Self-service BI is more efficient and reliable when you have a robust data governance framework to streamline and standardize the process. Here, we’ll discuss how data governance in self-service BI helps with risk management.
Business intelligence is a collection of processes that convert raw data into actionable insights. A traditional BI setup is highly technical and requires data analysts, data scientists, statistical analysts, and BI experts with relevant skills and knowledge. This team manages the processes and shares the insights with other employees to help them make data-driven decisions. However, there’s a branch of business intelligence that has simplified the process for non-technical employees and end users. This is known as self-service BI.
According to The Business Research Company, the self-service BI market was $10.02 billion in 2024 and is expected to grow at a CAGR (Compound Annual Growth Rate) of 17.3% to reach $22.42 billion by 2029. Self-service BI tools enable users to sort, analyze, derive insights, and generate data visualizations without requiring extensive technical expertise. Be it frontline employees or executives, they don’t have to contact the tech team with queries and wait for the insights/ reports to be sent. With self-service BI, they can perform the activity on their own and make data-driven decisions.
While this made self-service BI popular across industries, it also led to certain challenges and issues, especially with data management and governance. That’s because self-service BI also requires BI consultants to work on the backend and ensure that the data quality is as it should be to derive accurate insights.
In this blog, we explore the challenges of self-service BI and how data governance plays a crucial role in managing risks when data gatekeepers step back.
Challenges without Data Governance in Self-Service BI
The major challenges of using self-service BI deal with data. While most businesses know the importance of data in deriving insights, not many have a clear picture of how to handle data or ways to ensure its quality, compliance, etc. This results in a mismatch of expectations and outcomes. It turns self-service BI into a frustrating tool, resulting in employees sending emails to the BI with their queries and requests.
Data Inconsistency and Trust Issues
It’s no surprise that a business has vast amounts of data to deal with. Transactional data, data from social media and websites, data brought by stakeholders, customer data, etc., are all important and should be used for analytics. However, this raw data has duplicates, incomplete information, and other errors. Ensuring data consistency is a big challenge as low-quality data can result in incorrect insights.
Complexity Instead of Simplification
The market has several BI tools with extensive features and capabilities. Vendors promise flexibility, interactive features, and access to numerous data visualizations. While these sound great in theory, the practical application can be confusing and overwhelming. Which visualization should an employee use for which report? What happens if the wrong type of graph or chart is created? BI risk management is also about ensuring that the customized dashboards don’t complicate things when they should be simplifying the process.
Report Sprawl
Interactive dashboards are easy to use. Hence, employees can generate reports with a couple of clicks. Over time, this results in too many reports created by employees from across the organization. Quality, relevance, and accuracy can take a backseat without a proper understanding of why these reports are generated and how they are used. Repot sprawl leads to confusion and miscommunication, which can result in wrong decisions.
Lack of Standardization
Consistency in how your employees use self-service BI tools is vital for a business to be efficient and achieve its goals. This requires standardization of processes – the data used for insights, the types of reports generated, the validation process, when to use data-driven analytics, etc. This is more of a strategic plan than a series of operations or actions. A business cannot afford for each employee to follow a different standard or process when making data-driven decisions.
Absence of Governance
Data governance has to be a priority, but some businesses ignore it. When you don’t manage data and the analytics process with a proper framework, it can complicate the operations, lead to unverified reports, and may even attract lawsuits from outsiders or stakeholders due to various reasons. Data governance is not optional. It is mandatory even for self-service BI. That’s why many enterprises hire business intelligence consulting services to add a robust governance layer to their data-driven models.
What is Data Governance?
We mentioned data governance a few times. What does it actually mean?
Data governance is a collection of principles, practices, and tools that help manage the data assets of a business throughout the lifecycle. Aligning data requirements with business vision, mission, objectives, and strategy is important for seamless data management. It also includes data security and data compliance, where the data used for analytics is safe from unauthorized access and adheres to the global data privacy regulations, like GDPR, CCPA, etc.
The data governance framework empowers you to leverage your data assets to unlock their true potential and derive meaningful and accurate insights for proactive decision-making. From optimizing resources to reducing costs, increasing efficiency, and standardizing processes, data governance plays a crucial role in protecting your organization’s data and reputation.
How Data Governance Helps Manage Risks in Self-Service BI
Data governance is the solution to managing risks and challenges of using self-service BI tools in your business. Third-party and offshore BI consultants can help implement data governance practices.
Clear and Measurable Goals
The easiest way to complicate things is to be vague and directionless. You need clear and measurable goals when implementing business intelligence in your organization. The same applies to building the data governance framework. In fact, your goals and strategies should be aligned at all times to get the expected results. Be specific about the outcomes you expect, such as reducing the request rate by a certain percentage, increasing meaningful dashboard activity by X times, and so on. Make data compliance a part of your goals and objectives to prevent issues in the later stages.
Data Literacy
Data literacy is the ability to read, understand, and use data effectively for day-to-day activities. It includes various processes and tools implemented in the enterprise. While you can use AI for automation, your employees still need to know why data is important, how to access and use it, how to understand the insights, and how to generate meaningful reports. This requires data literacy programs to train employees to use self-service BI tools smartly. The context is important, and training can provide that.
Data Management Framework
Remember that your insights are only as good as the data provided as input. Low-quality data results in poor-quality insights and incorrect decisions. Data governance is also about data management across the enterprise, streamlining data flow, monitoring for data freshness, and so on. Partnering with an experienced business intelligence consulting company helps increase data quality to make the insights more reliable and promote greater BI adoption. The idea is to reduce trust issues and increase transparency about data management.
Central Data Repository
Data silos lead to truncated and outdated data, which eventually result in low-quality insights. Building a centralized data repository (data warehouse or a data lake) on-premises or on the cloud is also a part of data governance and management. Though the self-service BI dashboards have to be customized based on employees’ roles, they can also facilitate better collaboration instead of isolating teams from one another. Cross-functional reviews, using AI tools for tracking metrics, accessing data directly from a single central unified source, etc., are necessary to manage risks in self-service BI.
Data Security and Compliance
Data security cannot be an afterthought in today’s digital-first world. Proper data governance controls are necessary to make sure the business data is used appropriately. This is done by setting up restricted role-based access to data and insights. Employees have access to the datasets that are necessary for deriving insights for their jobs. For example, the marketing team can access data about sales, inventory, promotions, customer segmentation, etc. The access control can be strengthened using third-party software or two-factor authentication, etc. Tracking the logins and auditing them also ensures no one can misuse data.
BI Standardization and Documentation
While data democratization is important, decentralization using self-service BI without having proper standards and benchmarks can lead to chaos, miscommunication, and inaccurate decisions. These can cause major losses to your business. Create a detailed guide or documentation about do’s and don’ts, how to use self-service BI tools, types of reports to generate, etc. Offer employee training programs and make the documentation easily available for reference. Introduce the processes slowly so that employees can adapt to the changes and become efficient in using new systems at work.
Conclusion
Self-service BI and data governance help democratize data and analytics to empower employees to make data-driven decisions in real-time. The idea is to make data available throughout the enterprise without compromising security, quality, and compliance.
By partnering with a business intelligence consulting company, you can implement data governance that balances security, accessibility, and speed. The documentation will be updated periodically to train employees and ensure the systems are working seamlessly to help you achieve your business objectives and accelerate growth.
FAQs
What risks arise when data governance is relaxed in self-service BI?
When data governance is relaxed or ignored in self-service BI, it can increase the risks to data security, data quality, and data privacy management. Unauthorized access to data, using poor-quality data for insights, and breaching data privacy laws are major concerns. DataToBiz can help you avoid such risks by creating a comprehensive data governance documentation and implementing it in your organization.
How do I balance flexibility for users with compliance and governance?
You can balance flexibility with compliance and governance for self-service BI through the following steps:
- Hire BI consultants to form a team
- Define the objectives and policies clearly
- Plan and implement data literacy programs (for employees)
- Use AI and ML tools for automation
- Set up monitoring systems for corrections
Talk to our experts at DataToBiz for tailored support services.
Which governance frameworks work best in self-service BI environments?
Typically, a balanced and hybrid governance framework works best in self-service BI environments. This includes implementation of in-workflow data quality controls, creating a unified data catalog, setting up role-based access to the data repository, etc. At DataToBiz, we will understand your existing systems and future requirements to develop the governance framework that balances flexibility with speed.
How can I prevent data silos when more users access BI tools directly?
The best way to prevent data silos as more users access BI tools is to build a central data repository, like a data warehouse or a data lake. Then, use data integration tools to share data from the repository to analytical tools and visualization dashboards. A central database eliminates the need for departmental data silos. DataToBiz offers end-to-end data warehousing and governance services to clients from various industries.
What role does AI play in strengthening governance in self-service BI?
Artificial intelligence (AI) strengthens governance in self-service BI by automating recurring processes and streamlining workflows to reduce human intervention. This reduces the risk of human error and automatically ensures compliance standards are followed based on pre-defined parameters. AI also helps with monitoring and sending alerts in case of breaches or anomalies.
How do I ensure data security without slowing down self-service BI adoption?
You can ensure data security without slowing down self-service BI adoption by developing a data governance framework. Select BI tools that have built-in security features like role-based access and encryption. Our experts at DataToBiz will help you achieve this while also providing user training for employees to use the tools effectively.
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