Business Intelligence through Data Analytics - A “Big” Data Driven Road Map for Decision Making

Authors:

  • Aditya Ramamurthy, PMP, ENV SP - Hazen and Sawyer

Simple data analytics reveal basic insights; more sophisticated analytics, applied to data that has been pooled into a “data lake” with data from external and enterprise sources allows utilities to unearth deeper insights that will help to optimize performance. Because of the growing volume, complexity and strategic importance of asset management data, it is no longer desirable or even feasible for each department/ unit/division/function within a utility to manage this data by itself, or to build its own data analytics capabilities.

To get the most out of the new data resources, utilities are creating dedicated data groups that are potentially embedded within the core asset management program team to consolidate data collection, aggregation and analytics.

Three trends have emerged in the data management realm – cloud computing, mobile computing, and explosion of data. Utilities are collecting more data than ever before. However, the challenge facing utilities is their inability to convert all the data into meaningful & usable information. Over the past year, self-service business intelligence tools have provided the necessary capabilities for utility staff to process and analyze data to produce meaningful insights. Advances in technology have revolutionized data and performance reporting so that users (with limited IT development expertise) can perform data mining and develop high impact visuals for performance reporting. Water and Wastewater Utilities are implementing Business Intelligence (BI) frameworks to track and report key asset management performance indicators and other data analytics.

Benefits of this business intelligence reporting framework include:
1. Eliminates the reliance on core IT developers to develop and manage reporting frameworks as BI is now integrated with common applications, putting the non-IT user in a position to perform complex data analysis and develop aesthetically-pleasing visualizations
2. Significantly reduces development cost and level of effort
3. Through the concept of data “lakes”, data models can be constructed using data from various sources (CMMS, GIS, SCADA, project management, financial and customer information systems) with ease
4. Eliminates the extensive costs and need for complex and disparate system integration that is typically required to connect data for effective performance reporting
5. Reduces the time to develop high impact visualizations to hours or days, rather than weeks, months, and years
6. Complete transferability to mobile devices for use at meetings and workshops

This presentation will discuss the utility management business intelligence frameworks (2 case studies) that have been implemented by utilities for effective integration, tracking and reporting of various data within their organization. The main purpose of this paper is to discuss the value generated by two utilities implementing business intelligence through data analytics and how business intelligence aligns with the 3 data trends (cloud, mobile, and explosion of data).

For more information, please contact the author at aramamurthy@hazenandsawyer.com.

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