Automating business processes to enhance business intelligence

Business intelligence impacts corporate performance management, operational business processes, competitive intelligence, and strategic intelligence, which helps decision-makers implement business strategies, adapt to environmental changes, and gain competitive advantages (Mouhib, Razouk, & Hanano, 2016). Competitive advantages of automating business intelligence include relying on expert and efficient human capital, adherence to standards, and applying knowledge and technology, which are the most influential factor in driving a company’s goals (Yapa, 2019).

Automating business processes provides a fast collection of up-to-date information, quick decision-making, improved customer interaction, and improved production quality (Shadi, Aldhoiena, & Al-Amrib). Automating business processes also generates quality insights to improve operational strengths and productivity, as well as enhance the quality of overall customer interactions. The use of automated tools for data collection provides quantifiable insights that result in measurable improvements in business processes (Visweswariah, et al., 2010). By implementing process automation, businesses can enhance reporting capabilities, improve data quality, and make more informed decisions based on reliable and up-to-date data.

Streamlining data management for business intelligence

Organizations refer to business strategy and long-term goals to gain the capabilities they will leverage to outperform competitors. The key components of the business strategy include (Zoumpatianos, Palpanas, J. Mylopoulos, & Trujillo, 2013):

• Defining strategic, operational, and tactical goals for the organization.
• Associating relevant Key Performance Indicators (KPIs) to monitor the achievement of these goals.
• Establishing target values or acceptable ranges for the KPIs.
• Formulating strategic queries to check if goals are achieved, including sub-targets and trends over time.
• Periodically monitoring the KPIs and sub-areas to identify problems or opportunities that are not evident from aggregated KPI values.

Streamlining data management is crucial for such knowledge discovery and business intelligence. However, with the complexity of modern data warehouses, manually extracting and analyzing large datasets to assess the impact of deviations on the strategic model can be a daunting task (Zoumpatianos, Palpanas, J. Mylopoulos, & Trujillo, 2013). Extracting valuable insights combines techniques from various fields to uncover hidden patterns, trends, and relationships that influence decision-making. Data management involves the entire knowledge extraction process, including how data is stored and accessed, how to develop efficient and scalable models to analyze massive datasets, how to interpret and visualize the results, and how to model and support the interaction between human and machine (KURGAN & MUSILEK, 2006).

Automated business intelligence tools that continuously evaluate strategic queries and diagnose exceptional events in the context of the strategic model are necessary to effectively monitor and manage the business (Zoumpatianos, Palpanas, J. Mylopoulos, & Trujillo, 2013). Furthermore, data management integrates automated business intelligence (BI) tools such as Power BI and decision support systems more closely with decision-making (Pourshahid, Pourshahid, Richards, Amyot, & Akhigbe, 2014). This makes data management a critical aspect of business intelligence (BI) and analytics. The data management is responsible for storing structured and unstructured data for such decision support purposes.

Structured data is typically stored in Operational Data Stores (ODS), Data Warehouses (DW), and Data Marts (DM), while unstructured data is handled using content and document management systems (Mouhib, Razouk, & Hanano, 2016). Effective data planning and management practices are key for ensuring high levels of data quality and system quality in a data warehousing project (Wixom & Watson, 2001). The main motivation for a formal data management process is to ensure that the insights will be useful and valid. Streamlining data management processes helps to achieve desirable validity, novelty, usefulness, and understandability of the analysis results (KURGAN & MUSILEK, 2006).

A centralized data source can help to streamline data management by providing a single, authoritative source of data that can be accessed by multiple applications and users. This approach helps eliminate data silos and inconsistencies and enables more efficient data integration and analysis (Pourshahid, Pourshahid, Richards, Amyot, & Akhigbe, 2014). Centralized data management also ensures that data is maintained and updated consistently, reducing the risk of data inconsistencies and improving data quality (KURGAN & MUSILEK, 2006). The lack of data standards make it difficult or impossible to share or interpret data across application system boundaries (Wixom & Watson, 2001).

Streamlining continuous data for right-time analytics

Many business leaders today are more interested in discussing the future values for KPIs. Thus, to forecast future values for KPIs an initial set of recorded data can be treated as a training set, based on which a prediction model can be trained. This prediction model can then be used to forecast the values for KPIs that have not been recorded yet. Such types of forecasting simulations creates complete and continuous data series even if some intermediate values are missing, enabling more comprehensive performance analytics. They are also used to analyze how much the current KPI value deviates from the target, and how much time is left to achieve the expected target value (Zoumpatianos, Palpanas, J. Mylopoulos, & Trujillo, 2013).

Continuous data also refers to data that is captured and made available for use as soon as it is generated, with minimal delay. This means that the data is constantly updated and available for immediate use. The key characteristic of such data is its low latency or the time between when an event occurs and when the associated data is available. This contrasts with the data that is updated on a less frequent schedule, such as monthly, which would not be considered real-time or continuous. The impact of data latency on decision-making is significant. Longer data and analysis latency leads to delayed or less effective decision-making, as the information available to decision-makers may be outdated or forecasting missing (Watson, et al., 2006).

Reducing data and analysis latency depends primarily on technical solutions, such as real-time data warehousing. However, reducing decision latency requires changes in business processes and how people use information in performing their jobs. Furthermore, streamlining data management by implementing real-time BI capabilities requires a strong business case, technical solutions, and process changes (Watson, et al., 2006).

Automation with Power BI

Power BI helps automate business processes by streamlining data analysis, reporting, and decision-making, which in turn enhances efficiency and reduces manual tasks. Through features like scheduled data refreshes, dataflows, integration with Power Automate, AI-driven insights, and automated alerts, businesses can save time, reduce errors, and make more informed decisions in real time.

Power BI allows schedule automatic data refreshes for reports and dashboards, ensuring that decision-makers always have access to the latest data without needing to manually extract and update datasets. For continuous data streams, such as from CRM systems, financial databases, or IoT devices, Power BI can connect to live data sources, automating the process of ingesting real-time data into reports. With dataflows, automated rules can be set to clean and transform raw data before it is loaded into Power BI. This reduces the need for manual data preparation and ensures that data is always ready for analysis. It also integrates with Azure Machine Learning or other predictive analytics models, allowing businesses to automate complex analytical tasks, such as customer segmentation or sales forecasting.

Power BI can also automate the generation of customizable reports based on pre-defined criteria. Once set up, these reports can be auto updated with the latest datasets, and different users can receive tailored versions of the same report. Furthermore, Power BI integrates seamlessly with Power Automate, enabling businesses to trigger workflows based on events in Power BI reports and datasets.

Power BI also offers report subscriptions, where reports can be scheduled and automatically delivered via email to relevant stakeholders. This ensures the regular distribution of insights without manual intervention.

References

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• Mouhib, A., Razouk, R., & Hanano, A. (2016). BSC-SI: A Framework for Integrating Strategic Intelligence. International Journal of Social and Organizational Dynamics in IT, 1-14. doi:https://doi.org/10.4018/IJSODIT.2016070101
• Pourshahid, A., Pourshahid, A., Richards, G., Amyot, D., & Akhigbe, O. S. (2014). A goal-oriented, business intelligence-supported decision-making methodology. Decision Analytics. doi:https://doi.org/10.1186/s40165-014-0009-8
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• Yapa, S. (2019). Automating Business Processes. In S. Yapa, Customizing Dynamics 365 (pp. 109–134). Berkeley: Apress. doi:https://doi.org/10.1007/978-1-4842-4379-4_4
• Zoumpatianos, K., Palpanas, T., J. Mylopoulos, A. M., & Trujillo, J. (2013). Monitoring and diagnosing indicators for business analytics. Trento: University of Trento.