According to a recent study, the adoption rate of global business intelligence solutions stands at 26%, with this figure tending to rise in larger organizations. Despite this significant adoption rate, the majority of organizations are likely to encounter business intelligence challenges during the implementation phase.
It is essential for all organizations to strategically plan and address these barriers in order to overcome them successfully. This includes implementing BI best practices and staying updated with the latest developments and topics in the field to fully leverage the associated benefits.
The elements of business intelligence are a critical factor of every organization’s success. Businesses will be unable to get insights, respond to changes, manage the BI cycle, or make educated business decisions unless they have the ability to easily analyze data.
The business intelligence challenges are:
1- Data Integration from Different Source Systems
BI is only effective when it can consolidate and visualize data from all important repositories that exist in your organization to provide the end user the ability to gain holistic insights. If that is not achievable, there is no point to invest and try to implement BI in any organization.
Businesses should make sure that all data repos including databases, spreadsheets, cloud applications, social media, IoT devices, and ETLs available that contain important information to digest and analyze should be able to integrate with the adopted tools in order to allow senior management to have a complete overview of the data and be able to make better decisions based on full data and not a subset.
This represents a significant obstacle that will negatively impact the implementation and the quality of the decisions being made. That is why organizations are required to carefully plan and overcome it.
2- Data Quality Issues
Data quality is another significant challenge in business intelligence as it represents the core element to getting the most out of this initiative. With poor data quality, analyzing, slicing, and dicing data will jeopardize the ability to effectively gain insights and understand how the business operation is performing.
Organizations should make sure their data management strategy has clearly defined how to improve their data quality by ensuring the accuracy, consistency, and completeness of data.
Data entry errors, incomplete or missing data, outdated information are major factors of having poor data standards in your organizations. This will lead to unreliable analysis, flaws in predictions, and inability to identify patterns.
What we recommend usually is to prioritize having high standard quality of data by automating data entry, implementation data validation processes, and data cleansing techniques.
3- Complex Analytics
Advanced analytics methods like machine learning and predictive modeling can offer insightful data. However, there are difficulties in terms of talent, infrastructure, and budget allocation when adopting and integrating these advanced analytical capabilities.
The difficulties rely on having very well trained and skillful resources that are familiar with these advanced techniques to drive the best analytical experience throughout your organization. Also, you need to make sure to improve their employment retention to keep them working within your network as long as possible.
4- Low User Adoption
In certain instances, despite the setup of the software appearing to have gone smoothly, the project management being happy that the solution can accomplish its goals and users having access, the solution is not being used.
Throughout my experience with digital solutions implementation, I came to a conclusion that when the user adoption is low that usually means the software doesn’t provide an intuitive user experience that makes it easy for employees to work with.
A bad user experience is often accompanied by extended training periods, inefficiencies in task completion, and a direct impact on overall productivity. Moreover, it can be a crucial factor in the loss of invested money.
5- Ineffective Data Visualization and Dashboards
Another critical challenge that organizations face when implementing business intelligence is having ineffective data visualization and badly designed dashboards that can’t deliver what the senior managers are looking for.
Having an effective BI implementation requires to have all the pieces of the puzzle correctly placed. If data is of high standards and all other factors are available but the way you deliver the message by designing charts and reports is inaccurate that means you are bound to fail as it it won’t help your company as intended.
Possible problems with integrating the dashboard include a lack of interaction, the inability to pull data in almost real-time, rigid layouts, and even the incorrect use of color.
6- Creating a Data-Driven Culture
Establishing a data-driven culture in any organization is one of the most critical business intelligence challenges because it requires everybody on board to have a fundamental shift in their mindset and daily operations. Many struggle to achieve this because of many reasons including resistance to change, lack of the power of data, and inability to cope with the technological changes.
To overcome this barrier, organizations should be providing continuous training and support for employees that feel uncomfortable, promoting transparency, and highlighting the value of data in this digital world.
7- Managing the Use of Self-service BI
To efficiently harness the power of BI, organizations should tend to give their employees the freedom to access, evaluate, and analyze data on their own without the need for IT interference.
Without relying on IT, data scientists, or analysts, self-service BI enables business users to examine data, draw insights, and build dashboards or reports. Self-service business intelligence is based on the idea that all employees, regardless of analytics expertise, should have access to information that will help them make better decisions.
8- Lack of Skilled Personnel
The role of business intelligence relies heavily on professionals who can interpret and analyze complex data sets, design meaningful visualizations, and implement robust BI strategies.
Unfortunately, the demand for these skills often outpaces the supply, leading to a deficiency of qualified BI professionals. This shortage can impact an organization’s ability to fully leverage the potential of its data for informed decision-making.
Addressing this challenge requires investments in training programs, education initiatives, and talent acquisition to ensure that organizations have the skilled workforce needed to navigate the complexities of modern workspaces.
9- Data Silos
Data silos occur when information is isolated within specific departments or systems, hindering the seamless flow and sharing of data across an organization. This fragmentation can impact the effectiveness of BI efforts as a comprehensive and unified view of the data becomes challenging to achieve.
