Self-Service BI: Overcoming Adoption Barriers

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Self-service BI adoption rates remain low due to reluctance of users to embrace it, hidden overhead, neglected data governance, and more.

Business intelligence (BI) has already taken its place in the sun, as businesses in every industry reach out for BI consulting services and incorporate business intelligence into their workflows to guide decisions with facts, not guessing or speculations. As organizations see tangible benefits of the implemented initiatives, their desire to incorporate new information and give access to the generated insights to a larger number of users grows.

However, traditional BI architectures that were originally designed to support only power users – analysts, data scientists, and alike – are not effective here. That explains why self-service BI is gaining traction lately, as with all the same capabilities as traditional systems, they allow adopters to:

Accelerate the speed of making business decisions

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As opposed to traditional systems, with self-service BI, executives and managers no longer have to wait for the assistance of technologically advanced colleagues to modify the existing dashboard or create a new one. By accessing an application with an intuitive interface, a sales manager can use natural language to run an ad hoc query to learn the sales volumes in a particular location over the past few hours, quickly build a dashboard using drag-and-drop, and make necessary business decisions, thereby reducing the analytics cycle from days to hours and minutes. 

Free up IT departments from cumbersome tasks

The pressure imposed on the IT department by traditional BI frameworks can eventually be relieved by self-service solutions. With casual users enabled to modify existing reports, add new datasets to analysis, and build their dashboards, data scientists and business analysts can now focus on more high-value activities, such as building ML models, data curation, and modeling, while still assisting data consumers with non-trivial cases.

Foster data literacy across the company

Implementation of tools and technologies that automate business intelligence workflows for non-tech users, as well as embedding BI capabilities directly into applications for business users to rely on in their day-to-day activities, helps scale data culture. 

See also: Putting More Intelligence into Business Intelligence

Why self-service BI fails and how to ensure your initiative’s success

However, with all of the gains mentioned above, self-service business intelligence adoption rates remain low. Below you find the reasons why so many companies are pursuing self-services BI but failing to master it, as well as possible solutions to these obstacles. 

Reluctance of business users

There are cases when business users are unwilling to use new technology. Some may be too accustomed to the tools they are currently using, others may find the interface and functionality of new software too complicated or clumsy, or they may simply see no reason for the upgrade.

Then what is the point of adopting a new capital-intensive solution if there is a chance that nobody will use it? Let’s skip the obvious ‘the solution should meet your specific needs and requirements and move on to the essential self-service BI solution’s functionality to help you ensure its high adoption rates.

Firstly, the user interface should be graphical, intuitive, and familiar to end-users. Secondly, the natural language processing capability is another good-to-have feature if your end-users are not well-versed in SQL or programming languages. Pre-build report and dashboard templates, as well as drag-and-drop, would help simplify and streamline reporting. Besides that, you need to provide your end-users with comprehensive support: user manuals, product demos, online chart portals, embedded virtual assistants, as well as group and individual training.

Neglected data governance

Maximize the return on your Snowflake investment with insight into performance,  quality, cost, and more. [Learn More]

Even with a well-architected tech environment and educated end-users on board, your self-service BI initiative may not come to fruition due to unusable data. The reasons may differ; it may be of improper quality, with each department having its own version of the truth, it may be inaccessible or poorly secured, and so on.

The reason commonly lies in poor or insufficient data governance, which defines practices and policies for managing the quality of corporate information, controlling data flows, transforming information, and preventing it from leaking. Besides implementing a comprehensive data governance program, self-service BI adopters should appoint a dedicated team to oversee all data governance processes, identify, and resolve data issues, and control access to information, all while providing solid support to business users.  

Hidden overheads

Self-service business intelligence is a major investment. Besides creating the technology environment to support automated information integration and preparation, cleansing and modeling, the adopters would have to integrate it with a self-service BI tool. The absence of centralized supervision over the implementation process may lead to multiple teams across different departments having their own BI solutions with all the costs implied.

To avoid that, you have to impose limitations on the standalone efforts of individual teams and users and implement the technology according to your strategy, which should consider the number of users, types of activities, and vendor lock-ins, among other factors. 

Conclusion

To sum up, if you want to make your self-service business intelligence work, you have to enable:

  • Technology environment that automates the whole data analytics cycle as much as possible and is suitable for non-technical users.
  • Data maturity, which implies that all BI users are well aware of the importance of timely access to corporate information, high quality of information, security of all available data assets, collaboration with colleagues, etc.
  • Established processes and practices to monitor and supervise self-service BI workflows.

When deployed, your solution would still require much effort: scaling self-service BI initiatives as well as keeping them compliant with the ever-changing business needs is hard and resource-consuming. However, the result of successful self-service BI adoption would justify all the effort. 

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About Tatyana Korobeyko

Tatyana Korobeyko is a Data Strategist at Itransition: Software Development Company.

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