SI World 2015: SMUD Keynote Presentation on Analytics and Policy

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What criteria do policy makers rely on when selecting a path forward, and how does analytics play a role in that selection? Once a decision is made, how do staff implement it?

In the keynote at the upcoming SI World 2015, leaders from Sacramento Municipal Utility District (SMUD) will map out how their organization relies on analytics in making and implementing policy.

SMUD Board Vice President Nancy Bui-Thompson and Chief Grid Strategy & Operations Officer Paul Lau will present “The Role of Analytics in Developing and Implementing Policy.”

SMUD is a recognized innovator in electricity distribution and delivery. Their real-time visual analytic systems integrate data about distribution operations and maintenance, vegetation, outages, solar power, electric vehicles, weather and more to support confident decision making across the operations and strategic planning.

This year, SI World takes place Wednesday, October 28, in New Orleans. Registration is still open.

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SI World Special Registration Offer – Limited Time

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SI World

SI World is the leading conference exploring real-world results that organizations are achieving through situational intelligence.

Come hear industry-leading organizations such Florida Power & Light, Union Pacific and Sacramento Municipal Utility District (SMUD) describe their results and lessons learned from making real-time visual analytics a reality in their operations and strategic planning.

This year’s conference takes place Wednesday, October 28, in New Orleans and is co-located with Utility Analytics Week.   Register before October 1 and receive 50% off the registration fee.

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Analytics: The Valuable “A-ha” Moment

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Businesses and other enterprises invest in advanced analytics and situational awareness solutions to improve operations.  The value of this investment typically equates to one or more of the following benefits: customer satisfaction and retention, competitive advantage, cost savings, productivity improvement, workflow consistency and business agility, as well as being more responsive to actual or likely problems, failures, service disruptions, and crises.  The value and benefits are often visible, measurable and aligned with the impetus for installing the solution.

Other ongoing secondary benefits and value from these solutions can come about by happenstance, arising organically when end-users have an “a-ha moment” and apply their system, data, and/or other resources to purposes other than what it was originally intended.  Another serendipitous way of finding value is by performing “what if” analyses.  These types of secondary benefits come about by finding value hidden in your data.

Hidden value

Finding value hidden in your data brings about substantial benefits because many small optimal decisions and corresponding favorable outcomes can equal or exceed gains from a single large-scope optimal decision or action.  This is especially true if the smaller optimal decisions are repeatable and support recurring situations.  Benefits such as these do indeed positively impact top-line revenues, bottom-line earnings, and the common key performance areas that I listed in the opening paragraph.

Consider that your data may be generated from multiple systems and stored in databases and repositories that are a tightly coupled component of those systems.  As an example, your purchase transaction data is in an order management system, your production data is in an ERP system, your financial data is in a general ledger system, your customer support interactions are in a CRM system and your customer loyalty program data is in yet another system.  Some of these systems may run in-house and others in the cloud.  They may even be located in different geographies, including different countries.  Hence your difficulty in seeing or readily finding value in all of your data.

There is correlation between most if not all of your data.  Each purchase has a correlation to the production data, the financial data, the loyalty data, and possibly the customer support data.  However, because the data is often in various formats, systems, repositories  and locations, it’s impossible for a human to sift through the data to find either overt or subtle correlations that reveal the value hidden in your data.  The situation is exacerbated by data streaming from Internet of Things (IoT) devices.

These same issues also make it challenging for data processing systems, including analytics and business intelligence (BI), to find value hidden in your data.  Data processing systems should be up to the challenge, given their strength – performing highly repetitive and iterative tasks involving large amounts of data.  However, finding value hidden in data, especially if that data is spread across systems and in silos, requires specific architectures and technologies to overcome these challenges.

Identifying correlated items and events is more likely to occur, and occur more often, by architectures and technologies that aggregate data from multiple sources and feed that data to advanced analytics, machine learning, pattern matching, statistical methods and complex models.  Such correlations lead to value that can be found and brought forward for decision-making and action.

Here are some examples of found and realized value that was hidden in data:

  • Awareness of not using the most economical way of transporting items, and adjusting accordingly
  • Identification of excess cargo capacity while optimizing shipping routes
  • Identification of assets most likely to fail by drilling-down into key performance indicators

If you have found and benefited from finding value hidden in your data, please share your “eureka” story with us.

For more on this topic of finding value hidden in your data, please see the article “Through the Looking Glass: Critical Asset Insight and Transparency Increases Operational Efficiencies & Customer Confidence.

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Visual Analytics, With Empathy

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Business isn’t empathetic. Let’s face it, it just isn’t. For many of us, business happens inside a building lit by the lingering fluorescence of a good weekend. We are wizards at piloting Excel and chanting quantitative mantras. We are great at drill downs and analyzing historical data. And if we are lucky we will make one or two really good decisions a quarter, based on some column charts and a lot of intuition.

The rest of it is a crap shoot. While discussion groups on LinkedIn are singing the praises of ‘Business Intelligence’ I can’t help but wonder if something is missing from the equation. I’d posit that BI is a solution looking for an answer. The reason is that BI happens in the vacuum of the past and through the lens of equations void of real-world context.

As a user experience (UX) designer, I was taught to ask empathetic questions about the situation surrounding a problem. Algorithms are useful but they don’t always adapt themselves to the current context. Good UX means asking, “What is happening in the situation around me? What is the problem right now? What do my team members at work care about? What will my child want for their birthday? Where will I park downtown tomorrow night?” These questions are everyone’s concerns. They are also business problems, every one of them. If you fail at one the others suffer because everything is connected, not sliced and diced.

To answer questions with empathy, you need real-time awareness of conditions plus insight from the past to predict the future. What is true now may not be in 3 minutes, 3 hours or 3 days. BI is a measure of the past. It is two-dimensional and quantitatively vapid. It isn’t asking raw questions.

Situational intelligence, on the other hand, strives to analyze and present what the conditions are across the spectrum of time. It seeks context and not just quantity. In this way, it provides empathy to users solving a problem.

As an example of empathy and situational intelligence, consider the problem of planning a trip to a congested urban area during rush hour. Some people want to save money on their trip, some want to save time, and some want to balance both. Other people don’t care how long it will take or how much they will need to spend, as long as they are safe.

This simple traffic dashboard speaks to all or just some of these concerns. It requires only 3 APIs and connections to a handful of databases (Google real-time and typical traffic, Trulia crime statistics, video feeds from web cameras). It gracefully accommodates the 3 Vs of our Big Data world: volume, variety, velocity.

Traffic dashboard

But it also has empathy. The dashboard adjusts throughout time and at a moment’s notice to the concerns of the user based on cost, time or safety in the past, present or future.

BI is about a process. Situational Intelligence is about people and their unfolding story.

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