Self-Service Business Intelligence

Self-service business intelligence (BI) is the way of the future, but can it also be a two-edged sword? This analogy for self-service BI was found within the cautionary advice shared by Matthew Roche, a self-described Data Warrior and Principal Program Manager for Microsoft’s Cloud + AI Group, as he discussed the merits of self-service BI in the latest edition of his relatable Building a Data Culture video series:

“With self-service BI, typically the business professionals that are solving their own problems using Power BI are using the same tools that the BI professionals would use.”

Roche compares seasoned data professionals sharing BI tools with untrained data novices to a sword fighter using both the true edge and false edge of their blade. The former cuts with precision and efficiency, while the latter can be equally effective, but is less precise and requires more energy. According to this thought-provoking comparison, modern BI tools give access to many more users, but the results can easily become less reliable without the proper oversight.

“Strategic BI decision makers at large organizations...” were using the term, says Roche. He elaborates on the reality: that a 2-edge sword is really more about the true edge and the false edge. Roche contends that both the true and the false edges are useful, depending on the use case. (Check out Roche’s channel if you like swords - he’s really into them.)

Alas, let’s dig into the main point: that self-service is a great example of the true edge and the false edge. It is a finely tuned and money-saving instrument in some ways, but can be woefully blunt in other ways, often leading to mischaracterizations of data.

What is self-service BI?

Self-service business intelligence is an approach that allows users without technical expertise to mine and analyze large amounts of data. Self-service tools give these novice users the freedom to filter, visualize, and explore data without an assist from IT teams or data experts. This obvious convenience factor makes it unsurprising that the self-service BI market is expected to reach $14.19 billion by 2026.

Organizations seek out self-service BI capabilities to give the people who use and contribute to data most often direct access and control over the organization’s data. This levels of access ensures more informed decision -making while improving efficiency, revenue, and customer responsiveness. Self-service BI unshackles business users from IT departments and lets them focus on what they should be focusing on - their data.

Traditional vs. Self-Service

Under the traditional BI model, IT teams provide access to a company’s data, limit its manipulation, and often complete the analysis and model-building work. The business user is provided access on a strictly as-needed basis. While safe and reliable, it might take weeks for the IT team to extract data from source systems, then transform, cleanse, and load it into a data warehouse or other data store.

The BI team then creates queries to produce the requested analytics results and designs the dashboards or reports to display the information. This approach is quickly becoming unworkable in today’s rapid-fire business environment, where delays of just a few minutes in service can be fatal to a client relationship that took years to develop.

In a world where business users have to act on data that flows through multiple systems, some on the cloud, some locally, some coming in through social channels, others from IoT devices streaming in real-time, bottlenecks in IT can derail the entire business. In our modern same-day delivery world, the value of a company’s data quickly diminishes if not used immediately. This means business users and analysts need to exclude IT from the BI business loop when possible and seek business answers directly from the data.

Self-service BI has the answers for data discovery, quick access, and uncomplicated analytics enlightenment. Utilizing self-service BI, business analysts, executives, and users throughout an organization can run queries, create dashboards, build reports, and run models without the aid of IT.

Powerful Benefits

Once business users are free to do their own data preparation, data cleansing, data joins, ad hoc analysis, and reporting, self-service BI frees up and an organization’s IT team to focus on higher-value work and more technical tasks, such as curating data sets for business users and creating more complex queries. This will have the dual effect of making both the BI and the IT teams more productive. Additional self-service BI benefits include:

• Increased speed: By empowering business users with analytics tools, the speed of business processes is increased as they can directly analyze data and make quicker decisions.
• Business users' expertise: Business users, who often understand the data better than IT professionals, can quickly analyze results and determine the best actions to take based on the model's recommendations.
• IT department and BI team setup: Once the IT department and the BI team set up the data warehouse or data marts for a self-service BI system, business users can query data, create customized dashboards, and generate reports on their own.
• Collaboration and sharing: Modern BI tools often have built-in collaboration features that allow users to easily share their customized dashboards and reports with others. This sharing can occur on platforms like Slack, Microsoft Teams, Trello, and other collaboration tools, enabling widespread distribution of insights with just a click.

A self-service success story

One way to address the elephant in the BI room is by recounting how self-service BI has been deployed to manage....elephants? The Dallas Zoo pioneered a system of ankle bracelets a decade ago to track elephant movement and behavior, but the data generated by the bracelets soon became too much for the Zoo’s staff to manage manually. With an assist from Microsoft, the zoo enhanced their system with a data warehouse to synchronize the radio frequency ID (RFID) data gathered from the bracelets and link it to other data sources.

These data sources were made available to users through Power BI, and innovative zoo employees have since used these capabilities to study animal health and behavior in more depth, and expand the program to other zoo animals, while presenting the real-time location of each elephant on a kiosk for Zoo visitors. Among the many important tidbits of information gathered by Zoo employees, including Coordinator of Elephant Behavioral Science Nancy Scott, is the fact that each elephant walks an average of 10 miles a day, and impressive feat considering the relatively small enclosure size. Zebras and giraffes, now tracked by RFID tags as well, might walk up to 17 miles per day.

Based on the patterns and movements traced by the RFID sensors, the complexity of the enclosure has proven to be more important than the size for the emotional well-being of intelligent large mammals. Scott has also expanded upon this unique application of self-service BI to study social interactions and relationships between elephants:

“When I see two elephants have been close to each other at night, I know they’re probably spooning”

Challenges of self-service BI

Matthew Roche used his sword-wielding demonstration to illustrate the dangers of powerful data analytics tools in unskilled hands, but should we really be worried? Manish Kedia, Co-Founder & CEO of CloudMoyo has carefully considered how the sharp IT team side of the blade, and the less precise user edge must come together to make self-service BI workable:

“Enterprises need a mature governance strategy that is effective, all-encompassing, and realistic. Such a strategy is instrumental in maintaining the integrity, validity, and security of data. It primarily focuses on sharing the responsibility among the users through a tiered, hands-on training approach and user education.”

Resistance to self-service BI might come from business leaders, managers, and even C-suite executives who are reluctant to embrace a data-driven solution that conflicts with their normal way of doing business - one where personal knowledge and intuition where prioritized over cold, hard facts. But deferring to emotionless data isn’t always an easy (or correct) thing to do.

The analyst’s ‘junk in, junk out’ problem looms over the self-service BI reputation as well. Poor data will produce bad results if data sets are incomplete, not cleansed properly, or contain inherent data issues. These issues can lead to confusion over BI findings and, ultimately, faulty decision-making. Data security and privacy can become big issues when users circumvent IT. Expanded data access that self-service BI provides can cause problems if strong data security protections and an effective data governance policy aren't put in place.

Our Data-Driven Future

Despite the pitfalls, our future lies in self-service BI. A recent IBM report on digital reinvention showed that 58 percent of 1,100 executives surveyed in their Digital Reinvention Study expect new technologies to reduce BI barriers to entry, while 69 percent expect more cross-industry competition. Gartner renamed its research BI tools report to ‘Analytics and business intelligence platforms’, recognizing that self-service BI platforms have evolved beyond just BI and into the realm of analytics.

• Natural language querying capabilities are being deployed on many platforms to eliminate the need to write queries in SQL or other programming languages.
• AI and machine learning algorithms that can identify relevant data for users help to analyze and explain the meaning of data elements to them.
• Automation streamlines the data preparation process while suggesting appropriate charts and other types of data visualizations.

Qlik’s Insight Bot offers an AI-powered, conversational analytics experience that allows users to ask questions and get quick insights from their data using natural language processing. Built atop Qlik’s open API framework and its powerful associative engine, Insight Bot can connect to collaboration tools like Slack, Skype, Salesforce, and Microsoft Teams, letting users query data in a simple questions and answer way that humans prefer.

Of swords, elephants and the BI bottom line

Self-service Business Intelligence (BI) provides business users with powerful tools to mine, analyze, and visualize data without relying on IT, offering significant benefits like reduced bottlenecks, increased speed, and greater collaboration. Yet, just like an unskilled swordsman, misuse or lack of governance can lead to chaos. Matthew Roche’s sword analogy underscores the need for effective data governance to avoid data-related risks and ensure accuracy.

The Dallas Zoo’s innovative use of self-service BI to track elephants highlights the potential of this technology, while underscoring the importance of data management and integrity. Resistance to change, data security, and ongoing data quality issues demonstrate that while self-service BI is transformative, it requires oversight to be successful in the long run. As organizations navigate the self-service BI landscape, it's critical to balance agility with governance, ensuring data-driven insights are accurate, secure, and aligned with business goals.