BI lifecycle management consists of creating, designing, developing, and managing BI systems by incorporating business users into the design process and creating data models, database objects, data integration mappings, and front-end semantic layers directly from the input of business users.
Today’s organizations need to react more quickly, develop new approaches faster, conduct more agile R&D and get products and services to market faster than ever before.
BI-based decision-making with faster access to information and feedback provides more quick prototyping and testing opportunities.
The reporting benefit that BI generates often creates the tendency to substantiate the BI results to quantitative standards.
Every BI project has its life cycle. This blog will focus on a common life cycle for business intelligence and how marketers use it.
You will learn how successful project implementation is affected by human factors and the strategies used in business intelligence, like creating a roadmap and project planning
What Is Business Intelligence?
Business intelligence is the process of using technology to analyze and represent raw data into useful information.
Business intelligence understands your business better. The tools you use to implement BI will determine your approach.
In addition to dashboards, reports, data visualization, data discovery tools, and cloud data services, BI tools include data warehouses.
Technologies used in BI to transform unstructured or semi-structured data can also be used for data mining and as front-end tools to work with big data.
How Does The BI Life Cycle Work?
If we think of the maturity model as the different levels of BI in a company, or the destinations of where we want to arrive, then the BI life cycle is how you get there.
This approach is iterative as the BI solution needs to progress when the business develops.
A quick example is a customer who developed a good product for standard selling cost per unit.
This made sense and facilitated them to discover price demands in their market and take suitable action.
What Does Business Intelligence Do?
Business intelligence is known as technology, applications, strategies, and practices that allow business information to be collected, analyzed, incorporated, and shown in a meaningful way.
BI allows setting standards and benchmarks for monitoring performance and providing feedback in every functional area of the business using metrics that extend well beyond traditional financial measurements.
Organizations rely on business intelligence, or BI, to forecast future performance, identify customer insights, conduct quantitative analysis and performance reports, and share data.
BI is a continuous cycle of analysis, insight, action, and measurement as a framework.
Analyzing a business is based on what we know and feel to be important while filtering out the aspects of the business not considered mission-critical or detrimental to the growth of the organization.
BI tools measure what is considered important. The BI term for the most important measures is key performance indicators (KPIs).
Deciding what is important is based on our understanding and assumptions of what is important to customers, supplies, competitors, and employees.
This knowledge is unique to a business and is an incredible resource when formulating a BI strategy.
However, having such granular grassroots knowledge of the business can subconsciously limit the ability to see patterns obvious to others.
BI systems assimilate large amounts of complex data from disparate sources and then combine the data using complicated algorithms for allocating, aggregating, and massaging the data.
Business Intelligence Lifecycle
The first step is to analyze business requirements, which means that we should gather the business requirements and transform them into functional and non-functional specifications, create a template for reports, etc.
The next step is to design the logical data model.
We build a logical data model based on business requirements, which shows the business entities and their relationships.
The third step is to design the physical data model, where we transform the logical data model into a physical data model that defines the data warehouse structure.
The fourth step is to build the data warehouse. We create the data warehouse, build data marts, and load data in this step.
Once you have a team and consider the data sources required for your specific problem, you can start developing a BI strategy.
Using traditional strategic documents such as a product roadmap, you can document your strategy.
While the BI project is“Done” when the reporting is delivered and implemented, more often than not, there are additional questions and values to be derived from the data.
Key Performance Indicators
In a well-designed and comprehensive BI solution, no functional business area is without its own set of KPIs.
The goal is to have managers manage what is manageable, usually KPIs, not dollars.
A clearly defined BI cycle helps companies set goals, analyze the progress, gain insight, take action and measure the results.
What Are The 4 Major BI Tools of a Business Intelligence System?
An analysis of literature published from 2001 to 2010 identifies that there are four most common elements of a business intelligence system:
While data is captured in complex structures and databases to facilitate specific transaction requirements, organizations and businesses find it difficult to extract and capture the required information from data in transaction systems.
Thus, there was a need to develop a system that can dependably take out data from the source systems and restructure the content appropriate for business analysis.
The ETL tool retrieves data from ERP, CRM, analytics, and spreadsheets. Once extracted, the ETL tool starts data processing.
All extracted data is analyzed, duplicates removed, and standardized, sorted, filtered, and verified.
At this phase, transformed data uploads into the warehouse. Usually, ETL tools are provided out of the box with BI tools from vendors.
There are various structural elements of a BI architecture you will have to develop in case you want to create a custom solution for your business.
In other cases, you are always free to choose a vendor from the market that would carry implementation and data structuring for you.
One of the core elements of any BI architecture is a data warehouse.
The warehouse is a database that keeps your information in a predefined format, usually structured, classified, and purged of errors.
It’s the first and biggest element of business intelligence architecture. If marketers don’t preprocess data, your BI tool or your IT department won’t be able to query it.
Various types of solutions present analysts with smaller portions of a warehouse. The most used of them is online analytical processing and data marts.
These technologies offer quicker reporting and easy access to the required data. If you don’t have large volumes of data, the utilization of a simple SQL warehouse would be enough.
OLAP cube is a data structure optimized for quick data analysis from SQL databases (warehouse). Cubes source data from a data warehouse being a smaller representation of it.
However, the data structure assumes that there are more than two dimensions (row and column format of spreadsheets).
Ad Hoc Reporting
One of the benefits of BI reporting is performing ad hoc reporting by drilling down layers of data and pivoting on the rows and columns.
Such flexibility opens up human’s inquisitive nature to ask more questions that they wouldn’t ask if such access to data wasn’t available.
The predictive analysis makes forecasts about future business trends. Those predictions are based on past events analysis.
So, both BI and predictive analysis can use the same techniques to process data. To some extent, predictive analytics can be considered the next stage of business intelligence.
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