The Need For Applied Analytics
In the age of Big Data and Analytics, it is surprising to see that many organizations still struggle with the ability to extract meaningful information from their data repositories, information that can give them invaluable insight into better understanding their operations.
Purchasing complex and expensive tools for reporting & analytics or expensive storage devices for the collection of vast amounts of data doesn’t appear to be a challenge for most organizations. However, the challenge comes into play when these complex tools have to be used to mine the vast data for insight and foresight.
Challenges that organizations typically face are :
Patchwork solution for reporting and measurements : Many organization lack the necessary structure to implement a robust reporting and measurements program. Without properly defining the related roles and responsibilities, processes and procedures, standard set of operational and service level reports using defined templates, etc. organizations will struggle with operationalizing any efforts and producing quality and audit-ready reports that can be used for analytics.
Complex reporting tools : In many organizations, these overloaded tools can come with hundreds of tables, thousands of fields, and unclear or complex data dictionaries. Making the process of building much needed reports a challenge.
Lack of enterprise standards : In the absence of standards, each team determines what reports will be built, how the reports are built, and what information will be presented. In many cases, you have different teams, measuring the same KPI, but providing different values, introducing audit risks. These ad-hoc and unstructured reports are loaded with numerous charts, untested formulas, and pages of useless information.
Data quality : Another challenge facing organizations aside from the availability of robust reports and easy-to-use reporting tools, is that the data itself is lacking in quality. Teams aren’t documenting all the relevant information to define the problem, how the problem was resolved, or aren’t properly classifying the problems so meaningful reports can be generated.
Skillsets lacking in Continual Improvement Initiatives, specifically development and usage of Process Behaviour Charts (PBC) : Even if organizations were to overcome the significant problems with using the complex reporting tools, quality of data, and development of standardized operational and service level reporting packages, a significant challenge still exists in how to analyze the data to better understand the behaviour of the process. Building, interpreting, and using PBC charts itself can be a challenging task.
The absence of 1 – easy to use reporting tools, 2- meaningful, structured, and audit-ready reports , 3 – quality data, and 4 – skillsets specializing in reporting & measurements/continual improvement, ultimately results in a failed continual improvement program. Processes such as trend analysis, root cause analysis, defect prevention, and any other initiative to conduct analytics on the data to better understand the operational behaviour of the enterprise, so areas of improvements can be identified, will not exist. The net impact to the organization as a result is, lowered 1 – Customer Satisfaction, 2 – Revenues, and 3 – Profits.
Rather then invest in a heavily loaded and complex tool that comes with significant licensing, support, and training costs, Jnana IPC Analytics, is a light-weight, yet powerful tool, that has been built based on 17+ years of resolving operational issues across global companies. 18 core functions have been identified that provide breakthrough insight and foresight into the operational behaviour of the enterprise.
These functions allow the organization to conduct an efficient quantitative and qualitative analysis of the data to identify the root cause of disruptions and areas of improvement. The design of the functions guides the User through a logical process to better understand the data. The methodology provides the User with the important starting point to begin the analysis and provides a step by step walk-through. The process will not only help to answer the question, WHAT happened, but WHY it happened. Understanding the WHAT and WHY will allow for an improvement in uptime and availability, resulting in improved customer satisfaction, revenues, and profits through operational efficiencies.
The Jnana IPC Analytics tool is part of the M3 Framework. A tried and tested framework that has been used in some of the largest corporations in the world.