Top Business Intelligence Software To Transform Data Into Valuable Insights

Each organization has multiple data storage systems. Unfortunately, it leads to the creation of information silos that restrict the free flow of information from one department to another. As a result, it leads to delays in decision-making processes and eventually impacts the overall business revenue. 

To explain this further, consider a company that stores customer-related data in the Customer Relationship Management (CRM) software. On the other hand, all their financial, logistics, sales, marketing, and accounting data is stored in the Enterprise Resource Planning (ERP) software. On top of that, the critical revenue data sets are stored across multiple cloud applications. The most challenging aspect of using various software systems is that they categorize and label all data differently. Thus, business users must implement a single software system to standardize data for technical and non-technical users. 

Business Intelligence ERP software standardizes data and allows business users to gather data from various sources using a native API connection. It can also pull multiple data sets in a single location and store them in cloud data systems. The native API connections work well for small companies or single departments in medium-sized companies. On the other hand, large enterprises that generate large data sets require a more comprehensive business intelligence setup, such as a data mart or data warehouse.

Data mining, also known as data discovery, involves automated and semi-automated data sets to reveal inconsistencies and hidden patterns. Various correlations exist in data sets, including finding outliers in data, grouping specific sets of data, and drawing connections from distinct data sets. 

The most vital role of data mining is in predictive analytics and modeling as it uncovers hidden data patterns. The best feature of data mining is the association rule learning that analyzes data to construct correlations and draw dependencies. As a result, it helps companies better understand how customers interact with their websites and examine the factors that influence their purchasing behavior. 

The association rule learning was initially developed to find connections in purchase data stored in point of sales systems in supermarkets. The following example illustrates this beautifully. Suppose you visit a mall to purchase soya sauce, ketchup, and vinegar. Then the association rule will inform the business intelligence solutions that you have purchased a pack of noodles as well. Thus, BI tools improve the data discovery process tremendously and allow companies to uncover hidden insights that would have remained hidden otherwise.

Prescriptive and predictive analytics functions are a subset of data mining. They use algorithmic models, data visualization techniques, and existing data sets to predict the changes that have a higher probability of happening in the future. 

Predictive analytics forecasts future events based on the interplay between various data sets, including current and historical data, with numerous competitive advantages for businesses of all kinds. BI tools use machine learning, artificial intelligence, and data visualization techniques to predict future consequences. 

Predictive modeling is the most widely used form of predictive analytics. It uses algorithms to identify correlations between a particular measurement unit and any other features related to that unit. The primary purpose of predictive modeling is to find correlations between different data sets. It’s of high importance to insurance companies as it allows them to identify customers that might consider switching.

The primary purpose of descriptive modeling is to decrease the size of data that can be packed into small manageable groups. The application of descriptive modeling is to summarize information such as social media mentions or unique page views. 

Data warehouses contain data in three forms, namely structured, semi-structured, and unstructured. Unstructured data, also known as raw data, contains text documents and other complicated file types for computers to process. Another challenge with unstructured data is that they can’t be stored in sets of similarly formatted rows or columns. As a result, it is difficult for a traditional data analytics platform to analyze data, which is essential for understanding business outcomes. When such a significant volume of data is available in unstructured form, businesses need to select business analytics software based on its text analytics capabilities.  

Natural language processing software, also called text analytics software, is a vital part of BI software. It analyzes large volumes of unstructured data and reveals hidden patterns. It’s beneficial for businesses that deal with social media daily. Using the correct software mix of machine learning and data ingestion, companies can track phrases and keywords, such as business names, to identify how customers use that language. Other benefits of natural language processing include getting actionable insights into lifetime customer value, measuring customer sentiment, and learning customer trends that help predict the most promising product lines. 

A scalable BI platform ensures that even non-technical employees don’t face any challenges while using it. This is why the demand for embedded analytics is rising, as it allows companies to use data visualization tools to build interactive graphs, tables, and pie charts and serve them to internal and external stakeholders via a mobile app. 

Reports, visualizations, and dashboards embedded in the cloud application or company webpage allow companies to save a lot of time and hundreds of thousands of dollars that would have otherwise been spent on building analytics and reporting dashboards from square one to monitor business performance. In addition, these in-built interactive dashboards enable users to implement plug-and-play visualizations that significantly increase the time to introduce the product to the market. 

Decision analytics forms an essential part of a business intelligence solutions as they predict the cascading effect that an action may have across all the variables while making decisions. As a result, decision analytics provides businesses important information that helps predict outcomes and take necessary action. 

OLAP uses multidimensional databases to extract data from various data warehouses and produce reports that observe data from multiple perspectives. OLAP allows business intelligence software to combine different data aspects, go deeper into a single metric, and look at data for combinations of single metrics that aren’t possible in traditional business intelligence tools.

The following example will illustrate this in more detail. A supply chain BI software includes location, purchase date, expiration date, SKU, and the salesperson’s details. OLAP BI tools allow analysts to obtain a clear understanding of the combination of these metrics that would have otherwise been hidden inside two or three-dimensional spreadsheets. 

Each business user doesn’t need access to everything available on the dashboard. Most of them require access to only selected features that are most important to them. Companies can tweak their business intelligence software according to their needs. Here is a list of possible dashboards that most companies would want to have. 

a. Marketing dashboard allows creating ad hoc reports that present information through interactive data visualizations. For example, the number of qualified leads per day, the performance of the latest social media posts, and reports related to highest performing blog posts. 

b. IT support dashboard provides high-level data integration and reporting facility, providing key metrics on issues like current on-call developers, the number of open bug tickets, feature request leaderboard, and much more.

c. Sales dashboard helps in generating reports related to total revenues in a quarter, the number of prospects and leads in each stage of the sales funnel, the average number of meetings by each salesperson, and much more.

d. Customer success dashboards keep a record of the number of open and closed tickets per day, the average time to close a deal, the number of clients serviced each day, customer satisfaction rating, and much more.