Organizations that are succeeding despite the down economy are using automated analytical tools for a more scientific approach to decision making. Their results? Innovative and revenue-generating improvements and savings. Find out how by downloading this insightful whitepaper, a compilation of timely articles highlighting how people, processes and technology need to be on the same page to derive the most from analytics investments.

What if you could take 8 essential steps toward being an analytical company that can easily identify your most profitable customers, accelerate product innovation and understand the true drivers of financial performance? You can. Download now to learn more about what business analytics can do for you – and how to get started.

Just as the advent of the Internet represented a new dawn in personal
connectivity and communication, the smart grid infrastructure, including smart
metering and demand response mechanisms, is replete with potential benefits
for the utilities industry.

This white paper explores how you can use analytics and data management software to create a predictive view of AMI and smart metering data. The potential benefits are staggering: improved power grid reliability, efficiency and safety; reduced carbon dioxide emissions; and two-way communications with consumers who can benefit from better pricing options. You will learn how to:

• Manage large volumes of meter data with proven data management techniques
• Provide data security and audit trails
• Build interactive data delivery portals
• Unlock the information inside data with quality analytics

The Mechanics of Forecasting There is no shortage of articles, books, consultants and vendors to tell you - or sell you - their version of forecasting best practices. This white paper takes a different approach. Before you can choose and implement the best practice approaches that make the most sense for you, we recommend first focusing on avoiding potential pitfalls and common fundamental mistakes. The surest way to achieve process improvements is by identifying and eliminating the worst practices in forecasting.

Even while the utility industry is challenged daily with constant change, many workers and executives aren't always prepared to use all the statistical modeling tools available to them for managing it. This white paper explores how a tool for managing unpredictable change - statistical forecasting - may not be acceptable to people within the organization that are responsible for using it daily. The paper describes how, when adapting statistical software, a company must assess and address the organization's readiness for it. This paper uses a fictitious company as the backdrop for how the Strategic Value Assessment process can map manufacturing processes to the needed performance improvements. It also provides structure for analyzing a company's state of readiness for statistic-based changes. The net result is the development of a forecast road map that outlines a phased approach for introduction of appropriate data integration, analytical and business reporting techniques.

The job of the utility company CFO has changed dramatically over the last 10 years. Events like the collapse of Enron, the resulting SOX legislation and establishment of the Public Accounting Oversight Board have resulted in board members and business leaders requiring finance to excel at its core mission, getting the books right. The evolution of energy services companies, regulators' demands for transparency, the need for multiple books (FERC, GAAP) and the business' need for more information about finances create need for improved CFO office capabilities and simultaneously a shift from bean counter to strategic adviser. This white paper provides five key questions that CFOs should be concerned about as this evolution occurs - questions like managing regulatory compliance, enhancing revenue and cutting costs - and presents SAS Financial Intelligence, the SAS vision for a single, holistic answer for helping the finance department act as strategic adviser to the company.

Utilities companies are constantly required to change. Whether it's building new plants that produce fewer emissions, finding ways to perform with lower costs or readying itself for merger or acquisition, utility companies must respond to many competing outside circumstances. This whitepaper offers thoughts on improving performance despite this tumult, through utilization of management technology. It describes how good performance management integrates reporting, management and control by aligning strategies, resources and finances. It discusses how even greater benefits can be achieved when performance management is continually monitored and improved by management having accurate answers to critical questions. This paper also focuses on improving performance by integrating analytics into performance management and educates executives about how predictive analytics, when embedded into the performance management process, can empower organizations to gain greater insights from their past. By knowing past performing strengths and weaknesses, the utility can provide the change required for the future.

Because the utility industry is buffeted by risks that vary by the minute - weather, energy consumption and fuel costs to name but a few - their risk management systems need dynamic methodologies that account for non-normally distributed risk factors. This paper describes SAS Dynamic Risk Factor Modeling that enables the risk manager to develop models for specific risk factors, normal or not, and to integrate them with other factors. Dynamic Risk Factor Modeling can provide a clearer understanding of actual risks in the portfolio and enable risk management practitioners to efficiently fit multiple risk factor models simultaneously. This whitepaper discusses how models can be created for risks whose impacts are normally or non-normally distributed, like hurricanes affecting gas supplies. In model-based copula simulations, fitted risk factor models can preserve the appropriate form of the distributions and correlations that interrelate risk factors, generating analyses that provide precisely focused snapshots of portfolio risks.

Because the utility industry is buffeted by risks that vary by the minute - weather, energy consumption and fuel costs to name but a few - their risk management systems need dynamic methodologies that account for non-normally distributed risk factors. This paper describes SAS Dynamic Risk Factor Modeling that enables the risk manager to develop models for specific risk factors, normal or not, and to integrate them with other factors. Dynamic Risk Factor Modeling can provide a clearer understanding of actual risks in the portfolio and enable risk management practitioners to efficiently fit multiple risk factor models simultaneously. This whitepaper discusses how models can be created for risks whose impacts are normally or non-normally distributed, like hurricanes affecting gas supplies. In model-based copula simulations, fitted risk factor models can preserve the appropriate form of the distributions and correlations that interrelate risk factors, generating analyses that provide precisely focused snapshots of portfolio risks.