, someone must monitor the data quality and address any serious issues before business users lose faith in the numbers. While the self-service aspect of modern BI platforms offers more freedom and power to business users, it doesn’t remove the responsibility of organizations to manage and maintain these analytics systems over time. So yes, people still need to be involved on some level with managing your self-service BI platform—just like they do for self-service soda fountains to operate effectively at restaurants.

At many organizations, business teams have been forced to establish shadow IT teams to own and manage self-service analytics due to their corporate IT teams being unable or unwilling to embrace it. At other companies, more progressive IT teams have been able to partner with the business and successfully transition from a traditional BI approach to the new self-service model. Regardless of whether a business or IT team owns the self-service BI platform, I’d like to highlight three success factors for companies leveraging self-service BI platforms:

  • Training becomes more important, not less. Self-service analytics is not going to magically transform all of your employees into “citizen” data scientists. Training also doesn’t go away just because the tools are more intuitive and easier to use. Less time can be spent on learning how to use the product features and more time can be spent on how to interpret and analyze data properly (e.g., causation vs. correlation). In fact, it might be beneficial to view the training responsibility more as a coaching opportunity.
  • Community facilitates greater scale. As more and more people gain access to a self-service BI platform, the team managing your tool may find it increasingly difficult to be everywhere and answer everyone’s questions. Eventually, they may become the bottleneck that impedes user adoption. A strong user community reduces the BI team’s support burden by creating a forum where users can share ideas, ask questions, collaborate and learn from each other. There’s a profound difference between having 100 users assigned access to your self-service BI tool and building a user community around your analytics tool with 100 members supporting each other.
  • Governance doesn’t go away. The g-word isn’t overly popular or sexy, but it is just as important to self-service BI as it was with traditional BI. As one analytics director told me, “[Self-service analytics] will just create a prettier mess if the underlying processes are not in place.” However, the key difference is to better balance control with flexibility. Too much control can be stifling while too much flexibility can be reckless. You want your self-service tool to provide reliable, consistent data that is relevant and accessible to business users.

BI and analytics solutions create value by putting meaningful insights into the hands of those who can act on them—business people. As we evolve from an IT-dispensed approach to a self-service model, the flow of information is widening and opening up even greater opportunities for generating business value from analytics systems. However, there’s no “autopilot” button you press after deploying a self-service tool—someone needs to own and manage it over time. Don’t confuse “self-service” with “self-sufficiency”, or you’ll be disappointed with the return on your self-service BI investments.

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This article was written by Brent Dykes from Forbes and was legally licensed through the NewsCred publisher network.

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