The Above Came From the 2003 Gartner Annual CIO Report
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.
Business Intelligence Strategy
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.
What is Business Intelligence
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.
Primary Purpose of BI
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.
Key Driver of Digital Transformation
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.
Quick Case Study on Business Analytics Projects
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 BI Projects Fail
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).
Common Cause Problems
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.
Business Intelligence Failure Case Study Results
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.
What is 'Common Cause' in SPC?
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.
Common Causes of BI Project Failure
It seems that many BI projects that fail suffer from at least 3 major common causes:
- No value case.
- Waiting too long to ‘Go-Live’ with something to get the learning feedback loop started.
- Lack of a proper naming convention and associated structure
In the category of ‘special cause’, there is a long list, of course, but the top ones include:
- Poor data governance
This can rear its ugly head in a variety places. For example, it is not uncommon to be using Power BI to extract data from your Google Analytics System. Unfortunately, Google tends to change almost everything about their system, routinely. What this ultimately means is you end up with unreliable data, which can feed all the way through your ERP system.
Unreliable Google Ad Spend data will eventually be reflected on your balance sheet.
Poor data governance practices can have a significant impact on the reliability of Google Analytics and Google Ad Spend data, which can ultimately flow through to the three financial statements. Let's start by understanding what these financial statements are. The first one is the balance sheet, which provides a snapshot of a company's financial position at a specific point in time. It includes assets, liabilities, and shareholders' equity. The second statement is the income statement, also known as the profit and loss statement, which summarizes a company's revenues, expenses, and net income over a specific period. Lastly, the cash flow statement shows the inflows and outflows of cash from operating, investing, and financing activities. Now, back to the impact of poor data governance on these statements. When unreliable Google Analytics data is extracted into the data warehouse, it can lead to inaccurate information being fed into the financial statements. For example, if the number of website visitors is inflated due to incorrect data, it can result in an overstatement of revenue on the income statement. Similarly, unreliable Google Ad Spend data can lead to misrepresentation of advertising expenses, affecting the accuracy of the income statement and ultimately the balance sheet. Therefore, it is crucial to establish strong data governance practices to ensure the integrity and reliability of the data that flows through to these financial statements.
- Bad data being produced at the ERP or source system level and being corrected in the BW system.
- High project consultant turnover, often caused by the client versus the consultant.
- Inappropriate budgets (being too low to attract and retain the best resources).
- No business buy-in. But defining what exactly constitutes ‘buy-in’ and how to obtain it is no small undertaking.
- The use of a large number of ABAP developments versus fully exploiting the modeling capability of the BW system.
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.
In other words, BI projects are not benefitting from the ever increasing capability of the BI systems.
The common cause factors are actually easier to address and I want to offer you some ways to solve most of them:
- No Value Case. As it turns out, SAP actually will help you develop a BI strategy and value case for FREE!
You just need to know who to ask.
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.
Conversely, we can do it.
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.
- Waiting too long to Go-Live.
SAP has always recommended that you get something into the hands of your business users quick. I agree, but be careful! Bad data destroys credibility like termites chewing through a log.
BI Tools have advanced to the point where teams can often go live very, very quickly, then iterate until they get to their final solution.
- Not focusing on Data Storytelling.
Data storytelling refers to the practice of using data to tell a compelling and impactful story. It involves presenting data in a way that is accessible, engaging, and meaningful to the audience. By mastering the art of data storytelling, data analysts and data scientists can make their data analysis and reporting much more effective and impactful on the business.
When data is presented in the form of a story, it becomes easier for stakeholders to understand and relate to the insights being presented. It helps in making complex data more relatable and memorable, enabling better decision-making and driving action within the organization. Data storytelling also helps in creating an emotional connection with the audience, making the data more persuasive and influencing their behavior. Mastering data storytelling is essential for data analysts and data scientists to effectively communicate insights, drive business impact, and ensure that the value of data is fully realized within an organization.
- Finally, lack of a naming convention.
No matter where we go, we find this issue and have developed both a tool and methodology to implement a robust naming convention. We also can offer you a quick FREE 2 hour naming convention review to get you started.
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.
Emergent Role of Data Science
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.
Don't Limit Your Imagination to Achieve Success
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.
Need for Accurate Sales Data
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.
Social Media Data Mining
In today's digital age, companies are increasingly turning to data mining techniques to gain valuable insights from their social media feeds.
Data Driven Behavioural Aware Offers
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.
Big Data Insights Must Be 'Human Touched'
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.
Take Advantage of Our Business Intelligence Skills
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:
- Data analysis: The ability to analyze large volumes of data and extract meaningful insights is crucial in business intelligence. Data analysts should be skilled in statistical analysis, data visualization, and data mining techniques.
- Data visualization: Presenting data in a visually appealing and easy-to-understand format is essential for effective communication. Professionals with expertise in data visualization tools and techniques can create compelling visual representations of complex data.
- Business acumen: Understanding the business context and goals is essential for successful business intelligence projects. Professionals with a strong understanding of the industry and business processes can align data analysis with business objectives.
- Problem-solving: Business intelligence professionals should have strong problem-solving skills to identify issues, analyze root causes, and develop data-driven solutions. This requires critical thinking, creativity, and the ability to work with complex datasets.
- Communication: Effective communication skills are crucial for conveying data insights to stakeholders and driving action. Business intelligence professionals should be able to clearly articulate findings, present data in a compelling manner, and influence decision-making.
- Technical expertise: Proficiency in business intelligence tools and technologies is essential for data analysis and reporting. Professionals should have knowledge of database management, data warehousing, and data integration techniques.
- Data governance: Ensuring data quality, security, and compliance is important for reliable and accurate analysis. Professionals with expertise in data governance can establish data standards, implement data quality controls, and ensure data privacy.
- Project management: Business intelligence projects often involve complex data analysis tasks and multiple stakeholders. Professionals with project management skills can effectively plan, execute, and monitor projects to ensure timely delivery and successful outcomes.
- Continuous learning: Business intelligence is an evolving field, and professionals should be open to learning new tools, techniques, and technologies. Continuous learning helps professionals stay updated with industry trends and deliver innovative solutions.
- Collaboration: Business intelligence projects require collaboration with various stakeholders, including business users, IT teams, and data scientists. Professionals with strong collaboration skills can effectively work in cross-functional teams and leverage collective expertise for better outcomes.
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.
Lets reduce that rate of failure!
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