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5 self-service BI best practices for larger organizations

Self-service analytics programs can streamline the BI process by allowing users access to data, but scaling out to thousands of users requires proper planning.

The idea behind self-service BI is fairly simple: Put analytical power into the hands of the business users who...

most need it to make timely decisions. When normal line-of-business users are empowered by organizations with established self-service BI best practices, they're able to run queries, build reports and create visualizations that give them focused insight into the business trends most relevant to them -- all with minimal input from IT or even the business analysts.

However, while the driver is simple, the execution of self-service analytics is far more complex. It's all easier said than done when it comes to setting up a self-service BI program that can scale reliably across thousands of users.

"Organizations want to get the data in the hands of the people who are closest to it, without having to call IT," said Brian Moffo, director of analytics delivery at Anexinet. "However, most organizations are not ready for it. Organizational readiness, data quality and governance are the biggest challenges. Simply turning on the data faucet in the enterprise could be dangerous. Exploratory data can become gospel and published as fact."

In order to get ready, organizations need to establish self-service BI best practices that enable proper planning, strong data governance and infrastructure, and the wherewithal to commit to a full-scale, ongoing BI program.

1. Get quick wins first

To build momentum and prove out use cases for self-service analytics tools, organizations should look for quick wins first, said Lyndsay Wise, director of market intelligence at Information Builders.

"This means identifying key outcomes or metrics and creating self-service applications that align with taking action -- making business decisions -- based on the analytics and visualizations delivered," she said.

One example could be operational dashboards that help supply chain professionals route materials based on factors like weather, traffic and so on. Similarly, dashboards for the C-suite can provide immediate bang for the self-service buck.

"Executive dashboards also provide a great self-service access point by providing insight into overall performance, but [they also] let people drill through visualizations to evaluate situations to make better decisions by leveraging more insight," she said.

As a bonus, these executive dashboards would also be a great way to gain buy-in from key executive sponsors you'll need in order to push out an organizationwide self-service program. When they see the value in their daily lives, they'll be more likely to understand the value that self-service BI tools hold for other users across the enterprise.

2. Data readiness

Successful self-service BI best practices require a foundation of effective data governance and management. Experts like Moffo believe that organizations must enable business analysts and users to get creative with how they correlate and visualize data. He offered a few first steps in data readiness.

"Tighten up data quality standards so that all who interact with the data have clean data," he said. "Exploratory data is a great way of finding new ways to grow your business, but it still needs to have quality standards so that you are not making decisions off of inaccurate or unclear information."

At the same time, he said it is beneficial to loosen up data governance "just enough" to let analysts and users explore data streams that might not be otherwise available to them.

"Open up the data faucet gradually, and continually train everyone who is working with the data," he said. "The more they understand, the better the results will be."

3. Emphasize organizationwide collaboration

One of the ways to build successful self-service is by having IT work collaboratively with the business analytics team to identify how data will be managed.
Lyndsay WiseInformation Builders

Self-service BI best practices typically call for a high degree of collaboration among three major stakeholder groups: the business users who will utilize the analytics, BI analysts and IT professionals.

"In general, one of the ways to build successful self-service is by having IT work collaboratively with the business analytics team to identify how data will be managed. This way, the analytics team will develop solutions, and IT will manage the overall data assets," Wise said. "In some organizations, IT manages all analytics projects. The challenge to doing so effectively is that most IT departments are focused on technology and infrastructure. Successful self-service requires a team dedicated to solving business challenges by leveraging technology."

Justin Butlion, an analytics and BI infrastructure specialist, said he also prefers a model where business analysts take the lead in building out data visualization and modeling, while IT handles the back-end infrastructure.

How self-service BI works
Self-service BI can enable efficiencies and empower users, if organizations follow best practices.

"[Analysts] know the [data] consumers best and need data visualizations themselves to provide their services. IT are generally not familiar with the core business at the level that is needed for mapping out the needs of the business users," said Butlion, who is also the founder of ProjectBI, a community for analysts and data professionals.

From there, analysts should identify power users in various line-of-business groups to help build out tooling, models and visualizations that work best for their daily workflow.

"Visualization is creative work just as much as it is technical," Butlion said. "Taking an agile business/tech-working-together approach is one way to get the most out of the time spent designing and developing critical visualizations that will eventually help shape the direction of your organization or enterprise."

4. Plan for scalability out of the gate

While a few isolated pilot projects are great for showing proof of concept and gaining quick wins, the only way to sustain self-service BI across thousands of users is to build the program with scalability in mind from the start.

"In many cases, teams develop solutions that meet the needs of their teams or departments. Self-service is seen as a way for various groups to gain a lot of insight quickly," Wise said. "Unfortunately, many decisions at this level are made at the business level, without collaboration with IT."

This creates technical debt, unreliable data and compliance nightmares.

"In order to scale, companies need to understand their data assets, how they interrelate across the organization, what infrastructure currently exists and what is required on the platform level to scale," she said. "Basically, in order to scale self-service across the enterprise successfully, businesses need to use a proactive approach and evaluate solution providers that can support the level of future scalability and not simply current use cases."

It's not just a technology problem, either. Groups throughout the organization need to plan for process scalability if they want self-service analytics to truly take hold within their user base. Some organizations, like Morgan Stanley, are driving this by formalizing self-service BI best practices, like workflows, interdepartmental relationships and more, through a center-of-enablement approach.

Nathan Kollett, an analytics and data management professional, shares five dos and don'ts for establishing self-service BI best practices in organizations.

5. Get IT and BI in balance

Finally, IT and BI leaders must recognize that they're going to encounter tensions that they'll need to balance out as they run their self-service BI program in the long run.

For example, one big conflict that crops up is between BI analysts spending time training users on existing self-service capabilities and those building out new ones.

"People treat the analysts like a candy store and constantly want to get their hands on more tools, reports and dashboards," Butlion said. "Adoption is more important, and you need strong analysts that can push back against strong managers that are demanding new and shiny toys instead of using the tools already available."

Another big conflict is between speed and optimization, Butlion said.

"The BI and operations teams are always pushing for efficiency and optimization within the organization. The issue is that this philosophy might be against the overarching strategy of the company," he said. "If the goal is growth and everyone but the ops is focused on that, then you end up with a lot of head-butting. It's challenging to find that balance, and it can be extremely frustrating for the ops people."

These are tensions that can be balanced by establishing self-service BI best practices to maintain the health of the program.

This was last published in December 2018

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