Business intelligence (BI) encompasses the software frameworks (e.g. tools, applications, best practices, methodologies) that empower corporate executives to make optimized operational decisions, develop informed strategies and tactics, and foster improved performance. Dating back to the 1980s, this definition refers to the trends and insights derived when large data volumes of a company’s various operational systems and databases are collected, analyzed, and visualized for business decision-makers and end-users.
Some companies or business leaders might question why they should invest in business intelligence, when they already have a strong foundation of reporting tools and descriptive analytics within their organisation. On the one hand, reporting focuses on a specific output metric or source of data to give insights into historical trends and the status quo. Business intelligence, on the other hand, enables deeper analysis by relying on multiple inputs (for example, by connecting marketing data with salary information from HR) and uncovering previously unidentified process or data relationships. While reporting shows you that something has happened, business intelligence provides answers on why something has happened. Only if a company knows why a specific outcome resulted and how individual processes interact can sound decisions about the future be made.
When examples of leveraging business intelligence first began to emerge across industries such as healthcare, hospitality, or consulting, individuals in IT jobs were the most common users of such applications. And business analysts were dependent on their company’s IT architects and developers in enabling access to crucial query results and business analyses. Over the course of time, however, BI tools have become increasingly agile, user-friendly, and intuitive. Partially as a result of self-service developments, business intelligence is now frequently leveraged by both managers and employees alike in streamlining their day-to-day decision-making processes.
Unlike traditional business intelligence tools that are more commonly used with standardized data sets, however, latest technologies — like robotic process automation (RPA) and artificial intelligence in business — go one step further in enabling modern forms of BI-driven analytics. Such developments are increasingly able to reveal insights into frequently changing and dynamic business scenarios. As a result, enterprises globally will continue to rely on advanced analytical functions to not only understand their current operations but also prepare for challenges and successes down the road.
At this point, you might be wondering what value a formalized business intelligence initiative could have for your company. Business managers have, since the very beginning, used their intuition and basic operational indicators to grasp the current state of their business. Yet, the more information is analyzed, the more informed the decision-making and the bigger and better the outcomes.
By drawing meaning from quantifiable data on which to base business decisions, business intelligence practices give new perspectives in identifying operational bottlenecks, business opportunities, and market trends as well as their relations to individual processes. Such endeavours reveal where processes could be engineered more effectively as well as legitimize new strategies to bring the company forward.
When a company engages in extensive BI work, a key advantage stems from being able to quickly access and leverage information, independent of the source (e.g. ERP, CRM). At a moment’s notice, company directors and operational workers are able to use highly intuitive, accurate, and comprehensive information that competitors only be able to acquire through lengthy efforts on their part.
Even more than other softwares, BI tools have to be capable of dealing with a company’s specific infrastructure (i.e. software used, processes, organizational structure). Here, the biggest challenge is that the BI solution must be able to access and process preferably all data sources available in the company, some of which might still be analog, for the best knowledge gain. Instead of accepting compromises by, for example, only integrating a selection of data sources into one’s BI endeavors, automation software options — like UiPath’s Enterprise RPA Platform — can serve as a critical junction in bridging gaps in data sources as well as supporting digitization efforts of companies that still deal with paperwork.
When RPA and BI are used to work towards a common goal, data can be more easily brought together and made efficiently insightful for truly digital enterprises. On the one hand, automation endeavors as fueled by RPA can empower BI analysts by supporting digitization of data and taking over the parts of data collection and analysis that are connected to high manual effort, repetition, and standardization.
On the other hand, the path to automation can be steered more efficiently through the integration of BI tools into an RPA platform. UiPath, for example, is integrated with the popular BI solutions Kibana and Tableau to provide improved insights into the performance of bot-employee interactions. RPA also acts as a prime tool to automatically identify process exceptions and irregularities that should be at the core of future optimization efforts. UiPath is also partnered with Celonis, a leading provider of process mining software that can be used for simplifying the identification of automation opportunities. While RPA and BI can already bring results when leveraged individually, bringing the two technologies together leads to a whole that is greater than the sum of its parts.
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