Introduction to Responsible AI: Unpacking the harms
The latest in our Responsible AI blog series, the CallMiner Research Lab explores two of the main categories of harms that AI outputs can cause: Harms...
The Team at CallMiner
September 05, 2019
Business intelligence (BI) is a collection of tools and strategies that analyze and convert raw data into actionable and coherent information for use in business analysis to help in decision making. Every business has powerful transaction-oriented systems that store all data gathered from daily operations into repositories. To remain competitive, businesses must rediscover and utilize the information they hold, and this is where BI comes into play. With business intelligence, we can derive insights from a pool of available data to deliver precise, actionable, and nearly real-time inputs for decision making.
Business intelligence greatly improves business performance, with statistics showing up to a ten-fold increase in the return-on-investment. Organizations that utilize business intelligence see close to a five-time increase customer experience analysis and decision making speed. Conversely, failure to adopt BI has resulted in large chunks of data remaining unutilized
There are several business intelligence techniques companies can put to use to gain valuable insights to inform decision-making. Here’s a look at the most common BI techniques.
Analytics is a business intelligence technique that involves the study of available data to extract meaningful insights and trends. This is a popular BI technique since it lets businesses deeply understand the data they have and drive ultimate value with data-driven decisions. For instance, a marketing organization can use analytics to establish the customer segments that are highly likely to convert to new customers, and call centers leverage speech analytics to monitor customer sentiment, improve the customer experience, and for quality assurance purposes, just to name a few.
Predictive modeling is a BI technique that utilizes statistical techniques to create models that could be used in forecasting probabilities and trends. With predictive modeling, it is possible to predict the value for a particular data item as well as the attributes using multiple statistical models.
Online analytical processing is a technique for solving analytical problems with different dimensions. The most important value in OLAP is its multidimensional aspect that lets users identify problems from different perspectives. OLAP could be used to complete tasks such as budgeting, CRM data analysis, and financial forecasting.
Data mining is a technique for discovering patterns in huge datasets and often incorporates database systems, statistics, and machine learning to find these patterns. Data mining is an integral process for data management as well as the pre-processing of data since it ensures appropriate data structuring. End users could also use data mining to create models that reveal these patterns. For instance, a business could mine CRM data to predict which leads will most likely buy a certain solution or product.
The model visualization technique is used to transform the discovered facts into histograms, plots, charts and other visuals that aid in proper interpretation of the insights.
BI tools are all about helping you better understand trends and derive insights from data in order to make strategic and tactical business decisions. Here’s a rundown of a few popular business intelligence tools companies leverage to derive insights.
If you’re not tech-savvy, the Sisense BI tool could be a great option. It is incredibly user friendly and allows everyone within an organization to manage, analyze and visualize complex datasets without involving the IT department. This tool lets you gather data from various sources, including Google Analytics and AdWords. Since this tool uses in-chip technology, data processing is faster compared to other BI tools.
SAP Business Intelligence provides an array of advanced analytics solutions including machine learning, BI predictive analytics, and planning and analysis. This enterprise-level applications for client/server systems offers data visualization and analytics applications, reporting and analysis, mobile analytics and office integration. SAP is a robust solution intended for all roles (management, end uses and IT) and offers a ton of functionalities in a single platform.
Dundas BI is a browser-based BI tool that allows users to connect to multiple data sources in real-time. It provides great visualizations in tables, graphs and charts that could be customized and viewed from mobile devices and desktops. Users can easily build reports and extract certain performance metrics for purposes of analysis. Dundas offers support to all company types and across different industries.
To get the most out of your business intelligence tools and solutions, follow these expert best practices.
Select BI Tools That Align with Your Business Needs
It is essential that you understand all the needs of a business in order to properly implement a BI system. This understanding has two perspectives – both IT departments and end users have critical needs, and they are often different. To gain a critical understanding of business intelligence requirements, the organization should analyze the different needs of all its constituents.
“In the past, IT professionals had been the primary users of BI applications. However, BI tools have evolved to be more intuitive and user-friendly, enabling a large number of users across a variety of organizational domains to tap the tools,” says Mary K. Pratt in an article on CIO.com.
“Gartner’s Howson differentiates two types of BI,” Pratt explains. “The first is traditional or classic BI, where IT professionals use in-house transactional data to generate reports. The second is modern BI, where business users interact with agile, intuitive systems to analyze data more quickly.”
Ensure Data Quality
A business intelligence initiative will only succeed if it integrates high-quality data. Poor data often results in poor decisions, so data quality is paramount.
Data profiling is a great data quality management technique where statistics are collected and data examined for improved data governance. It aids in reducing risk, maintaining consistency, and optimizing search through metadata. According to Panopoly.io, “Data processing and analysis can’t happen without data profiling — reviewing source data for content and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.”
Choose Tools That Provide a Good User Experience
A good user experience can promote user adoption, which in turn allows organizations to obtain greater value from their BI programs and tools. A seamless user experience, including a user-friendly, intuitive interface, is crucial for end user adoption. “The user adoption rate among new business intelligence systems can be as low as 25%, often due to poor user experience (UX), slow queries, and irrelevant data and reports,” according to Rittman Mead. “But whatever the issue, it’s important that you build a BI experience that meets the standards of today’s users.”
The sheer volume of data and variety of BI techniques can easily get overwhelming for companies. While it often makes sense to enlist the help of a data scientist or an analytics expert to help you select the right methods and solutions, implementing user-friendly solutions that meet the needs of every department is the best way to maximize your ROI.
What BI methods and solutions do you find most effective?
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