Definition
 

Business intelligence (BI) is the process of collecting, analysing, and presenting business data to gain insights for better decision-making.

Description

As part of the BI process, organisations collect data from internal IT systems and external sources, prepare it for analysis, run queries on the data, and create data visualisations, BI dashboards, and reports to make analytics results available to business users for operational and strategic planning.

The ultimate purpose of BI projects is to drive better business decisions that allow firms to generate revenue, improve operational efficiency, and gain a competitive advantage over their competitors. To achieve this purpose, BI combines analytics, data management, and reporting tools, as well as numerous data management and analysis approaches.

How does business intelligence work?

A business intelligence architecture comprises more than just BI software. Business intelligence data is often held in a data warehouse designed for a full organisation, or in smaller data marts that include subsets of business information for various departments and business units, which are frequently linked to an enterprise data warehouse. 

Data stores built on Hadoop clusters or other big data platforms are rapidly being utilised as repositories or landing pads for BI and analytics data, particularly log files, sensor data, text, and other types of unstructured or semistructured data.

The steps in the BI process are as follows:

• Data preparation involves organising and modelling data sets for analysis
• querying the data
• distributing key performance indicators (KPIs)
• conclusions to business users, and using the information to support business decisions

Importance of business intelligence software for business analytics 
 

This is why business intelligence software for business analytics:

• Data-driven Decision Making:

Organisations can gather, analyse, and interpret large amounts of data from several sources using business intelligence software. This makes well-informed decisions based on empirical information rather than hunches or speculation possible.

• Competitive Advantage:

BI software helps companies remain ahead by offering insightful data on consumer behaviour, market trends, and operational effectiveness. They can use it to find chances for expansion, streamline operations, and adjust plans in response to shifting consumer expectations.

• Enhanced Performance Monitoring:

Effective monitoring of key performance indicators (KPIs) is made possible for enterprises by BI software, which provides real-time dashboards and reporting capabilities. This aids in pinpointing areas in need of development, swiftly resolving problems, and optimising performance within departments.

• Improved Operational Efficiency:

Businesses may discover inefficiencies, optimise resource allocation, and automate repetitive procedures with BI tools to streamline operations. Better resource utilisation, higher productivity, and cost savings result from this.

• Strategic Planning and Forecasting:

Business intelligence software enables firms to perform scenario planning and predictive analysis using historical data and current patterns. This helps with long-term strategy development, risk mitigation, and proactive market change adaptation.

How do data mining and business intelligence work together?
 

This is  how you can see business intelligence and data mining work together:

How does data mining help in business analytics?

To be most productive during data mining, data analysts typically adhere to a specific task flow. Without this framework, an analyst can run into a problem mid-analysis that they might have easily avoided if they had planned ahead. 

The following steps are typically included in the data mining process.

Step 1: Recognize the Industry

A thorough understanding of the project at hand and the underlying entity is necessary before any data is touched, extracted, cleansed, or examined. What objectives does the organisation hope to accomplish through data mining? What state is their business in right now? 

What does a SWOT analysis reveal? Understanding what would constitute success at the conclusion of the process is the first step in the mining process, even before any data is examined.

Step 2: Comprehend the Information

After the business challenge has been precisely identified, data should be considered. This covers the accessible sources, the methods for gathering and storing them, the potential ultimate product or analysis, and how the data will be assembled. This step also involves figuring out the data's storage, security, and collecting limitations and evaluating how the data mining procedure will be impacted by them.

Step 3: Get the Information Ready

It gathers, uploads, extracts, or computes data. After that, it is tidied up, standardised, examined for anomalies, evaluated for errors, and verified for rationality. The data may also be sized at this data mining phase, as an excessive amount of information gathering could unnecessarily slow down calculations and analysis.

Step 4: Prepare a Model

Now that our data set is tidy, let's do some maths. Data scientists look for links, trends, associations, or sequential patterns using the aforementioned forms of data mining. Predictive models may also be used to evaluate how past data might influence present-day results.

Step 5: Assess the Outcomes

Analysing the results of the data model or models serves as the conclusion of the data-centred part of data mining. The analysis's results can be combined, analysed, and given to decision-makers who have up until now mostly been left out of the data mining process. Organisations might make decisions at this step based on the results.

Step6: Make the Change and Monitor it

As the data mining process comes to an end, management takes action based on the analysis's conclusions. The business may choose to strategically change course in response to discoveries, or it may determine that better evidence or more pertinent results are needed. 

In either scenario, management evaluates the overall effects of the company and replicates data mining loops for the future by seeing fresh opportunities or issues.

Trends that can affect business intelligence reporting

Some of the trends that can affect business intelligence reporting include:

• Integration of AI and ML:

AI and ML algorithms may analyse large volumes of data in real time, which can also spot patterns, forecast trends, and offer proactive insights. By improving BI reporting's relevance, accuracy, and speed, this trend helps businesses make better-informed decisions.

• Self-Service BI and Data Democratisation:

Thanks to the trend toward self-service business intelligence, non-technical people are now empowered to access and analyse data on their own without assistance from IT departments. Because of the democratisation of data, stakeholders from all areas of the company may now create personalised reports, do ad hoc analyses, and obtain practical insights without the need for specialist technical knowledge. Therefore, decision-makers are more equipped to react swiftly and effectively to business needs, promoting an organisational culture of data-driven decision-making. 

• Real-Time Analytics and Streaming Data:

Real-time analytics and streaming data processing capabilities are becoming increasingly necessary in business intelligence reporting due to the increased volume and velocity of data provided by IoT devices, social media platforms, and other sources. Organisations must capture, process, and analyse real-time data streams to quickly identify new patterns, generate actionable insights, and adapt to shifting market conditions. This trend gives businesses a competitive edge in dynamic business contexts, facilitates quicker decision-making, and improves situational awareness.

Example

Mu Sigma, a top analytics and decision sciences company, offers data analytics, predictive modelling, and business intelligence solutions. It was founded in 2004 and provides services to clients in a number of industries, such as technology, banking, retail, and healthcare. Their analytics platform assists businesses in gaining useful insights from data to enhance decision-making and boost productivity.

FAQs

How can BI tools benefit businesses?

BI tools can benefit businesses by providing actionable insights, improving decision-making, optimising operational processes, identifying market trends, and gaining a competitive advantage.

 What are examples of popular BI tools?

Examples of popular BI tools include Tableau, Power BI, QlikView, MicroStrategy, and IBM Cognos.

How does business intelligence software differ from traditional analytics tools?

With advanced analytics capabilities, business intelligence software offers real-time data processing, interactive visualisations, and self-service features that traditional analytics tools may lack.

What types of data can business intelligence software analyse?

Business intelligence software can analyse various types of data, including structured data from databases, semi-structured data from spreadsheets, and unstructured data from social media, emails, and other sources.

What are the benefits of using a business intelligence platform?

Using a business intelligence platform can streamline data management processes, improve data quality and consistency, enable cross-functional collaboration, and provide a centralised platform for decision-making across the organisation.

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