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Research Report Audience

  • Utilities
  • Corporations
  • Government Agencies
  • Communities/Cities
  • Research Organizations
  • Investors/Financing

Contents of Report

I. Analytics Framework
A. Moving data from a fragmented level
B. Data Integration
C. Creating relevance in data
D. Automating data
E. Benchmarking Practices
F. Applying Analytics

II. Technology Requirements
A. Software Options/Requirements
B. Hardware Interfaces
C. Data Management
D. Data Visualization

III. Building Skills and Competencies
A. Outsourcing vs. Insourcing
B. Leadership Requirements

C. Training/Education & Curriculum


IV. Solution Case Studies
A. Utility Analytics
B. Community Analytics
C. Corporation Analytics
D. General Business Analytics

Analytical Model

This chart from Oracle's “Utilities and Big Data: Accelerating the Drive to Value” study shows providing usage patterns to customers is the primary use of meter data.
Business intelligence that leads to analytics will perhaps play the biggest role in differentiating businesses and communities from the competition. As our society continues to become more and more digital, the use of data will be imperative for making smarter decisions, smarter investments and faster response to market shifts driven by consumer demands.

In a Harvard Business Review article titled Analytics 3.0 the author writes about the evolution of analytics in our society: “The use of data to make decisions is, of course, not a new idea; it is as old as decision making itself. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could. Today it isn’t just online and information firms
that can create products and services from analyses of data. It’s every firm in every industry.” The author speaks to the three major transformations:
• Analytics 1.0—the era of “business intelligence.”
• Analytics 2.0—the era of big data.
• Analytics 3.0—the era of data-enriched offerings.

The International Data Corporation has provided the following 10 predictions with the greatest potential on big data and analytics:
• Through 2020, spending on cloud-based BDA technology will grow 4.5x faster than spending for on-premises solutions; open source technology will represent the core of this new architecture.
• By 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality.
• Shortage of skilled staff will persist and extend from data scientists to architects and experts in data management; big data–related professional services will have a 23% compound annual growth rate by 2020.
• By 2020, 90% of databases (relational and non-relational) will be based on memory-optimized technology.
• By 2020, distributed micro analytics and data manipulation will be part of all big data and analytics deployments.
• Through 2020, spending on self-service visual discovery and data preparation market will grow 2.5x faster than traditional IT-controlled tools for similar functionality.
• By 2020, data monetization efforts will result in enterprises pursuing digital transformation initiatives increasing the marketplace's consumption of their own data by 100-fold or more.
• By 2020, the high-value data — part of the Digital Universe that is worth analyzing to achieve actionable intelligence will double.
• By 2020, 60% of information delivered to decision makers will be considered by them always actionable, doubling the rate from the current (2015) level.
• By 2020, organizations able to analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.

NESI-SES Research Questions Addressed

The NESI-SES Association is addressing these challenges and opportunities through key research and development in the areas of utility analytics, community analytics, building analytics and general business analytics. Critical to this research is the development of an analytical framework and investment in resources that enable advanced analytics 3.0.

This NESI-SES research project will analyze five focus areas that we refer to as research pillars:

  • Shaping Policy
  • Advancing Technologies
  • Engaging Stakeholders
  • Enabling Solutions
  • Building Skills/Competencies
Shaping Policy Key Questions Addressed
    1. What organizational policies and procedures need to be developed within the utility sector and corporations in general to optimize the use of analytics?
    2. How can a community and its stakeholders take advantage of community wide analytics and leverage this information for increased quality of life within the community? What policies and procedures need to be adopted to enable this cross platform analytic investment and tool?

Advancing Technology Key Questions Addressed

    1. How can data be organized to better enable business intelligence?
    2. What targets and measurements are needed to drive value?
    3. How can tools and methods be developed to create predictive actions?
    4. What changes will be required within institutional behaviors?
    5. How can data be optimized through real-time decisions?
    6. What are the primary converging technologies (hardware and software) that will enable further growth within multiple sectors?
    7. What financial resources will be required to advance the technology and how can the ROR be quantified?

Engaging Stakeholders Key Questions Addressed

    1. What new relationships need to be forged within communities to leverage the value of data?
    2. What new alliances will be required to ensure success?
    3. What vendors or suppliers exist and will they be able to facilitate the needs within corporations and the community?

Enabling Solutions Key Questions Addressed

    1. How can a standardized framework be leveraged cross organizational boundaries?
    2. How can a common control center/analytic team create greater value to independent corporations and the community?
    3. How can tools and methods be used to interface with the IoT?
    4. How can analytic models and use of real-time information improve security, safety and overall quality of life?
    5. What will be the requirements for a data center to facilitate data, data security, uptime, etc.

Building Skills/Competencies Key Questions Addressed

  1. What are the current gaps?
  2. What education will be required to facilitate the complexity and opportunities presented by analytics 3.0?
  3. What curriculum is needed within higher education to meet the future job demands?
  4. How can mentorship programs be created to add overall value and expedite the supply to meet the demand?

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