Self-service Business Intelligence Tools For Financial Analytics – How will the modern data analyst enable his team—and his company—to dominate the data decade? This is the question that leading companies such as Walmart, General Motors, Hulu and Schneider Electric are asking themselves today.
Self-service analytics is a type of business intelligence that enables users to access and analyze data without relying on the support of IT or BI experts. This means less manual reporting work for your analytics department and more time to invest in strategic initiatives.
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Self-service enables first-time business users to access data and provide insights so that every business user can make data-driven decisions instead of relying on opinions or feelings. Because of its benefits, self-service analytics is quickly becoming a must-have for businesses of all sizes.
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So what are these benefits and what best practices should be followed? Read on to find out.
Users can quickly and easily access data, extract answers and create reports without having to wait for someone else to do it for them.
This is especially true for Factory14, a European consumer goods company focused on increasing the profitability of brands sold on Amazon. Their brand management and operations teams were drowning in spreadsheets. These data silos hindered their ability to keep up with product demand.
That’s when they decided to implement a real self-service analytics solution. Here’s how then-VP of analytics Leon Tang described the increased efficiency of self-service analytics:
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“For someone like [CBO] to just walk in, type a few words into the search bar and get exactly the information they need, just like that — it’s just amazing.” Today, people use Excel much less. Instead, they find exactly the data they need.”
Factory14 has since been successfully acquired and Leon Tang has accepted a second position as Director of Data for Jobandtalent. However, the impact self-service analytics has had on their team is undeniable.
By allowing users to access data themselves, self-service analytics reduces the likelihood of errors that can occur when data is manually entered, downloaded, or processed. While this is true in any industry, the accuracy and reliability of data is extremely important in healthcare.
For example, consider Gilead Sciences, a research-based biopharmaceutical company. Here’s what Murali Vridhachalam, head of business data and analytics, had to say about how self-service analytics helped improve the performance of their insights:
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“I’ve noticed a lot of different teams working on hidden data and analytics. Now at Gilead, we have a holistic approach to driving data-driven [insights]. It’s not a cliché… Many things depended on whether a BI team could produce reports, for example. We’re trying to break that culture by enabling self-service analytics.”
The democratization of data and the rise of self-service analytics have the potential to improve patient outcomes in healthcare.
With self-service analytics, users have more control over their data and can create reports and dashboards that meet their specific needs.
Data cloud leader Snowflake has seen first-hand the increased customization capabilities that self-service analytics can provide. That’s because Snowflake doesn’t just deliver best-in-class data products; Its entire business is also based on data.
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“As a company, we believe that measuring the right data leads to good business results. As a data platform company, we must deliver on this promise,” -Sunny Bedi, CIO and CDO at Snowflake
To meet the growing needs of their highly successful business, Sunny knew he would have to secure additional resources or find a way to work smarter. Then he turned to self-service analytics. Customization was one of the driving factors behind this decision.
“Pre-prepared reports are not enough. “If you’re living in this world, you’re not taking advantage of all the opportunities your data has to offer.” – Sunny Bedi, CIO and CDO at Snowflake
Learn how self-service BI and customization helped Snowflake reduce its IT backlog by 20% – read the full case study here.
Definition Of Self Service Analytics
The first step in adopting self-service analytics is making sure all your data is easily accessible to those who need it. This means that there is a central repository – such as a cloud data warehouse – where the data can be stored and accessed, ensuring that the data is properly tagged and organized.
Another important aspect of self-service analytics is ensuring that users have an intuitive and engaging interface to explore data. It should be easy to use and navigate and give the user the ability to customize their experience.
With so many users gaining insights from data, it’s important to encourage collaboration to avoid reinventing the wheel. This means enabling business users to share reports and send insights to tools like Google Sheets, Microsoft Teams, Slack and email so that insights can be further disseminated.
Self-service analytics are based on high-quality data. Therefore, it is important to follow data management best practices. This means you need to ensure that your data is well managed and controlled. Without proper data management, your self-service analytics efforts are at risk.
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Finally, it is important to continuously iterate on self-service analytics programs. This means continuously collecting feedback and making changes and improvements so that the program can continue to meet the needs of users. In addition, it is important to track metrics so that progress can be tracked and issues addressed in real time.
A self-service analytics platform should be easy to use and allow users to quickly access and analyze data. It should have an intuitive interface that is easy to operate. Ease of use is key for users to access data without many steps.
As your business grows, it’s important that your self-service analytics platform can scale with you. Platforms that lack scalability can quickly become obsolete because they cannot keep up with the changing needs of growing businesses.
A self-service analytics platform should be flexible enough to allow users to create highly customizable reports without relying on help from IT or other departments. This level of flexibility is key to making data-driven decisions quickly.
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Unauthorized access to data can spell disaster for any company. This is why it is essential that your self-service analytics platform has strong security measures in place. Self-service analytics platforms should have at least SOC 2 Type II, ISO 27001, Privacy Shield Framework and GDPR certifications. By ensuring your platform meets these standards, you can help protect your data and prevent unauthorized access.
If you want to take your business to the next level, self-service analytics is essential. The ability to quickly analyze data and generate personalized, actionable insights at the moment of impact is what sets you apart from the competition.
Offers powerful search and AI-driven self-service analytics tools that make it easy for you to find and gain insights. Are you ready to get started? Start your free trial today! What is Self-Service BI? Self-Service BI vs. Traditional BI Why is Self-Service BI Important? Benefits of Self-Service BI How does Self-Service BI work and how are different industries using Self-Service BI? What should I look for in a self-service BI tool? Delivering insights to everyone. Frequently Asked Questions about Self-Service Business Intelligence
At the heart of self-service business intelligence (BI) is the idea that employees should be able to access business data and gain insights—without the help of an IT department or extensive SQL knowledge. It’s typically a self-service BI tool or application that enables end users across the organization to analyze and present data without the help of IT.
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This means teams in operations, marketing, product development, sales, finance and more can use data every day to make decisions while easily monitoring data management processes. Employees can customize their queries and dashboards to answer their specific questions and provide insights that will help them in their role.
There are several differences between traditional BI and self-service BI. These differences affect who can access data, how quickly someone can receive data, and how much autonomy teams have when it comes to understanding how their work impacts the organization.
In traditional BI, the custodians of all data are a small group of people – usually IT or business intelligence teams. They are the ones who control everything: import data from different sources, manage and organize data in the data warehouse, run queries, create dashboards and send reports. To be involved in traditional BI, team members must be highly skilled. They typically have extensive training and experience in data management and specific software platforms.
Before team members in traditional BI organizations can submit data or make significant changes, they often need to obtain and approve approvals, so the process can take time. A detailed explanation may be required to complete the task. This delay means that the data coming from the BI or IT teams is at least slightly out of date by the time it is sent to the requester. Data lag means that the company makes decisions based on historical data rather than current trends.
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However, self-service BI is a more modern model where users can access and export data themselves. As with traditional BI, data is loaded into the warehouse in the same way – just different
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