In a recent New York Times article, it was mentioned that civil aviation had its safest year ever. In the last five years, the death risk for passengers in the United States has been one in 45 million flights, according to Arnold Barnett, a professor of statistics at M.I.T.
In other words, flying has become so reliable that a traveler could fly every day for an average of 123,000 years before being in a fatal crash.
If you compare a flight on an airplane to a BI project (in my opinion, every flight is part a project and is exceptionally complex), you might come to the conclusion that it’s a good thing a BI project is typically not ‘life or death’.
However, in many projects, especially in those industries that are governed by government regulations, such as Title 21 CFR Part 11 or aviation, BI is considered a life and death system and as such, must be tested meticulously to meet government reporting standards.
Business Intelligence (BI) is a critical component in understanding and improving business analytics projects. As a CEO, it is essential to grasp the concept of BI and data warehouses because it is these systems that provide valuable insights and enable data-driven decision-making for your organization.
Business Intelligence or BI is a set of practices of collecting, structuring, analyzing, and turning raw data from various heterogenous data sources into actionable business insights. The main purpose of BI is to provide actionable business insights and support data-driven decision-making.
Business intelligence projects often form part of major digital transformation projects because they play a crucial role in driving strategic decision-making and optimizing business performance. Key Performance Indicators (KPIs) are essential in measuring the success of these projects and determining the impact of digital transformation initiatives.
In the renewable energy industry, a business intelligence project may focus on defining and analyzing KPIs such as energy production, cost savings, and carbon emissions reduction. By gathering and analyzing data from renewable energy sources, businesses can identify trends, patterns, and opportunities to improve their operations, increase efficiency, and make data-driven decisions that align with their sustainability goals. Integrating business intelligence into digital transformation projects allows organizations to harness the power of data and drive innovation in the renewable energy sector.
Business intelligence involves gathering, analyzing, and interpreting data from various sources to identify trends, patterns, and opportunities. Using advanced data analytics practices, it helps in understanding customer behavior, optimizing operations, and improving overall performance. By harnessing the power of BI, you can gain a competitive edge, drive innovation, and make informed strategic decisions. Understanding BI is crucial in appreciating the reasons behind business intelligence project failures, as it allows you to identify and address common causes such as a lack of value case, delays in going live, and inadequate data storytelling. By leveraging the capabilities of BI, you can overcome these challenges and ensure the success of your business analytics projects.
Business intelligence encompasses the process of collecting, evaluating, and interpreting data from multiple sources to discover emerging trends, patterns, and potential opportunities. It helps in understanding customer behavior, optimizing operations, and improving overall performance. By harnessing the power of BI, you can gain a competitive edge, drive innovation, and make informed strategic decisions. To truly grasp the factors behind the failures of business intelligence projects, it is essential to understand the significance of BI. This understanding enables you to effectively identify and address common causes like a lack of value case, delays in going live, and inadequate data storytelling. By harnessing the capabilities of BI, you can triumph over these challenges and ensure the triumph of your business analytics projects.
Why business intelligence projects fail should not be a mystery. But why should BI Projects fail at such a high rate? I have managed many of them from various business intelligence vendors and so far; they have been successful. I have also managed a variety of different types of BI projects with different goals. For example, most business intelligence projects have as a goal to get to a level of self-service business intelligence.
Other projects incorporated various flavors of data visualization software in order to deliver business intelligence dashboards using a variety data sources. Most of these projects combined operational data, structured data, unstructured data and semi-structured data. Almost all of these projects combined popular SaaS tools, such as Tableau, Qlik, xCelsius and other other standalone business analytics applications to build, among other things, advanced map-based data visualization functionality (GIS).
So I had to do some investigating to find out some potential ‘common cause’ problems and possibly some ‘special cause’ problems that cause business intelligence projects to fail.
I took the approach of looking for common causes versus special causes, due to my background in SPC. First a quick primer on SPC common and special causes.
In the world of business intelligence (BI), it is crucial to understand the concepts of statistical process control (SPC) and the difference between common cause and special causes. SPC is a method used to monitor and control processes to ensure they are operating within acceptable limits. Common cause refers to the natural variation that is inherent in a process, while special causes are factors that are outside of the normal variation and can lead to unexpected results. To illustrate this in a layman's terms, let's consider the example of airline depot maintenance overhaul (MRO) process. In this process, common causes might include minor variations in the time it takes to complete each maintenance task or small fluctuations in the availability of parts.
These variations are expected and can be managed within the normal range of business process operations. On the other hand, special causes could be an equipment failure that leads to a delay in the maintenance process or a sudden increase in the number of aircraft requiring overhaul due to unforeseen circumstances. These special causes require immediate attention and action to prevent further disruptions. By understanding and properly addressing both common causes and special causes, the airline industry can ensure the safety and reliability of their MRO processes, just like how they have achieved their safest year ever in terms of passenger safety.
It seems that many BI projects that fail suffer from at least 3 major common causes:
In the category of ‘special cause’, there is a long list, of course, but the top ones include:
The list could go on, of course, but what is interesting is that if you were to go back 30 years, you would find more or less the same problems.
The common cause factors are actually easier to address and I want to offer you some ways to solve most of them:
If you don’t know who to call, drop me a line, and I’ll get your request to the right team. They work on an appointment basis and have some very nice tools to help map your problems, map solutions to your problems and put numbers in a business case.
If you want a simpler approach to developing a business intelligence business case, I offer a simple business case builder you can download here. It is designed as a self-service tool, but does require knowledge of SAP BW and SAP in general.
Now let's talk about some of the successful business intelligence use cases that we see working. These are common Business Intelligence Project Examples and trends that are rapidly emerging.
Data science is a multidisciplinary field that combines scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It leverages the capabilities of Business Intelligence (BI) to uncover new insights and optimize various aspects of organizations. For example, in the defense forces, data science can be used to optimize military logistics supply chains. By analyzing large volumes of data on factors such as transportation routes, inventory levels, demand patterns, and weather conditions, data scientists can identify inefficiencies and bottlenecks in the supply chain. They can then develop predictive models and optimization algorithms to improve the allocation of resources, reduce costs, and ensure timely delivery of critical supplies to the military forces. This integration of data science and BI helps in making data-driven decisions and improving operational efficiency in complex and regulated industries like defense.
In a sales data analysis project, the use of Business Intelligence (BI) is always of the highest importance to our clients. Failure to properly implement BI in these projects can have serious consequences, such as improper commissions being paid and driving higher employee turnover as a result.
The accurate analysis of sales data is crucial for determining the correct commission amounts for sales representatives. Without the use of BI, there is a higher chance of errors in calculating commissions, which can lead to dissatisfaction among the sales team. This dissatisfaction can then result in higher employee turnover as sales representatives may seek better opportunities elsewhere. Therefore, it is imperative to prioritize the implementation of Business Intelligence in sales data analysis projects to ensure accurate commission calculations and to maintain a motivated and stable sales team.
This author personally witnessed the extreme negative effects of having inaccurate sales data and the subsequent impact on commissions. This event affected thousands of sales people within a single company and cost millions of dollars in turnover and low morale.
In today's digital age, companies are increasingly turning to data mining techniques to gain valuable insights from their social media feeds.
By analyzing the vast amount of data generated by social media platforms, companies can make more informed business decisions, especially when it comes to ecommerce and dynamic promotional marketing offers. This practice is essential for businesses to stay competitive in the fast-paced digital landscape.
The importance of using big data cannot be overstated, as it allows companies to avoid embarrassing failures and make strategic moves based on accurate and up-to-date information. One specific example of how data mining is transforming the consumer packaged goods (CPG) industry is through sentiment analysis. By analyzing social media conversations, companies can gauge the sentiment towards their products, identify potential issues, and make necessary improvements. This helps them tailor their marketing strategies and promotional offers to better meet the needs and preferences of their target audience, ultimately driving sales and fostering brand loyalty.
Business intelligence projects can avoid failure by having people with the following required business intelligence skills. These skills are important for effectively analyzing data, identifying trends, and making informed decisions to drive business growth. The most important skills include:
By having professionals with these skills, business intelligence projects can overcome common causes of failure and achieve data-driven success.
Don’t miss this opportunity protect your BI project from failure. Our engineers are pretty busy.
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