Inc. magazine ranked ValuD Consulting #1085 on its 36th annual Inc. 5000, the most prestigious ranking of the nation’s fastest-growing private companies. Out of the nearly seven million private companies moving the economy forward every day, only a tiny fraction demonstrate consistent high growth. ValuD’s Inc. 5000 recognition, for the third consecutive year, puts the firm in rarefied company, given that only one in five companies make the list three times…
To propel results over the past year, ValuD has:
- Grown to 230 people to deliver IBM TRIRIGA, IBM Maximo, and Motors@Work Internet of Things solutions.
- Built an industry leading FASB/IASB Lease Accounting practice, complete with CPAs, to facilitate rapid TRIRIGA Real Estate implementations and to streamline FASB compliance.
- Developed and delivered MobilD, a mobility product solution purpose-built for TRIRIGA.
- Enhanced pre-packaged Motors@Work integrations with Maximo Asset Management, Maximo Asset Health Insights, and IBM Watson Platform.
[Admin: This post is related to the 08.18.16 post about being recognized for a second straight year. To see other related posts, use the ValuD tag.]
Watson Analytics is an intuitive, cloud-based analytics tool that will help TRIRIGA users explore their data for insights and display findings as dashboards and infographics for easy sharing. All without having to become a data scientist.
Watson Analytics guides data discovery and predictive analytics by using automatic visualizations and enables dashboard creation so that you can get insights and make informed decisions. Pairing Watson Analytics with data from TRIRIGA enables you to explore your integrated workplace management processes and obtain new insights on your day to day operations. The IBM Watson Analytics service will allow you to discover patterns and meanings in your data.
The TRIRIGA Connector for Watson Analytics gives you the ability to export your TRIRIGA data into Watson Analytics. With Watson Analytics, you can tune data, discover insights to help solve your business problems, and display your findings…
[Admin: To see other related posts, use the Watson tag or Analytics tag.]
Improving maintenance procedures through predictive analytics has recently seen many notable developments. Siemens is integrating IBM Watson Analytics into its MindSphere platform to help implement predictive maintenance schemes. Rockwell Automation recently launched its predictive analytics capability for factories and machinery. And PTC and Deloitte Digital announced that they are jointly developing predictive maintenance solutions for factory operations.
These developments are hardly surprising. Predictive analytics enables users to detect if, and when, machinery is likely to breakdown. For example, predictive analytics enabled Duke Energy to identify a slight increase in the turbine vibration of a steam turbine – after maintenance was performed. The subsequent repairs resulted in $4.1 million of potential power generation loss being prevented. Facility managers at plants and factories are therefore likely to be looking for such solutions to keep things running.
But is predictive analytics a natural sell in the world of commercial real estate? There is certainly some evidence of this. Since 2009, UK-based retailer Sainsbury’s has used software provider Verisae’s (now part of Accruent) predictive maintenance solution to reduce product loss from refrigeration failures. Since 2016, engineering firm KONE has partnered with IBM Watson to embed intelligent analytics in its elevators and escalators to improve their performance and reduce instances of unplanned maintenance.
Nevertheless, such examples are not necessarily the norm. According to our 2016 Global Energy Leaders Survey, 46% of the 250 facility managers surveyed said improving the collection, analysis and reporting of energy data from their electrical assets is a very important priority. In contrast, more granular asset-level energy management was only considered very important by 22%. And in our 2015 Green Quadrant for Building Energy Management Software, only 18% of the customer panel we interviewed considered maintenance scheduling and predictive maintenance to be a very important asset management functionality.
The average facility manager is therefore more likely to be concerned with basic data capture than advanced solutions like predictive analytics…
[Admin: This post is related to the 07.13.17 post and 03.16.17 post about IBM Watson Analytics, and the 08.01.16 post about the competitors of IBM TRIRIGA. To see other related posts, use the Verdantix tag.]
Is there any way to embed the HTML code used for Google Analytics (or similar technologies) within the TRIRIGA code?
[Admin: This post is related to the 06.05.16 post by Dennis Salguero about Watson Analytics and Cognitive IoT.]
In November, we announced the launch of our TRIRIGA Connector for Watson Analytics – letting business professionals and facility managers easily integrate their TRIRIGA buildings data directly into Watson Analytics…
Self-service analytics: The answer to the data skills-gap
Today, getting access to the information you need to run your business probably means working across a number of different roles and teams: IT to get you access to your data, business analysts to help make sense of your data, and data scientists to help build predictive models. But the time and cost associated with getting the insights you need to solve daily problems can be prohibitive…
This is where Watson Analytics comes in. The easy-to-use interface is designed with business users in mind, enabling guided data discovery, predictive analytics and automatic visualization. For real estate and facility managers, Watson Analytics with TRIRIGA is your data scientist in a box…
How will you use your new insights?
The cognitive power of Watson Analytics allows it to uncover important and unexpected insights hidden in your data. You may uncover energy usage trends that you didn’t know were impacting your bottom line, or use Watson Analytics to reduce under utilized space and help lower your cost per square foot.
By exploring lease data, you might predict future capacity and achieve standards compliance. Or you may drill into your maintenance data for insights that will reduce operations costs and simplify service delivery…
[Admin: This post is related to the 11.15.16 post about the IBM TRIRIGA Connector for Watson Analytics overview.]
When learning about new technologies and tools, it often helps to get one’s hands just a little bit dirty and see what really makes them work. That’s the idea behind the new Bootcamp labs at IBM InterConnect 2017, March 19-23 in Las Vegas.
These instructor-led labs run 3 to 4 hours, giving enrollees the opportunity to do hands-on work with new products and technologies. Attendees can find a deeper dive in these sessions led by subject matter experts…
IoT Platform to Maximo/TRIRIGA hands-on lab
Thursday, 8:30 AM – 12:15 PM, Mandalay Bay South Seas I1, Session ID: 7436A
This lab offers attendees a basic understanding of how connected operations work. Use a simulated temperature sensor (a gauge meter in Maximo) to send a temperature reading to the Internet of Things (IoT) Quickstart. The message is then sent to NODE-RED, which parses the message. When a reading changes, it goes into a REST-API call that inserts the meter reading into the referenced asset meter readings. The reading updates the measure point and triggers a work order using Maximo functionality, or if one chooses to do so, using a TRIRIGA work task…
[Admin: This post is related to the 02.06.15 post about exploring the TRIRIGA user group community, and the 06.07.16 post about using sensors with Watson IoT to create TRIRIGA work tasks.]
Is there a list of keywords that IBM Watson Analytics uses in column headers to try to determine properties? Could “Gross” be a keyword for currency? Once set in Watson Analytics, it doesn’t appear to be converted.
For example, look at the out-of-the-box “TRIRIGA Buildings & Space Allocation Data” set. Gross Area shows as currency ($). Others are too: Area/Occupant (Gross), Gross Rentable. But not all number columns are, ones that don’t include “Gross”. The UOM doesn’t appear to be defined in the properties, so it seems like it must be using the column header? I haven’t tried changing the header, but as-is, once exported, is there a way for that column to show correctly as an area instead of currency?
This question is best suited for the IBM Watson Analytics forum. There are posts on that forum that indicate that Watson Analytics does look at column names when deciding whether to add currency values, but I did not see a list of all the special keywords. Other than changing the column name before upload, I don’t know of a way to override those special keywords or modify the UOM that Watson Analytics assigns in the data set. So these questions will be better answered in the Watson Analytics forum. That forum is also the place where you can suggest enhancements around this Watson Analytics behavior.
[Admin: This post is related to the 12.15.16 post about finding information about the IBM TRIRIGA Connector for Watson Analytics, and the 11.15.16 post about the overview and demo. To see other related posts, search “Watson Analytics“.]