Business Intelligence Development Life Cycle – Wikipedia summarizes it best as “A set of techniques and tools for transforming raw data into meaningful and actionable information for business analysis purposes” because it covers the main aspects of 1) techniques – such as dimensional modeling that make raw data fully meaningful for business analysis , 2) transformation needed to contextualize raw data into information to gain knowledge, 3) tools needed for transformation or ETL/ELT, will be further expanded – read on 4) purpose. This brief definition summarizes BI.
Purpose is important, organizations should not start BI initiatives for the sake of building a data warehouse. The drive should be to make decisions based on data to support business strategy. Infied, captures the importance of acting on knowledge gained in his publication: Leveraging Knowledge as he observes: “unless knowledge leads to informed decisions or actions, the whole process is useless”
Business Intelligence Development Life Cycle
Reflecting on various business intelligence initiatives I’ve contributed to in various roles from data warehouse architect to integration developer, I found that Kimball’s lifecycle model doesn’t just capture groups of dependent tasks; more importantly, it provides guidance for executing tasks in parallel. We follow these guidelines during various implementation projects
Steps In The Data Life Cycle
Two different schools of thought have emerged about how to implement a data house, a core data repository designed to support business intelligence. Both are Inmon and Kimball. There is often confusion that Inmon does not support dimensional modeling, but this is not the case, both approaches support dimensional modeling (denormalization of data for analysis purposes). The key differentiating features in the approach are as follows: The addition of machine intelligence in our business workflows has now become the norm, and more and more data-driven predictive analytics are being developed and integrated into existing business operations to aid decision-making, improve efficiency , reduce risk and improve employee experience.
However, with the proliferation of analytical models and AI being generated, we face the challenge of effectively managing the analytical lifecycle to ensure that those models deliver strong business insights that lead to optimal decisions, identified opportunities and the right actions. It is a varied and complex task.
Below is my take on managing the entire analytics lifecycle, providing a step-by-step guide through the process of formulating a business problem, developing and deploying analytics, and finally decommissioning analytics. Note that although the process is presented sequentially, it is indeed an iterative process that can be used to produce repeatable and reliable prediction results.
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Any analytics or AI solution starts with a business problem or business use case. Only when the business requirements are clearly understood can analytical solutions be designed and developed with projected business outcomes. This is also the stage where business sponsors must be secured who are committed to using the analytics developed in the proposed use cases, should they meet the original design goals. Often projects fall into the trap that their analysis output is not adopted by any potential users, thus all efforts are wasted. Consequently, it is critical to engage business sponsors and co-create solutions with them at an early stage of the project.
Two other aspects of problem formulation include project scope definition and success metrics. This is the time to ask critical questions such as: What is the scope for this project? Does it include the entire employee population, or just a specific business unit, geo, country, segment? How do we define success? What success criteria should be measured? What is the current state, and what is the future state? What will the target user experience indicate in the future? Getting answers to these questions is important to the Solution Design stage.
At this stage, a lot of analytical modeling and machine learning algorithms are applied to the data to find the best representation of the relationships in the data that will help answer the business questions or address the business needs identified in the first phase. Extensive model building, testing, validation and calibration will be performed accordingly. Another key area in this development stage is data quality assurance which ensures data quality at various stages of development based on business and analytics requirements.
What Is Business Intelligence? Bi Definition, Meaning & Example
AI trust has become an increasingly important aspect of any machine learning-based solution. At the end of the day, if end users don’t trust the insights, predictions or recommendations generated by AI solutions, they will ignore the data. Thus, ways to increase data transparency, demonstrate the fairness and robustness of AI output, and make AI decisions explainable become critical to driving adoption of solutions. There are several tools developed by IBM Research to promote AI trust, including AI Factsheet 360 (https://aifs360.mybluemix.net/), AI Fairness 360 (http://aif360.mybluemix.net) and AI Explainability 360 (http://aif360.mybluemix.net) ://aix360-dev.mybluemix.net). A lot of good information can be found there.
The final step of this stage is to interpret the insights in a business context, share them with business stakeholders and validate them. The information gathered from the AI trust assessment will be very useful during the review.
This is the stage where we take the developed solutions and insights, and implement them using a repeatable and automated process. Campaigns and activation sessions to target user groups are a great way to raise awareness of the solution which ultimately drives adoption of the solution. On the other hand, integrating solutions into existing operational flows is another good way and in fact an option to drive their adoption and achieve business results. Sometimes a certain level of change management is required to incorporate the results of analysis into previous processes, and strong support and encouragement from sponsors and business stakeholders will make this process easier and faster.
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Note that there may be a need for a Solution Trial stage immediately after development, to validate the solution on a relatively small scale before scaling it up. Once the team has verified that the pilot results indeed meet the established success criteria, along with the approval of the business stakeholders, it can move to the solution deployment stage.
After the solution is deployed and deployed, its performance should be monitored, usage should be tracked and feedback should be collected, to support any potential solution adjustments and improvements in the future. Conversely, if serious defects or serious performance issues are indeed identified, the team may need to return to the solution development stage.
Once a solution has been successfully deployed and its usage is continuously tracked, it is time to measure success. Success metrics defined during the Problem Formulation stage will be measured and reported at this stage. Other metrics may include ROI in terms of cost savings or revenue generation, as well as NPS based on feedback.
What Is Business Intelligence (bi): Complete Implementation Workflow
Once the solution is in steady state, it can be switched to BAU mode. This is also a time to streamline/automate processes as much as possible to make them more efficient with minimal maintenance or human intervention. Periodic model updates will also occur during this stage.
In addition, with continuous monitoring and measurement of model performance based on standardized metrics, the team must constantly evaluate the validity and effectiveness of the solution to see if it still meets the current business needs. If gaps are identified, the model needs to be improved and recalibrated. This can lead to a cycle back to solution development, or even to solution design if business needs have evolved over time, existing data sources are no longer valid or new data becomes available. This ultimately makes lifecycle management an iterative process.
If the original business need is no longer valid, a new solution with improved capabilities has been developed, or the customer has moved on, the existing solution is no longer needed and it is time to retire the model.
Business Intelligence (bi) Life Cycle: A Complete Guide.
After reviewing the end-to-end analytics lifecycle, I’d like to point out a few things that could potentially complicate, slow down, or even compromise this entire process.
Ying Li manages the Data Science team at IBM CHQ HR, leading projects that develop advanced people analytics to support leaders in making important decisions. Business intelligence is not just a set of tools to analyze raw data to help make strategic and operational decisions. It is a framework that provides guidance for understanding what to look for in different amounts of data. As a framework, BI is a continuous cycle of analysis, insight, action and measurement.
The analysis of the business is based on what we know and feel is important while filtering out aspects of the business that are not considered mission critical or detrimental to the organization’s growth. Deciding what matters is based on our understanding and assumptions about what matters to customers, supplies, competitors and employees. All of this knowledge is unique to the business and is an incredible resource when creating a BI strategy. However, unconsciously having a detailed ground level knowledge of the business
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