Business Intelligence

Data Analytics with Business Intelligence

As data volume, variety, and sources rapidly increase, organizations struggle to manage data and generate meaningful reports through data analytics. In today’s fast-paced environment, a lack of real-time reporting risks companies falling behind competitors who use  actionable intelligence to make valuable, data-driven intelligence-based decisions.

 

Business Intelligence :

Business intelligence (BI) is an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization. Business intelligence tools and processes allow end-users to identify actionable information from raw data, facilitating data-driven decision-making within organizations across various industries.


There are several BI tools in the marketplace, which aid business users in analyzing performance metrics and extracting insights in real-time. These tools focus on self-service capabilities, reducing IT dependencies and enabling decision-makers to quickly recognize performance gaps, market trends, or new revenue opportunities. As a result, BI applications are commonly used to make informed business decisions, advancing a company’s position within the marketplace.

Business Analytics :

The term business intelligence is commonly used in association with business analytics, and while there is significant overlap between the two areas, business intelligence focuses specifically on what is happening in your business and why, while business analytics includes solutions that help you leverage that insight to plan for the future. Business intelligence uses descriptive analytics to formulate conclusions about historical and current performance, providing context around changes in key performance indicators (KPIs). 

Business analytics and business intelligence are inclusive of prescriptive and predictive analytics practices, which help advise decision-makers on potential future outcomes. Both BI and business analytics solutions enable stakeholders to make better decisions, and these should be viewed as complementary to one another.

Choosing the right BI solution provider is a complex undertaking. Hemicube will partner with you to deliver the expertise you need, so you can avoid common pitfalls and accelerate your return on investment.

Preparing and consolidating data sets:

Data warehouses 

After pre-processing and aggregating data sets, the data is then fed into one central repository, a multidimensional cube such as a data warehouse or data mart, which supports business analytics and reporting tools. For larger data sets, businesses typically use an open-source data storage framework. Designing a central data source allows reports to correlate multiple sources of information into one dashboard and gives end users a seamless and fully customizable experience when creating reports.  

ETL – Extract, Transform, Load

BI solutions rely heavily on a data integration process that combines multiple data sources into a single, consistent data store loaded into a data warehouse or other target system. ETLs are essentially the mapping of data sources to extract information as and when needed. Since data extraction takes time, it is common to execute the three phases in the pipeline. While the data is being extracted, the [AA1] transformation process runs while processing the data already received and prepares it for loading while the data loading begins without waiting for the completion of the previous phases. The result of clean ETL designs results in reporting being available in real-time instead of waiting weeks or even month for reports.

OLAP – Online analytical processing

This technology extracts big data from relational tables and reorganizes it into a multidimensional format, enabling fast processing and insightful data analysis. Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time. They borrow aspects of Navigational databases, hierarchical databases, and relational databases.

Multidimensional Structure:

A multidimensional structure is defined as “a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data”. The structure is broken into cubes, and the cubes can store and access data within the confines of each cube. “Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions”. Even when data is manipulated, it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications. Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem, unlike other models.

Hemicube Data Scientists

 

Every organization’s environment, business needs, and competitive edges are unique. The solutions we implement reflect that. Data analytics helps companies describe their businesses, looks deeper into reasons why positive or negative growth has happened, generates more robust reports based on correlated data rather than single data sources and advice on possible action plans to take immediately. Hemicube’s infrastructure agnostic team will consult individually with stakeholders to discuss the possibilities available 

 

within current data sets, conduct an in-depth audit to analyze the existing databases, and understand the types of reports needed and how each department prefers to visualize them for ease of use.

Key Benefits :