The idea behind self-service business intelligence is simple: Put analytical power into the hands of the business users who most need it to make timely decisions. When line-of-business users are empowered by organizations with the right tools and self-service BI best practices, they're able to run queries, build reports and create data visualizations that give them focused insight into the business trends most relevant to them -- all with minimal input from IT or the BI team.
However, while the driver is simple, the execution of a self-service BI deployment is far more complex, especially in a large organization. It's all easier said than done when it comes to setting up a self-service 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, a project director at life insurance software vendor IPipeline Inc.. "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 a process that enables proper planning, strong data governance, a scalable infrastructure and the wherewithal to commit to a full-scale, ongoing business intelligence program. Here are eight best practices for self-service BI initiatives to help put your organization on the path to success.
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1. Get quick wins first
To build momentum and prove use cases for self-service analytics tools, organizations should look for quick wins first, said Lyndsay Wise, director of market intelligence at Information Builders, a BI and data management software vendor.
"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, BI 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] let people drill through visualizations to evaluate situations to make better decisions by leveraging more insight," Wise said.
As a bonus, these dashboards would also be a great way to gain buy-in from key executive sponsors needed in order to push out an organizationwide self-service program. When they see the benefits 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. Make data readiness a priority
Successful self-service BI applications 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, without sacrificing proper governance. He offered a few first steps in supporting data readiness.
"Tighten up data quality standards so that all who interact with the data have clean data," Moffo 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 business analysts and other users explore data streams that otherwise might not be 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."
Lyndsay WiseDirector of market intelligence, Information Builders
3. Emphasize organizationwide collaboration
Self-service BI best practices include a high degree of collaboration among three major stakeholder groups: the business users who will utilize the self-service tools, the BI analysts who support them 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 BI analysts take the lead in building out data modeling processes and data visualization capabilities, while IT handles the back-end infrastructure.
"[Analysts] know the [data] consumers best and need data visualizations themselves to provide their services. IT [is] 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 data and business analysts.
From there, BI analysts should identify power users in business units and departments to help build out tooling, analytical data 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 scalable analytics deployments by formalizing self-service BI best practices for workflows, interdepartmental relationships and more through center-of-excellence or center-of-enablement approaches.
5. Get IT and BI in balance
IT and BI leaders must recognize that they're going to encounter tensions they'll need to balance out in the long run as they manage a self-service BI program.
For example, one big conflict that crops up is between BI analysts spending time training users on existing self-service capabilities and 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 team 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."
6. Ensure compliance with data security laws
Organizations that have embarked on or are thinking of adopting self-service BI initiatives need to think seriously about associated data security and privacy strategies, stressed Anees Merchant, global head of digital and applied AI at analytics vendor Course5 Intelligence.
"Data can be misused, or biases can creep into the picture when organizations look at data elements which aren't required for an individual to take steps or actions based on the insights they generate," Merchant said. Personally identifiable information that isn't needed for analysis uses should "be completely kept out of the self-service analytics roadmap," he warned.
BI managers and their teams also need to consider how much data they want to make accessible to different individuals and at what level of depth, as well as how frequently the data should be updated, Merchant said.
Keeping up with compliance requirements on data protection and privacy is becoming a bigger challenge because of new regulations that are being enacted. The most obvious examples are GDPR and the California Consumer Privacy Act; in addition, measures similar to the CCPA have been proposed in several other states in the U.S.
"Staying compliant isn't a straightforward task," said Gene Yampolsky, a BI and data visualization developer who currently works at Wells Fargo under a consulting contract. "However, the thought that data protection and compliance must be a core part of all business practices makes perfect sense." The various regulations provide "rules and guidelines that help organizations protect their systems and data from security risks" as part of self-service BI environments, Yampolsky added.
7. Create a process to train and onboard users
Deploying a new technology or initiating a new program often requires significant training and change management. That's especially true with self-service BI and analytics initiatives, which means business teams and users need to be involved from day one, Merchant said.
"The plan needs to ensure that the business teams are committed and motivated to make the program successful," he explained. Toward that end, BI managers and business leaders "need to ensure there is adequate time and budget allocated for onboarding and hand-holding as needed."
Equally important, the training and onboarding process should emphasize how business teams can use the insights generated by the business intelligence tools to meet the organizational goals of the self-service BI program.
The focus shouldn't simply be on how to use the BI platform, Merchant said. Instead, he recommended that it be "more aligned to working on what questions the business teams need answers to daily and how the self-service platform can enable them with that."
8. Monitor self-service deployments and costs
Self-service BI enables business users to become more self-reliant and less dependent on the IT and BI teams, from data discovery and data preparation to querying, data visualization and reporting. To be successful, a self-service environment "has to support the need for a personalized and collaborative decision-making environment for the information workers," Yampolsky said.
However, Merchant cautioned that things can go wrong if an organization isn't careful about monitoring deployments. Self-service programs can quickly get out of hand, he said, citing the potential for runaway costs due to uncontrolled scaling; the risk of faulty conclusions and insights from inconsistent data; and breaks in the process "that can get wider if not arrested in time."
To avoid these pitfalls, self-service BI best practices include setting up processes that allow the BI team to monitor, manage and control a program without hampering the ability of users to do required analytics work. That should enable the BI program to efficiently scale as needed and achieve ongoing business success, Merchant said.