Self-service Business Intelligence Tools: The Effect On Computer-dependent Business Atmospheres – It’s increasingly important for businesses to have a clear view of all their data to stay competitive, and that’s where business intelligence (BI) tools come in. After all, nearly 50% of businesses already use BI tools and are predicted to in the coming years.
But for those who have yet to adopt a tool or simply want to learn more, it can be difficult to understand exactly what BI is. We created this complete guide to educate people about what BI is, how it works, and more.
Self-service Business Intelligence Tools: The Effect On Computer-dependent Business Atmospheres
Business intelligence combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions. In practice, you know you have modern business intelligence when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes. Modern BI solutions prioritize flexible self-service analytics, regulated data on trusted platforms, empowered business users, and rapid insights.
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It’s important to note that this is a very modern definition of BI – and BI has had a stifled history as a buzzword. Traditional business intelligence, capitalization and all, originally appeared in the 1960s as a system for sharing information between organizations. The term business intelligence was coined in 1989 alongside computer decision-making models. These programs continued to evolve, drawing insights from data, before becoming specialized offerings for BI teams with IT-based service solutions. This article serves as an introduction to BI and is the tip of the iceberg.
Businesses and organizations have questions and goals. To answer questions and track performance against goals, they collect the necessary data, analyze it, and determine what steps need to be taken to achieve their goals.
On the technical side, raw data is collected from business systems. The data is processed and then stored in data warehouses, cloud, applications and files. Once stored, users can access the data, starting the analysis process to answer business questions.
BI platforms also offer data visualization tools that transform data into charts or graphs and present them to key stakeholders or decision makers.
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Business intelligence is much more than a specific ‘thing’, business intelligence is an umbrella term that covers the processes and methods of collecting, storing and analyzing data from business operations or activities to optimize performance. All these things come together to create a big picture of the business to help people make better, actionable decisions. Over the past few years, business intelligence has increasingly included processes and activities to improve performance. These processes include:
Business intelligence includes data analysis and business analysis, but uses them only as part of the overall process. BI helps users draw conclusions from data analysis. Data scientists delve into the specifics of data, using advanced statistics and predictive analytics to discover patterns and predict future patterns.
Data analysis asks, “Why did this happen, and what might happen next?” Business intelligence takes these models and algorithms and breaks down the results into actionable language. According to Gartner’s IT glossary, “business analytics includes data mining, predictive analytics, applied analytics, and statistics.” In short, organizations conduct business analysis as part of their larger business intelligence strategy.
BI is designed to answer specific questions and provide immediate analysis for decision making or planning. However, companies can use analytics processes to continuously improve follow-up questions and iteration. Business analysis should not be a linear process because answering one question is likely to lead to follow-up questions and iterations. Rather, think of the process as a cycle of data access, discovery, exploration, and information sharing. This is called the analytics cycle, a modern term that describes how businesses use analytics to respond to changing questions and expectations.
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Historically, business intelligence tools have been based on a traditional business intelligence model. It was a top-down approach where business intelligence was driven by the IT organization and most if not all analytical questions were answered through static reports. This meant that if someone had a follow-up question about the report they received, their request was pushed to the bottom of the reporting queue and they had to start the process over. This led to slow, frustrating reporting cycles and people not being able to use current data to make decisions.
Traditional business intelligence remains a common method for regular reporting and answering static questions. However, modern business intelligence is interactive and approachable. While IT departments are still an important part of managing data access, users can customize dashboards at multiple levels and generate reports in no time. With the right software, users will be able to visualize the data and answer their own questions.
So now you know what BI is and how it works. But how does BI actually help businesses?
BI is more than simple software – it’s a way to get a holistic and real-time view of all relevant business data. The implementation of BI brings countless benefits, from better analysis to increased competitive advantage. Key benefits of business intelligence include:
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Many different industries have adopted enterprise BI ahead of the curve, including healthcare, information technology, and education. Every organization can use data to transform operations. Based on the information in this article and available online, it can be difficult to understand the exact capabilities of BI. Real-life examples can help, so we create case studies from our clients’ success stories.
For example, financial services firm Charles Schwab used business intelligence to gain a comprehensive view of all its branches in the United States to understand performance metrics and identify opportunities. Access to a central business intelligence platform allowed Schwab to put branch data into a single view. Now, branch managers can identify clients whose investment needs may change. And management can track whether a region’s performance is above or below average and click to see the branches that drive that region’s performance. This provides more optimization opportunities and better customer service for customers.
Another example is meal kit service HelloFresh, which automated its reporting processes because its digital marketing team was spending too much time on it each month. With the help of HelloFresh, he saved the team 10-20 working hours a day and made it possible to create much more segmented and targeted marketing campaigns.
A BI strategy is a blueprint for success. In the initial stages, you need to decide how the data will be used, gather key roles and define responsibilities. It can sound simple at high levels; however, starting with business goals is the key to success.
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There are three main types of BI analytics that cover a wide variety of needs and uses. These are predictive analytics, descriptive analytics and prescriptive analytics.
Predictive analytics takes historical and real-time data and models future outcomes for planning purposes. Descriptive analysis is the process of identifying trends and relationships in data using historical and current data. And prescriptive analytics uses all relevant data to answer the question “what should my business do?” to a question.
We have covered many advantages of BI. But like any major business decision, implementing BI comes with difficulties and drawbacks, especially during the implementation phase.
Many self-service business intelligence tools and platforms simplify the analysis process. This makes it easier for people to see and understand their data without having to dig into the data themselves. There are many BI platforms available for ad hoc reporting, data visualization, and creating customized dashboards for multiple levels of users. We’ve outlined our recommendations for evaluating modern BI platforms so you can choose the right one for your organization. One of the most common ways to present business intelligence is through data visualization.
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The key to the successful implementation of BI is choosing the right platform for the task. When choosing a tool, it’s best to keep in mind which key features are most useful for your business. Some of the key features of BI tools are:
Arguably one of the most useful BI tools are dashboards, which allow you to aggregate and view complex data in one place. These dashboards can have different purposes, such as for complex analysis or stakeholder engagement. The challenge is to put together a dashboard that best suits your needs.
As the data landscape grows and the collection, storage and analysis of data becomes more complex, it is important to consider the relationship between BI and big data. Big data has become a bit of a buzzword in the industry lately, so what exactly is it? Well, data professionals define it with the “four Vs”: Volume, Velocity, Value and Variety. These four define and differentiate big data. People tend to consider volume as the main determining factor, as the amount of data is constantly increasing and relatively easy to store for long periods of time.
As you can imagine, this is important for BI as businesses create more and more data every year and BI platforms need to keep up with the growing demands placed on them. A good platform will grow