Over the past few years, the evolution of technology has allowed the business world to continually undergo dramatic changes. Thus, in the contemporary fast-paced business environment, organisations must make quick and informed decisions to remain ahead of their competition. However, decision-making can be complex and challenging due to factors such as uncertainty or a lack of knowledge of possible consequences, especially when dealing with vast amounts of data. This is where operational business intelligence (OBI) comes in. OBI analyses real-time data, gains insights, and makes informed decisions driving growth and profitability. In this article, we’ll explore how operational BI can take your decision-making to the next level and help you stay ahead in a highly competitive market.
Due to the possible confusion in distinguishing between OBI and BI, it is first necessary to outline the primary differences. OBI is measured in real time and thus produces an array of available decisions as data is generated, whereas BI provides the technologies to collect and analyze data that will be used to make decisions in the future. BI is analysed on a predictive basis, whereas due to the contemporaneous collection OBI data, it is accurate and non-hypothetical.
Essentiality for decision-making
OBI is crucial for decision-making as it provides constant real-time visibility into business operations. Due to the veracity and immediacy of information being conveyed, the elongated time periods businesses typically require in making important decisions are significantly reduced. Thus, OBI grants organisations a competitive edge in enabling them to swiftly collect, analyse, and act on data.
The information conveyed to businesses by OBI allows organisations to optimise their business processes and improve operational efficiency whilst simultaneously reducing costs and increasing revenue. This can all be done by simply observing the trends or patterns that have been identified by OBI and making the appropriate decisions considered during the analysis.
These functions allow organisational decisions to occur on an informed basis due to the collection of real-time data, rather than such decisions relying on guesswork or intuition.
OBI is becoming increasingly important as businesses continue to generate progressively larger amounts of data. The Internet of Things (IoT) is the use of the internet to connect computing devices embedded in everyday objects, permitting them to transceive data. With the advent of the IoT and other connected devices, companies are now able to collect vast amounts of data instantaneously.
Thus, OBI is an essential tool for decision making as it allows organisations to use the collected data and identified trends to make informed decisions that will as a result keep them well ahead of their competition.
Understanding the components of operational business intelligence
OBI is a complex system that consists of several components. This article will consider the primary three facets of OBI.
Data Collection
The first component is data collection. Data collection is the process of collecting and analyzing accurate insights for business operations such as marketing, sales, and operational decision-making. Data can be collected automatically from various sources, including sensors, databases, or using other methods. The data collected will be relevant to the business operations and thus allows organisations to scrutinize their business data in a manner that can benefit their company. Once data has been collected, it is then processed and analysed to provide insights into business operations.
Data Warehousing
The second component of operational BI is data warehousing. This occurs in a data warehouse, which is a special sort of data management system. Data warehouses are central locations which store data and were developed for the purpose of enabling and sustaining BI functions, including but not limited to analytics. They often store significant quantities of historical data, and thus trends over certain periods of time can be reconsidered whenever necessary. These warehouses consolidate substantial amounts of data from various sources. Thus, this real-time data is accessible to users across the organisation. Following the collection of this data (as illustrated above), data warehousing provides valuable insights into business processes to improve decision making and thus improve the overall function of the company. Thus, users can make informed decisions based on the insights provided.
Data Visualisation
The third component of operational BI is data visualisation. To put it simply, data visualisation is the presentation of representation of information in a readable format. This primary purpose of data visualisation is to ensure compact information is easy to understand. For example, this may occur by means of a chart, infographic, diagram, map or other visual platform. This allows users to draw patterns, trends and correlations between data points that may not have been visible previously.
Data visualisation tools allow users to create dashboards and reports that provide insights into business operations. This enables users to quickly identify trends and patterns in the data, allowing them to make informed decisions.
Data collection and analysis for operational business intelligence
The collection and analysis of data are critical components of operational BI. Data collection occurs from various sources, including sensors, databases, and other systems. The data is then processed and analysed in a manner that provides insights into business operations. Data analysis involves using statistical methods to identify data trends, patterns, and anomalies. This allows organisations to make informed decisions based on real-time data.
Data collection and analysis is performed using various tools and techniques. These include data mining, machine learning, and predictive analytics. Data mining involves analysing data to identify patterns and relationships that draw conclusions regarding trends in commercial behaviour. Some techniques include pattern recognition, classification, clustering, regression, etc. The purpose of data mining is to ensure marketing and sales are at their most effective, guarantee customer service is exemplary, increase production uptime, successfully assess and predict risks and reduce expenses. These characteristics of data mining allow the technique to contribute substantially to the decision-making processes.
Machine learning involves using algorithms to assist computer systems in analysing data and making data-based predictions without following explicit instructions. Essentially, the computer will be able to learn without direction. The results of this technique have been proven to be more accurate than others as it is a trial-and-error system, in which the machine continues to learn and adapt. Thus, machine learning is the ideal choice for situations where data constantly fluctuates.
Predictive analytics involves using statistical methods to predict future outcomes based on current and historical data patterns. The purpose of this technique is to allow businesses and organisations to adapt to use their resources in an advantageous manner by reducing or highlighting the risks of certain decisions and improving the efficacy of business operations.
Applying operational business intelligence to daily operations
OBI is applicable to daily operations in various ways. For example, it can monitor equipment and identify potential issues before they occur using predictive analytics as discussed previously. It can also optimise business processes and improve operational efficiency. OBI can also monitor customer behaviour and identify trends, allowing organisations to make informed marketing and sales strategy decisions which will improve customer satisfaction and generate a more personal experience for consumers. Furthermore, OBI is an essential tool for managing or monitoring risks and is easily adaptable due to the availability of real-time data which the system relies upon.
OBI is used across various industries, including manufacturing, healthcare, and retail. It is especially beneficial in industries where real-time data is critical, such as transportation and logistics.
Forecasting
OBI can forecast and predict trends. Forecasting involves predicting future outcomes based on historical data. Predictive analytics uses statistical methods to predict future outcomes based on historical data and other variables. This process aids business leaders in determining whether there are enough resources available for certain tasks in the future. It is a valuable tool in making informed and strategic business decisions. It allows these decisions to be made accurately, as they are made in reliance of historical data using qualitative and quantitative models.
OBI can forecast and predict analytics in various industries. For example, it can predict demand for products and services, allowing organisations to adjust production and inventory levels accordingly. Furthermore, it can also predict equipment failures and maintenance needs, allowing organisations to proactively address these issues.
Benefits for staying ahead in a competitive market
Operational BI provides various benefits to help organisations stay ahead in a competitive market. These benefits include:
- Real-time visibility into business operations
- Budgeting
- Improved decision-making based on real-time data
- Planning
- Increased operational efficiency and effectiveness
- Saving costs
- Better customer insights and enhanced customer satisfaction
- Increased revenue and profitability
- Reduced risk and improved compliance
- Ameliorated risk assessment and management
Operational BI gives organisations a competitive edge by enabling them to consistently make informed decisions quickly and effectively. This will optimise business performance immediately and generate continuous improvements as time goes on.
Tools and software for operational business intelligence
The various tools and software available include:
Business Intelligence Platforms
- Business intelligence platforms such as Tableau, Microsoft Power BI, and QlikView
Tableau is a visualisation platform which allows business to explore and manage data in an expeditious manner, allowing them to discover and distribute accurate insights with ease.
Microsoft Power BI is an interactive data visualisation platform which allows for multiple data sources to be combined so that vital business insights can be dispensed in a manner that will drive success.
QlikView is a platform for data discovery and business intelligence, which generates interactive visualisations and analyses for complicated datasets using an in-memory data processing engine to swiftly store and retrieve data.
Data Warehousing Systems
- Data warehousing solutions such as Amazon Redshift, Google BigQuery, and Microsoft Azure SQL Data Warehouse
Amazon Redshift is a data warehousing system designed to store large quantities of data for analysis. It is also a beneficial tool for the performance of large-scale database migrations if businesses are choosing to switch platforms or organise data.
Google BigQuery is a data visualisation platform for both smaller quantities of data and large-scale analysis.
Microsoft Azure SQL Data Warehouse is an analytics system which reduces the time taken to gain insights across data warehouses with the use of advanced technologies.
Data Visualisation Software
- Data visualisation tools such as D3.js, Chart.js, and Highcharts
D3.js is a JavaScript library for manipulating data-based documents via HTML, SVG and CSS. The tool avails the standard capacity of modern browsers without unnecessary limitations.
Chart.js is a similar JavaScript system, however is typically used for visualisation due to the accessibility and ease of understanding in presentation.
Highcharts is a JavaScript software library for charting, useful for timelining and displaying important data points or resources.
Predictive Analytics Tools
- Predictive analytics tools such as IBM SPSS, SAS, and RapidMiner
IBM SPSS and SAS are statistical software platforms used for managing and analysing data.
RapidMiner is an automated and visualised data science platform.
These are some examples of the available tools which can be selected for OBI – there are plenty others to consider! Choosing the right tools and software is critical to the overall success of OBI. Organisations should evaluate their needs and select the tools and software that best meet their requirements.
Implementation
Implementing operational BI requires careful planning and execution. Organisations should follow these steps to implement operational BI successfully:
- Define business objectives and requirements
- Identify data sources and collection methods
- Design data models and architecture
- Select tools and software
- Develop dashboards and reports
- Train users and stakeholders
- Monitor and evaluate the performance
By following these steps, organisations can successfully implement operational BI and reap the benefits of real-time data analysis.
Conclusion
Operational business intelligence is critical to decision-making in today’s fast-paced business environment. It provides real-time visibility into business operations, enabling organisations to make informed decisions quickly and effectively. By understanding the components of operational BI, collecting and analysing data, applying data to daily operations, forecasting and predictive analytics, and leveraging the benefits to stay ahead in a competitive market, organisations can gain a competitive edge. By choosing the right tools and software and implementing operational BI successfully, organisations can achieve their business goals and drive growth and profitability.