Any program that gathers, organizes, and prepares a lot of unstructured data from both internal and external systems for analysis is known as business intelligence (BI) software. Typically, the program is used to query and report on intricate business data. Better company decisions, more income, increased operational effectiveness, and competitive advantages are the ultimate goals of BI software.

Business intelligence tools collect and convert data into a comprehensible format for analysis from a variety of sources, including databases, spreadsheets, and other business applications. In addition to performing data mining, forecasting, and reporting, the software also visualizes data using charts and graphs, enabling users to spot patterns and trends in the data. BI software also comes with reporting capabilities so users can create custom reports and presentations shareable with stakeholders.

Table of Content

• Business Intelligence Software Concept
• Business Intelligence Tools For Enterprises
• What Are Examples of Business Intelligence Software?
• Types of Business Intelligence Software
• Key Features of Business Intelligence Software
• Benefits of Business Intelligence Software
• Latest Trends in Business Intelligence Software
• Potential Issues With Business Intelligence Software

Business Intelligence Software Concept

Understanding the extent of business intelligence (BI) entails learning various concepts linked to data, including data analytics, data warehousing, data lakes, data modeling, ETL, and data integration.

• Data analytics and business intelligence

Data analytics consists of the processes used to examine and obtain insights from data. While BI evolved out of DSS, data analytics has more in common with data mining and statistical modeling.

BI and data analytics are related but distinct concepts. BI is focused on data visualization and reporting, and some BI tools can be used effectively by any authorized manager or stakeholder in an enterprise. Data analytics is more often the purview of data scientists, who instead work with programming languages like Python or R, and take advantage of their computational, statistical, and subject matter expertise to answer difficult business questions.

For example, BI could graph statistics about customer support calls for any timespan, aggregated along arbitrary dimensions (customer demographics, time of day, etc.). A production anomaly detection system that automatically sends alerts to technicians based on tests performed in real time on this same data would fall within the realm of data analytics.

• Data warehousing

A data warehouse is a centralized repository for clean data. The information contained in a data warehouse is intended for consumption by applications and systems that perform analysis. BI tools use data after it’s loaded into the data warehouse to generate insights that help managers improve decision-making.

Within a data warehouse, raw sales data might be replicated to a staging area, joined with historical data, merged with customer information from another database, then used for reports and visualizations on management dashboards.

• Data lakes

Data lakes are centralized repositories like data warehouses, but they are designed to store unstructured data in its native, raw format. Many modern BI tools can act on raw data, and keeping the data unstructured allows for the flexibility to run a range of analytics, as well as the freedom to transform and structure the data to suit an enterprise’s other needs.

• Data modeling

Data modeling is used to standardize data into consistent, governable, and reusable formats and structures. Data stored in a data warehouse is stored in modeled form, while data in a data lake is only modeled after it’s retrieved, based on requirements specific to a particular project or analysis.

Data modeling is important for business intelligence because it creates a clear picture of where data is located, what it means, and how it can be used by various systems and tools.

• ETL and data integration

ETL and data integration are closely related to the terms we’ve already discussed and to each other. ETL is short for “extract, transform, load” and describes the process for ingesting data into data warehouses. Information is extracted from sources, transformed to fit a data model, then loaded into the warehouse. ETL’s modern cousin, ELT, loads data before transforming it, and it is particularly useful when the data’s destination is a cloud data warehouse. Data integration describes the process of combining many disparate sources of data into a processing and storage system useful for generating business intelligence and data analytics.

Business Intelligence Tools For Enterprises

To begin with, these technologies have made data discovery accessible to everyone, once the domain of advanced analytics specialists. Furthermore, these technologies provide you with the insights you need to accomplish a lot of goals, including growth, quickly address pressing problems, gather all of your data in one location, predict future results, and much more.

We will go over business intelligence tools with you and hopefully help you choose the perfect one for your company.

• 1. Microsoft Power BI

One of the most popular BI tools is Power BI, offered by leading software giant Microsoft. This tool is downloadable software, so you can choose to run analytics either on the cloud or in a reporting server. Syncing with sources such as Facebook, Oracle, and more, generate reports and dashboards in minutes with this interactive tool. It comes with built-in AI capabilities, Excel integration, and data connectors, and offers end-to-end data encryption and real-time access monitoring.

• 2. Tableau

Tableau is known for its user-friendly data visualization capabilities, but it can do more than make pretty charts. Their offering includes live visual analytics, an interface that allows users to drag and drop buttons to spot trends in data quickly. The tool supports data sources such as Microsoft Excel, Box, PDF files, Google Analytics, and more. Its versatility extends to being able to connect with most databases.

• 3. QlikSense

QlikSense is a BI tool that emphasizes a self-service approach, meaning that it supports a wide range of analytics use cases, from guided apps and dashboards to custom and embedded analytics. It offers a user-friendly interface optimized for touchscreens, sophisticated AI, and high-performance cloud platforms. Its associative exploration capability, Search & Conversational Analytics, allows users to ask questions and uncover actionable insights, which helps increase data literacy for those new to using BI tools.

• 4. Dundas BI

Dundas BI is a browser-based BI tool that’s been around for 25 years. Like Tableau, Dundas BI features a drag-and-drop function that allows users to analyze data on their own, without involving their IT team. The tool is known for its simplicity and flexibility through interactive dashboards, reports, and visual analytics. Since its inception as a data visualization tool in 1992, it has evolved into an end-to-end analytics platform that is able to compete with the new BI tools available today.

• 5. Sisense

Sisense is a user-friendly BI tool that focuses on being simplified and streamlined. With this tool, you can export data from sources like Google Analytics, Salesforce, and more. Its in-chip technology allows for faster data processing compared to other tools. Key features include the ability to embed white-label analytics, meaning a company can fully customize the services to its needs. Like others, it has a drag-and-drop feature. Sisense allows you to share reports and dashboards with your team members as well as externally.

Other popular BI tools include: Zoho Analytics, Oracle BI, SAS Visual Analytics, Domo, Datapine, Yellowfin BI, Looker, SAP Business Objects, Clear Analytics, Board, MicroStrategy, IBM Cognos Analytics, Tibco Spotfire, BIRT, Intercom, Google Data Studio, and HubSpot.

What Are Examples of Business Intelligence Software?

In general terms, enterprise BI use cases include:

• monitoring business performance or other types of metrics;
• supporting decision-making and strategic planning;
• evaluating and improving business processes;
• giving operational workers useful information about customers, equipment, supply chains and other elements of business operations; and
• detecting trends, patterns and relationships in data.

Specific use cases and BI applications vary from industry to industry. For example, financial services firms and insurers use BI for risk analysis during the loan and policy approval processes and to identify additional products to offer to existing customers based on their current portfolios. BI helps retailers with marketing campaign management, promotional planning and inventory management, while manufacturers rely on BI for both historical and real-time analysis of plant operations and to help them manage production planning, procurement and distribution.

Read Also: Best Business Intelligence Software

Airlines and hotel chains are big users of BI for things such as tracking flight capacity and room occupancy rates, setting and adjusting prices, and scheduling workers. In healthcare organizations, BI and analytics aid in the diagnosis of diseases and other medical conditions and in efforts to improve patient care and outcomes. Universities and school systems tap BI to monitor overall student performance metrics and identify individuals who might need assistance, among other applications.

• Business intelligence for big data

BI platforms are increasingly being used as front-end interfaces for big data systems that contain a combination of structured, unstructured and semi-structured data. Modern BI software typically offers flexible connectivity options, enabling it to connect to a range of data sources. This, along with the relatively simple user interface (UI) in most BI tools, makes it a good fit for big data architectures.

Users of BI tools can access Hadoop and Spark systems, NoSQL databases and other big data platforms, in addition to conventional data warehouses, and get a unified view of the diverse data stored in them. That enables a broad number of potential users to get involved in analyzing sets of big data, instead of highly skilled data scientists being the only ones with visibility into the data.

Alternatively, big data systems serve as staging areas for raw data that later is filtered and refined and then loaded into a data warehouse for analysis by BI users.

• Business intelligence trends

In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, BI analysts and BI specialists who work closely with data architects, data engineers and other data management professionals. Business analysts and other end users are also often included in the BI development process to represent the business side and make sure its needs are met.

To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality on an incremental and iterative basis. Doing so enables companies to put BI features into use more quickly and to refine or modify development plans as business needs change or new requirements emerge.

Other notable trends in the BI market include the following:

In general terms, enterprise BI use cases include:

• monitoring business performance or other types of metrics;
• supporting decision-making and strategic planning;
• evaluating and improving business processes;
• giving operational workers useful information about customers, equipment, supply chains and other elements of business operations; and
• detecting trends, patterns and relationships in data.

Specific use cases and BI applications vary from industry to industry. For example, financial services firms and insurers use BI for risk analysis during the loan and policy approval processes and to identify additional products to offer to existing customers based on their current portfolios. BI helps retailers with marketing campaign management, promotional planning and inventory management, while manufacturers rely on BI for both historical and real-time analysis of plant operations and to help them manage production planning, procurement and distribution.

Airlines and hotel chains are big users of BI for things such as tracking flight capacity and room occupancy rates, setting and adjusting prices, and scheduling workers. In healthcare organizations, BI and analytics aid in the diagnosis of diseases and other medical conditions and in efforts to improve patient care and outcomes. Universities and school systems tap BI to monitor overall student performance metrics and identify individuals who might need assistance, among other applications.

• Business intelligence for big data

BI platforms are increasingly being used as front-end interfaces for big data systems that contain a combination of structured, unstructured and semistructured data. Modern BI software typically offers flexible connectivity options, enabling it to connect to a range of data sources. This, along with the relatively simple user interface (UI) in most BI tools, makes it a good fit for big data architectures.

Users of BI tools can access Hadoop and Spark systems, NoSQL databases and other big data platforms, in addition to conventional data warehouses, and get a unified view of the diverse data stored in them. That enables a broad number of potential users to get involved in analyzing sets of big data, instead of highly skilled data scientists being the only ones with visibility into the data.

Alternatively, big data systems serve as staging areas for raw data that later is filtered and refined and then loaded into a data warehouse for analysis by BI users.

• Business intelligence trends

In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, BI analysts and BI specialists who work closely with data architects, data engineers and other data management professionals. Business analysts and other end users are also often included in the BI development process to represent the business side and make sure its needs are met.

To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality on an incremental and iterative basis. Doing so enables companies to put BI features into use more quickly and to refine or modify development plans as business needs change or new requirements emerge.

Other notable trends in the BI market include the following:

• The proliferation of augmented analytics technologies. BI tools increasingly offer natural language querying capabilities as an alternative to writing queries in SQL or another programming language, plus AI and machine learning algorithms that help users find, understand and prepare data and create charts and other infographics.
• Low-code and no-code development. Many BI vendors are also adding graphical tools that enable BI applications to be developed with little or no coding.
• Increased use of the cloud. BI systems initially were slow to move to the cloud, partly because data warehouses were primarily deployed in on-premises data centers. But cloud deployments of both data warehouses and BI tools are growing; in early 2020, consulting firm Gartner said most new BI spending is now for cloud-based projects.
• Efforts to improve data literacy. With self-service BI broadening the use of business intelligence tools in organizations, it’s critical to ensure that new users can understand and work with data. That’s prompting BI teams to include data literacy skills in user training programs. BI vendors have also launched initiatives, such as the Qlik-led Data Literacy Project.

Types of Business Intelligence Software

A wide range of data analysis applications created to satisfy various information needs are combined in business intelligence. Both self-service BI software and conventional BI platforms support the majority. The following are some of the BI technologies that are accessible to organizations:

Ad hoc analysis. Also known as ad hoc querying, this is one of the foundational elements of modern BI applications and a key feature of self-service BI tools. It’s the process of writing and running queries to analyze specific business issues. While ad hoc queries are typically created on the fly, they often end up being run regularly, with the analytics results incorporated into dashboards and reports.

Online analytical processing (OLAP). One of the early BI technologies, OLAP tools enable users to analyze data along multiple dimensions, which is particularly suited to complex queries and calculations. In the past, the data had to be extracted from a data warehouse and stored in multidimensional OLAP cubes, but it’s increasingly possible to run OLAP analyses directly against columnar databases.

Mobile BI. Mobile business intelligence makes BI applications and dashboards available on smartphones and tablets. Often used more to view data than to analyze it, mobile BI tools typically are designed with an emphasis on ease of use. For example, mobile dashboards may only display two or three data visualizations and KPIs so they can easily be viewed on a device’s screen.

Real-time BI. In real-time BI applications, data is analyzed as it’s created, collected and processed to give users an up-to-date view of business operations, customer behavior, financial markets and other areas of interest. The real-time analytics process often involves streaming data and supports decision analytics uses, such as credit scoring, stock trading and targeted promotional offers.

Operational intelligence (OI). Also called operational BI, this is a form of real-time analytics that delivers information to managers and frontline workers in business operations. OI applications are designed to aid in operational decision-making and enable faster action on issues — for example, helping call center agents to resolve problems for customers and logistics managers to ease distribution bottlenecks.

Software-as-a-service BI. SaaS BI tools use cloud computing systems hosted by vendors to deliver data analysis capabilities to users in the form of a service that’s typically priced on a subscription basis. Also known as cloud BI, the SaaS option increasingly offers multi-cloud support, which enables organizations to deploy BI applications on different cloud platforms to meet user needs and avoid vendor lock-in.

Open source BI (OSBI). Business intelligence software that is open source typically includes two versions: a community edition that can be used free of charge and a subscription-based commercial release with technical support by the vendor. BI teams can also access the source code for development uses. In addition, some vendors of proprietary BI tools offer free editions, primarily for individual users.

Embedded BI. Embedded business intelligence tools put BI and data visualization functionality directly into business applications. That enables business users to analyze data within the applications they use to do their job. Embedded analytics features are most commonly incorporated by application software vendors, but corporate software developers can also include them in homegrown applications.

Collaborative BI. This is more of a process than a specific technology. It involves the combination of BI applications and collaboration tools to enable different users to work together on data analysis and share information with one another. For example, users can annotate BI data and analytics results with comments, questions and highlighting via the use of online chat and discussion tools.

Location intelligence (LI). This is a specialized form of BI that enables users to analyze location and geospatial data, with map-based data visualization functionality incorporated. Location intelligence offers insights on geographic elements in business data and operations. Potential uses include site selection for retail stores and corporate facilities, location-based marketing and logistics management.

Key Features of Business Intelligence Software

After going over the definition and capabilities of BI tools, let’s look at ten features you might not have realized you required.

• 1. Data visualization

What better way to interpret data than to see it in a visual format? Data visualization is one of the most powerful features of BI tools. It allows users to see patterns and trends that they might not otherwise be able to see.

BI tools allow you to create a variety of visuals, such as charts, graphs, and heatmaps. There are also many different ways to customize these visuals to fit your needs.

• 2. Data discovery

Data discovery is a feature that allows users to explore data without having to write any code. This is a great way to quickly find insights that you might not have found otherwise.

Typically, data discovery involves using natural language processing (NLP) to parse through data. This means that you can ask questions about your data in plain English and the BI tool will return results accordingly.

For example, you make a statement like “show me all customers who live in California” or “show me all orders from the last month.” This can help you save a lot of time and effort when trying to find specific data sets.

• 3. Data mining

Data mining is the process of extracting valuable information from large data sets. This can be done manually or with the help of machine learning algorithms. BI tools that offer data mining capabilities can help you find hidden trends and patterns in your data. This can be extremely helpful when trying to make better decisions about your business.

• 4. Ad-hoc reporting

Ad-hoc reporting is a feature that allows users to create custom reports on the fly. This is different from traditional reporting, which typically involves predefined reports that are run on a schedule.

With ad-hoc reporting, you can quickly generate reports based on any data set that you have. This can be extremely helpful when you need to get a quick answer to a question.

• 5. Data warehousing

Data warehousing is the process of storing and organizing disparate data in a central location. This allows users to access the data more easily and provides a single source of truth for the organization. A data warehouse can be used to store historical data, current data, or both. This can be extremely helpful for organizations that need to track long-term trends.

• 6. Predictive analytics

Predictive analytics is the process of using past data to predict future outcomes. This can be done with the help of machine learning algorithms. BI tools that offer predictive analytics capabilities can help you make better decisions about your business. For example, you could use predictive analytics to predict customer churn or forecast future sales.

• 7. Real-time data

Real-time data is data that is constantly updated. This means that you can get current insights into what is happening with your business.

BI tools that offer real-time data capabilities can be extremely helpful for organizations that need to make quick decisions. For example, if you’re running a marketing campaign, you’ll want to be able to see how it’s performing at that moment so that you can make necessary changes accordingly.

• 8. Collaboration

Collaboration is the process of working together to achieve a common goal. BI tools that offer collaboration features can help teams work together more effectively.

For example, some BI tools offer chat features that allow team members to communicate with each other in real time. Other BI tools offer features that allow team members to share data and work on projects together.

• 9. Mobile device compatibility

BI tools that offer mobile device capabilities can be extremely helpful for organizations that need to make decisions on the go. For example, if you’re a sales manager, you’ll want to be able to access your sales data from your phone so that you can make decisions while you’re away from your desk.

• 10. Scalability

Scalability is the ability of a system to handle increasing workloads. BI tools that are scalable can grow with your organization.

This is an important feature to consider if you anticipate your organization growing in the future. For example, if you’re a startup, you’ll want to make sure that your BI tool can scale as you add more users and data sets.

Benefits of Business Intelligence Software

A successful BI program produces a variety of business benefits in an organization. For example, BI enables C-suite executives and department managers to monitor business performance on an ongoing basis so they can act quickly when issues or opportunities arise.

Analyzing customer data helps make marketing, sales and customer service efforts more effective. Supply chain, manufacturing and distribution bottlenecks can be detected before they cause financial harm. HR managers are better able to monitor employee productivity, labor costs and other workforce data.

Overall, the key benefits that businesses can get from BI applications include the ability to:

• speed up and improve decision-making;
• optimize internal business processes;
• increase operational efficiency and productivity;
• spot business problems that need to be addressed;
• identify emerging business and market trends;
• develop stronger business strategies;
• drive higher sales and new revenues; and
• gain a competitive edge over rival companies.

BI initiatives also provide narrower business benefits — among them, making it easier for project managers to track the status of business projects and for organizations to gather competitive intelligence on their rivals. In addition, BI, data management and IT teams themselves benefit from business intelligence, using it to analyze various aspects of technology and analytics operations.

Latest Trends in Business Intelligence Software

Several of the most popular trends in business intelligence are obvious. With significant advancements being made in the workforce automation and digital transformation domains, artificial intelligence is leading the pack.

• Artificial Intelligence

Artificial intelligence aims to make machines perform those complex tasks on their own that can only be executed through human intelligence. Our interactions with analytics and data management are getting revolutionized through artificial intelligence. According to the Strategic Technology Trends report, the trend will combine engineering and hyper-automation with AI with a high focus on possible security risks and vulnerable attack points.

Businesses can enjoy real-time alerts about what is happening every second and get immediately notified about unexpected events. Integrating AI in BI solutions will assist in automatic and comprehensive analysis of the full dataset from any data source without human effort. You can instantly access business reports on growth or trends or forecast, anomalies, what-if analysis, value drivers, key segments correlations, etc. AI can also be utilized for online verification processes, like CAPTCHA technology, with the help of generative adversarial networks (GANs).

• Data Discovery

Data discovery means discovering patterns and discrepancies in data. It is the process of using advanced analytics and visualizations to present all the data collected from different internal and external sources. It has great benefits in keeping relevant stakeholders involved with the data since it allows them to extract actionable insights and intuitively manipulate and analyze the data. The demand for data discovery tools across businesses of all sizes has boomed due to the increasing need for data usage and insights.

Generating insights that add value to the business requires a deep understanding of the relationship between data in the form of guided, advanced analytics, visual analysis, and data preparation. 

Online data visualization and discovery tools are helping businesses create a sustainable decision-making process. The detailed and interactive reports or sales charts presented with several graphs will help teams spot crucial outliers and trends within minutes. Since it is a fact that humans process visual data better, in 2022, the usage of the dashboard as a visual communication and collaboration tool will increase. 

In-depth data analysis through interactivity and augmented analytics will replace simple KPI monitoring. KPI dashboards will have other interactive features, too, such as real-time data and AI-based alarms.

Business users need software for this purpose that is:

• Flexible and agile
• User friendly
• Helpful in reducing time to insight
• Beneficial in handling a variety of data at high volumes

• Data Literacy

The ability of businesses to use data analytics and insights in their decision-making has become a core factor in determining the business’s success. From goal setting to strategizing to taking action, businesses require data at every step. No wonder data literacy is of utmost significance for every business. It is the reading, writing, analyzing, and communicating data in a particular context. Data literacy requires a deep understanding of all the tools and technologies adopted and techniques and methods implemented for data analysis.

 Business leaders must equip all the organization members with the training and tools required for working with data and analytics. Managers need to assess the skill sets of employees, and managers need to identify gaps and weak spots. Team members fluent in data can be appointed as mediators for non-skilled groups. With the right tools and quality training, all the members will acquire enough data literacy to use data as the key language and perform advanced analysis. By 2025, prediction says that data literacy will be so widespread that businesses will no longer require data scientists to progress technologically.

• Data Quality Management

Currently, there is abundant data in every business flowing in from literally everywhere, and it has become crucial to assess data quality before using it. Poor quality data can cost businesses around $9.7 and $14.2 million per year. No wonder data quality management is an increasingly significant trend. Poor data quality can lead to a poor understanding of consumer behavior, wrong estimation of conversion rates, poorly generated marketing budgets, incorrect resource allocation, bad investments, and other errors that can harm businesses significantly. 

Data quality management is the solution to all these problems. It ensures that businesses only use the correct data for analytical purposes to arrive at the right data-driven decisions. Data quality depends upon how complete, timely, accurate, consistent, and compliant it is. There can be no outdated data that does not fit within the timeline or duplicate or missing values. Companies are collecting complex data from several sources regularly, and managing these data using the right tools and processes has become critical.

• Predictive & Prescriptive Analytics Tools

Predictive analysis means forecasting future possibilities by extracting information from existing data sets. It is data mining of past data. Companies get an insight into their future along with alternative scenarios and risk assessments that are reliable enough. It helps companies better understand their customers, products, and partners and identify potential risks and opportunities.

For instance, the airline industry can use it to determine how many tickets to sell at a particular price. The hotel industry can gain insight into how many guests can be expected on a day so that hotels can adjust their availability accordingly. 

Marketers can use this trend to predict customer purchases or responses to locate cross-selling opportunities, and bankers can generate credit scores. The prescriptive analysis goes even a step further. It uses techniques like graph analysis, complex event processing, simulations, neural networks, recommendation engines, machine learning, and heuristics to determine the appropriate business decisions and steps for achieving a particular goal.

It incorporates future outcomes in decision-making that improve the decision-making quality related to optimizing scheduling, inventory, production, and supply chain design to enhance customer experience.

• Real-time Data & Analytics

Since the pandemic arrived, the need for accurate updates and real-time data has become crucial in strategizing and responding to crises. It has played a crucial role in the best possible decision-making for risk aversion and survival during such risky times. Even in the future, developing proper business responses and strategies will require forecasting and alarms.

Live dashboards implemented across companies will provide immediate access to relevant information regarding their business and solve any potential issues. Businesses are staying on top of changes and adapting to immense challenges with the creation of ad hoc analyses. No wonder companies need to gear up rapidly to the increasing use of up-to-date data.

• Collaborative Business Intelligence

Businesses are getting more competitive, and thus the need for collaborative business intelligence has enhanced. It combines collaboration tools like online BI tools, including social media and other 2.0 technologies. Fast-track businesses where analysis is done and reports edited are massive pose unique challenges that only enhanced collaboration can solve. These online BI tools generate automated reports that can be scheduled at specific times for specific people. 

For instance, setting up business intelligence alerts helps to share embedded or public dashboards that are highly interactive and flexible. Such a collaborative environment is especially useful in the current work-from-home setup of organizations. The business world now is more dynamic than ever, and such high levels of collaboration are necessary for problem-solving. It is not limited to document updates or exchanges but extends to the progress of meetings, e-mail exchanges, calls, and ideas collection. Studies predict that in the future collaborative business intelligence will become accessible by larger sets of users and more connected to bigger systems.

• Data Automation

During the past decade, so much data has been produced, stored, and analyzed that companies felt the need for data automation solutions to handle the massive volumes of data collected. Business intelligence allows users to consolidate all the data managed by a company. It provides techniques to discover, measure, analyze, monitor, and evaluate large-scale data. The new trend points toward businesses automating the maximum possible processes using multiple technologies and tools such as AI, no-code tools, machine learning, low-code, etc. 

The barriers between data scientists and business users are slowly diminishing. Right now, businesses have a one-stop destination for any data requirement, like analyzing, collecting, monitoring, analyzing, sharing findings, and reporting. Predictive analytics and automated reports have enhanced the capability of businesses to automate data without depending on IT departments. Data scientists predict that over the next decade, one of the significant trends in business intelligence will be the automation of data science tasks.

• Embedded Analytics

Businesses have become much more productive and capable of improved decision-making by embedding various BI components such as reports or dashboards into their applications. These embedded dashboards add much more value to businesses than conventional spreadsheets. Studies by Allied Market Research indicate that the embedded analytics market will reach $77.52 BN by 2026, with a CAGR of 13.6%.

Organizations can offer more polished presentations and report to customers by white-labeling the selected applications. Embedding analytics to an application gives scope for enhanced collaboration and increases the involvement of every stakeholder rather than just embedding a dashboard or BI features. It equips employees and clients to manipulate the data in a well-monitored environment that facilitates better extraction of insights from every area of your business.

Potential Issues With Business Intelligence Software

These days, it’s feasible thanks to efficient BI tools that simplify complex reports. Additionally, they aid in improving services, increasing operational efficiency, and better understanding clients. For this reason, the BI approach is gaining traction.

There are still significant challenges in putting BI tools into practice, though. We wrote an extensive post to assist you in thinking through the primary obstacles that could arise using BI. Let’s get straight to explore these challenges and understand how you may overcome them.

• 1. Ineffective Data Architecture

One of the key challenges in developing business intelligence solutions is designing data architecture. Basically, it forms the foundation upon which the entire BI ecosystem rests and impacts the effectiveness, scalability, and sustainability of BI solutions.

A well-structured data architecture ensures that information is organized, integrated, and maintained in a consistent manner. On top of that, it streamlines data access, eliminates silos, and provides a single source of truth across users.

However, there is a great variety of information sources, including structured, semi-structured, and unstructured, so integrating and managing diverse data types becomes a daunting task. To keep everyone in the company interacting with the same reliable and up-to-date information, your team has to build a flexible and agile data architecture.

• 2. Poor Data Quality

Next on our list of BI challenges is data quality. The accuracy of business intelligence solutions relies on the data they are built on. Even with substantial investments, dedicated efforts, and a skilled team in place, you can’t succeed if you use incomplete, missing, or outdated information.

To harness such kinds of business intelligence problems, data management can serve as a helping hand. It includes such practices as profiling, real-time monitoring, and regular information cleansing. At its core, data management ensures that information is secure, accessible, and correct.

To ensure data quality (DQ), you can define key data quality metrics such as accuracy, completeness, consistency, timeliness, or uniqueness. By establishing clear and measurable criteria, it is possible to both benchmark relevant information and identify areas that need improvement.

Additionally, there are many methods to monitor data quality, such as creating reports for tracking DQ or incorporating a DQ indicator into your current reports. These techniques allow you to determine how reliable and accurate the data you receive.

• 3. Unclear BI Strategy

Another one of the significant challenges of BI solutions is the lack of a clearly defined business intelligence strategy. And really, how can you guide the implementation process and ensure that the organization’s goals and objectives are met effectively without an appropriate plan?

If you don’t have a clear strategy, the scope of your BI projects can expand beyond the intended. As a result, you can face delays, increased costs, and difficulties in getting meaningful insights.

Without a solid plan, you might also end up with BI tools that don’t meet the needs of end users. This can cause low user adoption rates and frustration among employees who struggle to find value in the BI tools.

As you can see, underestimating the significance of a well-implemented business intelligence roadmap can lead to a range of additional problems. So take your time to carefully plan and execute your data-driven strategy.

• 4. Weak Content Management

Companies that overlook the importance of effective content management can face many challenges during the business intelligence implementation process. As a quick side note, here content management means handling and organizing the data itself, reports, dashboards, and other information generated by BI systems.

Obviously, employees are more likely to embrace BI solutions when they can easily find and use the content they need. Appropriate content management facilitates quick search and retrieval of specific reports or data.

It’s also important to create documentation for each of these reports since it provides clear explanations of the metrics used to assess data. Documentation ensures users understand and appropriately interpret data to make better-informed decisions.

Additionally, it is worth notifying your employees about new reports or updates to existing ones. Thus you can help your team to keep up with the latest insights.

On top of that, alongside effective content management, it is critical to deliver information in an accessible and simple manner. With that in mind, consider designing user-friendly interfaces for BI tools. Such UIs can present complex data in a simple manner utilizing charts, graphs, and other visualizations.

Without a well-crafted design, users may be unwilling to engage with BI solutions. This, in turn, can significantly contribute to lower rates of BI adoption. More on that we will talk in the upcoming chapter.

5. Low Adoption Rates

One of the common problems that can arise during business intelligence implementation is low adoption rates. You may face challenges in encouraging your staff to employ business intelligence tools. For example, they may be reluctant to say goodbye to Excel, SaaS platforms, or other apps they are so comfortable using.

Being unfamiliar with BI tools is just one reason why people do not want to use them. Another reason is a deficiency in data literacy. If you don’t have skilled employees who can interpret data accurately, then possibly you can’t make informed decisions. Whereas, data-literate employees can assess the credibility of data sources, understand the context, and draw meaningful conclusions.

Consequently, if you want to harness the full potential of your business intelligence solutions and tackle problems on time, then it is definitely worth fostering data literacy among your team.

To get to the point, you may offer training or master classes to your staff. Also, consider opening support channels in your team’s message board to provide timely responses to your employees queries. As a result, you can build a community of data-literate individuals within your organization who can help others and share their expertise.

• 6. High Fees

We’ve almost come toward the conclusion of the list of business intelligence challenges and we can’t omit the substantial expenses it requires. Pricing for BI software might vary depending on:

• data volume
• company size
• project dimension
• customization
• number of users
• complexity of the implementation process

Typically, you need to consider costs for licenses, hardware specifications, employee training, and software maintenance. The latter can come with additional fees.

That is to say, implementing BI is not a one-time endeavor, but rather an ongoing process that demands constant development and investment. Business intelligence tools need to evolve alongside a company’s changing needs and its existing data landscape.

So, as a garden requires constant care to thrive, a successful BI implementation requires sustained attention, which may cause additional expenses. Yet, it is definitely the case when such expenses are reasonable.

Companies that opt to cut corners in efficiently processing data run the risk of being pushed out of the competition. Hence, investing in robust analytics and BI tools is a pathway to success.

• 7. Hard to Deliver Mobile BI

The list of business intelligence challenges will be incomplete without mobile BI. Though adapting business intelligence tools to mobile devices appears to be simple, it doesn’t mean that this process is devoid of complexities.

Here are some basic issues you can come across along your way:

• Data Security. Mobile devices can be more susceptible to security breaches, so employing strong encryption and authentication measures is crucial.
• User Experience. With different screen sizes and a variety of device types, it’s vital to think about how users can smoothly navigate and interact with your BI solution on their mobile devices.
• Performance and Speed. Mobile networks might not always provide the same level of connectivity and speed as traditional networks.

Overall, to successfully utilize mobile BI, you need careful planning, design, development, testing, and ongoing maintenance. On top of that, your team should be experienced in BI, web, and mobile technologies.

Finally

Choosing the appropriate BI tool is a crucial step in addressing business intelligence implementation issues. Tableau, Qlik Sense, and Microsoft Power BI are the top business intelligence platforms with the biggest market shares, according to TrustRadius.