Data analytics buying guide part 2: Creating an analytics-ready workplace
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In the electric utility world, Big Data is a Big Deal. Attend just about any industry conference and youâ€™ll hear folks talking about the data deluge swamping utilities as smart grid technologies like smart meters and sensors and Phasor Measurement Units (PMUs) go live.
And here's why it's such a big deal: Utilities are starting to use advanced analytics software to turn that data into actionable business intelligence. If market forecasters have it right, the industry is just getting started down that path. Utility spend on data analytics â€“ less than half a billion dollars in North American in 2011 â€“ is expected to swell 29% a year to $2 billion in 2016, according to the Utility Analytics Institute. Worldwide, Pike Research expects cumulative spending on smart grid data analytics to total over $34 billion by 2020.
So if exploring data analytics options is on your agenda, this five-part buying guide â€“ created with valuable input from analytics experts at Origin, BRIDGE Energy Group, Space-Time Insight and Opower â€“ is designed to help you get started and know the right questions to ask.
But before you can start kicking tires and looking under the hood at analytics toolsets, you need to assess how ready your organization is â€“ and what it's going to take in terms of people, processes and equipment to be ready. For instance:
Â· Do you have a business case that has executive support?
Â· Who will own your data analytics program; where does it fit within a utility?
Â· Is your organization siloed, or will it easily embrace cross-team data sharing and analytics project planning?
Â· Do you have in-house analytics expertise or will you need to hire, train or bring in outside consultants?
Â· Do you have the basic infrastructure to accomplish advanced analytics?
Winning executive support
Any major business initiative with a price tag attached is going to need executive sponsorship â€“ particularly so when the initiative is so closely linked to driving business value.
Some utilities will be fortunate to already have a strong business sponsor, suggests Jeremy Oosthuizen of Origin. If not, he says one has to be found and cultivated. His advice? "Create a small quick win early: Think big, but start small. Show the potential ROI by pointing out a) money to be saved, b) efficiencies to be gained with internal processes, c) opportunities for revenue protection and d) monetization of current data sources."
Because you may have only one opportunity to show executives that the initiative can add value, Oosthuizen says it is a good idea to get consulting help to create the business case and the strategy to take to executive leadership.
BRIDGE Energy Group's Ethan Cohen agrees that demonstrating fast ROI is the ticket and suggests performing a proof of concept or delivering incremental value quickly to win executive support.
Who owns analytics?
To be useful, an analytics initiative needs active participation from throughout an organization. But it also needs an owner. One idea from Origin's Oosthuizen is to create a BI Center of Excellence â€“ a cross-functional group with a reporting line directly to a VP or similar level executive on the business side, not IT.
A slightly different suggestion comes from Cohen, who suggests creating a business analytics group that reports to the CIO. "The group should not be an IT group but rather they should work closely with IT, with the BI group and with the data warehouse group so that they have access to data," he says.
Recognizing that smaller utilities may not have the luxury of staffing such a group with full-time people, Oosthuizen says it should still be a go-to group that everybody in the organization is aware of.
Embracing cross-team initiatives
The advent of smart grid technologies is forcing utilities to rethink their organizational structures and break down traditional silos. The merging of IT and OT is one example. A data analytics initiative is another reason to shift the organizational mindset toward a more collaborative environment where employees in every department are focused on a shared set of business objectives.
"Utilities can no longer afford to settle for the status quo," notes Cohen of BRIDGE. "The champions for change must establish metrics and measure the business process at each step of the way. Eventually innovation â€“ or doing business differently â€“ becomes second nature and is no longer a 'special project' requiring significant management oversight."
Finding the right skill sets
One concern for many utilities will be whether they have to hire a new team to run analytics initiatives.
Larger utilities may already have staff with the required skill sets or with skills that can be updated relatively easily via training programs or the temporary use of outside consultants. Smaller utilities may find the latter the best option if there are no obvious in-house candidates.
Still, Steve Ehrlich of Space-Time Insight notes that the next breed of analytical solutions utilizes captive talent within an organization, although "analytical skills around statistical computing are always a plus."
Once an analytics program is in place, Oosthuizen of Origin says power users and analysts should receive in-depth training, which may include the principles of analytics and data warehouse layout as well as the features and functions of the toolsets and how to configure them.
"Other users may only need cursory training on how to sign on and access pre-built reports, with or without parameters," Oosthuizen adds. "Others may need virtually no training, since they may never access the analytics system but get their reports emailed to them."
To be analytics ready from an equipment standpoint, BRIDGE Energy Group recommends the following:
Â· Appliances (Neteeza, HANA, EXADATA, etc)
Â· Columnar data storage (typically comes with appliance)
Â· Shared nothing architecture
Â· As much cache (memory) as possible
Â· For ultimate solution avoid data storage on a device, go for memory resident solution (such as SAP HANA)
Â· Relational database and Columnar database, OLAP
Â· Data visualization
Â· Highly redundant/HA
Â· Guaranteed delivery
Â· HA â€“ high availability
Â· High speed
Â· Professional services
Â· Analytics focused, deep industry knowledge
Â· Proven delivery record
Now that you have a sense of what it's going to take to make your workplace analytics-ready, the next step is to determine where you want analytics to take you. We focus on some of the options in part 3 of our buying guide.
From industry sourcesâ€¦
2012 Utility Industry Survey on BI, Analytics and Big Dataâ€“ BRIDGE Energy Group
California ISO: Bringing State-of-the-art to California's Gridâ€“ Space-Time Insight
An Introduction to Situational Intelligenceâ€“ Space-Time Insight
Data validation for smart grid analyticsâ€“ Utilicase
6 Big Data Liesâ€“ InformationWeek
From Smart Grid Newsâ€¦