Data analytics buying guide part 4: Tips to traverse the procurement process



Elsewhere in this buying guide we've explained:


In the electric utility world, Big Data is a Big Deal. Attend just about any industry conference and you’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 - less than half a billion dollars in North American in 2011 - 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 - created with valuable input from analytics experts at Origin, BRIDGE Energy Group, Space-Time Insight and Opower - is designed to help you get started and know the right questions to ask.

·         What Big Data means for utilities

·         How to prepare for analytics

·         Where analytics can take you

·         How to measure your progress


But of course, none of those other aspects matter unless you also handle the procurement correctly. In this article, we've got a laundry list of questions insiders recommend you ask before you buy plus tips to smoothly traverse the process, from the initial requirements gathering to the contract fine print (which is discussed on page 2).


As you might imagine, the analytics vendor and services landscape is getting increasingly crowded, which makes it more difficult to negotiate. Where to begin? We asked our experts for recommendations on the best buying process and the questions you should ask BEFORE you make a decision on an analytics software program and/or services vendor.


The buying process

You don't need to break new ground here. Start with a standard internal requirements gathering, says Jeremy Oosthuizen of Origin, and ensure your purchase is aimed at meeting them. And, he says, make sure you are procuring for the future.


An RFP process is the next step.  BRIDGE Energy Group also recommends a proof of concept with a solid business case (or partnering with a vendor to co-innovate by building a solution that can be introduced to the marketplace).


12 questions to ask BEFORE you decide on analytics software


1.     Will our users want to use the software?

2.     Will it provide output, with very few clicks or completely automated, in the formats our users want?

3.     Does it allow for design using current data warehousing theory?

4.     Can it access and integrate with all of our diverse data sources?

5.     Is the ETL toolset easy to configure and use?

6.     Can it accommodate for poor data quality and fidelity issues?

7.     How future-proof is the solution across ERP upgrades?

8.     What level of security is offered - and is it enough?

9.     To what extent can you access the source code?

10.  What do the license model and support model look like?

11.  What is the software vendor's track record?

12.  Are their products ready to handle increasing complexities brought by big data?


7 questions to ask BEFORE you select a services vendor


1.     Do they have experiences in the tools you've selected?

2.     Can they provide soup-to-nuts services, or are they just providing bodies to help with some tasks?

3.     Do they have a proven track record for understanding the business and IT objectives?

4.     Can they rapidly prototype the solution and then apply innovation against the production-grade solution?

5.     What level of security can they provide?

6.     Can they provide expertise in all of the following?

a.     Strategy setting and defining the solution

b.    Requirement gathering

c.     Help with product selection and installation

d.    Design of the data warehouse

e.     Configuring the user interface

7.     Do they have experience in the utility industry?


Should you choose the cloud?

Another important consideration is whether to host your own solutions or use a cloud-based approach instead. Internal skill sets, available capacity and sensitivity of data should all be considered and will probably emerge during the requirements gathering process.


"One of the main criteria would be whether the organization has the skills in-house to deal with a data warehouse and BI application, or if they want to acquire those skills," says Origin's Jeremy Oosthuizen. "Having it cloud-based does not negate the need for all technical skills, but it does reduce it greatly."


Steve Ehrlich of Space-Time Insight, however, makes a case for self-hosting. "Big Data solutions are best implemented in a self-hosted environment," he says. "The reliability and the security needs around Big Data transfers are not proven yet."

The fine print

When you've made your decisions and are reviewing purchase and service agreements, there are few clauses you should consider to safeguard your interests.


Ehrlich of Space-Time Insight says an important one is IP protection against innovative business ideas that will be applied with the advanced analytics platform. To that, BRIDGE Energy Group adds a payment structure based on defects, success, survey and ROI achieved as well as clear language on licensing agreements, CPUs, cores and users. They also suggest re-negotiate language and unlimited lab licenses for development and testing.


Once you've asked all the questions and settled on what you want to buy, you'll want to start thinking about how you're going to know if you got it right. We explore ways to measure your analytics success in the fifth and final segment of our buying guide.





From industry sources...

2012 Utility Industry Survey on BI, Analytics and Big Data - BRIDGE Energy Group

California ISO: Bringing State-of-the-art to California's Grid - Space-Time Insight

An Introduction to Situational Intelligence - Space-Time Insight

Managing big data for smart grids and smart meters - IBM

Data validation for smart grid analytics - Utilicase

6 Big Data Lies - InformationWeek


From Smart Grid News...

Strategic Asset Management: A Primer for Electric Utilities

Everybody's talking Big Data, but Echelon gets to the point

Smart grid data analytics: Looming gap will force major expenditures

Smart grid data analytics in the real world

Smart grid Big Data: Survey confirms utility opportunities, but spots blunders as well

Top 5 signs you need Operational Intelligence for smart grids

Just 3 simple steps to drive business value from smart grid data?

Smart grid analytics: Why you should fire your coders (and hire solvers instead)

Smart meters and Big Data: A clear case for governance best practices