With new possibilities for enterprises to easily access and analyze their data to improve performance, data modeling is morphing too. More than arbitrarily organizing data structures and relationships, data modeling must connect with end-user requirements and questions, as well as offer guidance to help ensure the right data is being used in the right way for the right results. The ten techniques described below will help you enhance your data modeling and its value to your business.
The goal of data modeling is to help an organization function better. As a data modeler, collecting, organizing, and storing data for analysis, you can only achieve this goal by knowing what your enterprise needs. Correctly capturing those business requirements to know which data to prioritize, collect, store, transform, and make available to users is often the biggest data modeling challenge. So, we can’t say it enough: get a clear understanding of the requirements by asking people about the results they need from the data. Then start organizing your data with those ends in mind.
Staring at countless rows and columns of alphanumeric entries is unlikely to bring enlightenment. Most people are far more comfortable looking at graphical representations of data that make it quick to see any anomalies or using intuitive drag-and-drop screen interfaces to rapidly inspect and join data tables. Data visualization approaches like these help you clean your data to make it complete, consistent, and free from error and redundancy. They also help you spot different data record types that correspond to the same real-life entity (“Customer ID” and “Client Ref.” for example), to then transform them to use common fields and formats, making it easier to combine different data sources.
Data can become complex rapidly, due to factors like size, type, structure, growth rate, and query language. Keeping data models small and simple at the start makes it easier to correct any problems or wrong turns. When you are sure your initial models are accurate and meaningful you can bring in more datasets, eliminating any inconsistencies as you go. You should look for a tool that makes it easy to begin, yet can support very large data models afterward, also letting you quickly “mash-up” multiple data sources from different physical locations.
Understanding how business questions can be defined by these four elements will help you organize data in ways that make it easier to provide answers. For example, suppose your enterprise is a retail company with stores in different locations, and you want to know which stores have sold the most of a specific product over the last year. In this case, the facts would be the overall historical sales data (all sales of all products from all stores for each day over the past “N” years), the dimensions being considered are “product” and “store location”, the filter is “previous 12 months”, and order might be “top five stores in decreasing order of sales of the given product”. By organizing your data using individual tables for facts and for dimensions, you facilitate the analysis for finding the top sales performers per sales period, and for answering other business intelligence questions as well.
Computers working with huge datasets can soon run into problems of computer memory and input-output speed. However, in many cases, only small portions of the data are needed to answer business questions. Ideally, you should be able to simply check boxes on-screen to indicate which parts of datasets are to be used, letting you avoid data modeling waste and performance issues.
A key goal of data modeling is to establish one version of the truth, against which users can ask their business questions. While people may have different opinions on how an answer should be used, there should be no disagreement on the underlying data or the calculation used to get to the answer. For example, a calculation might be required to aggregate daily sales data to derive monthly figures, which can then be compared to show best or worst months. Instead of leaving everyone to reach for their calculators or their spreadsheet applications (both common causes of user error), you can avoid problems by setting up this calculation in advance as part of your data modeling and making it available in the dashboard for end users.
Each action should be checked before moving to the next step, starting with the data modeling priorities from the business requirements. For example, an attribute called the primary key must be chosen for a dataset, so that each record in the dataset can be identified uniquely by the value of primary key in that record. Suppose you chose “ProductID” as a primary key for the historical sales dataset above. You can verify that this is satisfactory by comparing a total row count for “ProductID” in the dataset with a total distinct (no duplicates) row count. If the two counts match, “ProductID” can be used to uniquely identify each record; if not, look for another primary key. The same technique can be applied to a join of two datasets to check that the relationship between them is either one-to-one or one-to-many and to avoid many-to-many relationships that lead to overly complex or unmanageable data models.
Data modeling includes guidance in the way the modeled data is used. While empowering end users to access business intelligence for themselves is a big step forwards, it is also important that they avoid jumping to wrong conclusions. For example, perhaps they see that sales of two different products appear to rise and fall together. Are sales of one product driving sales of the other one (a cause and effect relationship), or do they just happen to rise and fall together (simple correlation) because of another factor such as the economy or the weather? Confusing causation and correlation here could lead to targeting wrong or non-existent opportunities, and thus wasting business resources.
More complex data modeling may require coding or other actions to process data before analysis begins. However, if such “heavy lifting” can be done for you by a software application, this frees you from the need to learn about different programming languages and lets you spend time on other activities of value to your enterprise. A suitable software product can facilitate or automate all the different stages of data ETL (extracting, transforming, and loading). Data can be accessed visually without any coding required, different data sources can be brought together using a simple drag-and-drop interface, and data modeling can even be done automatically based on the query type.
Data models in business are never carved in stone because data sources and business priorities change continually. Therefore, you must plan on updating or changing them over time. For this, store your data models in a repository that makes them easy to access for expansion and modification, and use a data dictionary or “ready reference” with clear, up-to-date information about the purpose and format of each type of data.
Business performance in terms of profitability, productivity, efficiency, customer satisfaction, and more can benefit from data modeling that helps users quickly and easily get answers to their business questions. Key success factors for this include linking to organizational needs and objectives, using tools to speed up the steps in readying data for answers to all queries, and making priorities of simplicity and common sense. Once these conditions are met, you and your business, whether small, medium, or big, can expect your data modeling to bring you significant business value.
Part 1 in the “Advanced Integration” series
This blog is the first of a series that will drill down into technologies for advanced integration and enhanced intelligence and potential applications. We will highlight these capabilities, along with details on architectural design and evolutions in available underlying products and technology components. Here, we are exploring how an integrated platform could support a personal travel assistant.
The significant ongoing growth in global travel has triggered the need for more advanced travel-planning and execution tools to supplement existing solutions, which individually cover only certain aspects of the overall itinerary.
Today’s platform technology makes it possible to build a highly automated travel-planning and execution-monitoring solution. The technology can combine features from existing applications with intelligent microservices like satellite services, Internet of Things (IoT), machine learning, and blockchain technologies and data sources.
The result is an intelligent personal travel assistant, offering the traveler all data required in a fully automated way, in real time – from planning to execution, including routing, bookings, scheduling, checkouts, and final cost settlements. Imagine how a personal travel assistant could simplify the life of the frequent traveler, whether for business or leisure. The additional benefits are obvious in terms of administrative cost savings, and also for better alignment with various carriers and optimization of occupancies. For energy-intensive transportation providers, the result could also translate into more environmentally sustainable practices through resource optimization.
Today, organizing a trip requires a lot of human judgment and manual steps during scheduling and execution. Multiple data sources need to be consulted. Today’s travel management solutions cover only the needs of the traveler in specific areas, like route planning, booking, or expense management; they offer little integration across the various areas. For example, a route planner provides travel schedule alternatives, but limited functionality in reservation and ticketing. Likewise, booking systems have limited functionality in automatic rebooking or subsequent payment adjustments, requiring lots of manual intervention.
Access to all relevant travel-data sources in real time is a prerequisite to produce qualified and updated travel schedules throughout the travel journey. These include carrier schedules (flights, train); lodging, dining, and entertainment (hotels, restaurants, performance venues); travelers’ profiles (route preferences, loyalty programs); and access to supporting services (hotlines, insurance). This is true of existing travel management solutions.
However, using historical traveler patterns, machine learning can now identify one or more routes, as well as lodging and entertainment suggestions, and propose these through the personal travel assistant, with the corresponding time and cost implications. The traveler can then select the best suitable option and by so doing, constantly update the machine learning engine. The engine can also release bookings and reservations in due time and issue ticketing, considering time-window restrictions, penalty clauses, soft/hard bookings judgments, etc. Contracts with various providers are used as a source.
Satellite services and IoT allow the location of the traveler to be monitored throughout the journey, as well as the location of each carrier and deviations from the original schedule. The machine learning engine can anticipate potential conflicts and reschedule the trip to a best alternative route going forward, making all necessary adjustments in bookings and reservations.
Ticketing and other required verification documents can be pushed to the personal travel assistant upon confirmation of the various carriers. Payment settlement follows through various channels (such as bank transfer, credit card, blockchain) upon confirmation of carrier usage, either detected through satellite and IoT data sources or confirmed manually. Final settlement of all costs and expenses can be fully automated, sharing the relevant data with standard travel and expense applications.
The foundation of the new solution is a data platform consolidating all relevant data sources, as well as offering required security capabilities and mobile access. For example, the future state could include:
Making this a reality, however, will require meaningful change to all existing compliance and authorization barriers. The feasibility requires flexibility in changing bookings in an automated way, as well as semi-authorized payment settlements and adjustments.
However, if those barriers to entry could be overcome through policy change, the improvements in the travel experience are considerable, including:
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Posted by Barney Beal, Content Director
One might not expect Goodwill to be in the car detailing and mattress reconstruction business.
That’s because Goodwill has what may seem like a straightforward business model – accept donated items, sell them and use the funds to pay for job placement, training and other community-based programs.In fact, there is a great deal of complexity in how Goodwill centers operate and for Goodwill of Silicon Valley, with some of the highest housing costs in the country, a number of factors have combined to make dealing with that complexity vital to survival.
“One of the concerns I have with Goodwill is that commodity prices have gone down while the minimum wage has gone up,” creating lower prices for goods and higher costs, said Christopher Baker, CFO of Goodwill of Silicon Valley. “There is a tsunami coming for Goodwill. The only way to get through this is optimizing our business.”
Ridding Complexity to Stave off Catastrophe
That’s no easy task because, like many other Goodwill branches, Goodwill of Silicon Valley is a complex operation. It runs 18 different stores plus a boutique store, an ecommerce site (thriftonic.com), 20 donations sites, after market salvage, car detailing and, until recently, mattress reconstruction.
Moreover, dealing with donations adds complexity most retailers don’t need to deal with.
“Donated goods area is a different beast,” Baker said. “We have a good-better-best pricing system. In the donated goods business, we deal with 12 million items per year. Each is unique.”
Goodwill of Silicon Valley also tracks who sorted each item and how long items are kept on the shelves. Each item has a four-week rotation where it continues to get discounted and eventually pulled off the shelves and sold in bulk, which is treated differently, sold per pound. Properly tracking what’s sold to salvage can make a huge difference to the bottom line.
“It’s a complex organization in how we generate invoices and manage the separate businesses,” Baker said. “We were doing that with a lot of software. I was using an outside consultant [to help manage it]. He’s a bright guy but I was beginning to wonder who the customer was.”
Leaning on a Technology Background
Baker, who previously worked in technology, and the rest of management at Goodwill of Silicon Valley recognized that for the organization to continue to survive it needed to better manage all of its information and the existing processes. It was running Microsoft Dynamics GP for ERP and RMS for point of sale (POS).
“We had a lot of software, but I still needed a lot more from a CRM and planning perspective,” Baker said. “The cost of doing this and integrating all these platforms together didn’t make sense.”
It deployed NetSuite to manage ERP, including multiple subsidiaries, inventory management, CRM, omnichannel POS, procure-to-pay and is using the NetSuite Bronto commerce marketing tool. NetSuite was able to handle all of Goodwill’s complexity. The organization was flexible about adopting some of the best practices NetSuite has learned over its many years and was able to customize the system to its needs in others, Baker said. Notably, Goodwill of Silicon Valley created a customized bill of lading packing ticket that employees can simply hand over to drivers making a pick up, creating significant time savings in manual entries. In fact, simplifying processes for the workforce was another critical point.
“We have over 100 percent turnover,” Baker said, noting that many employees take positions at Goodwill to help get back on their feet. “We want to place people in meaningful employment going forward. It was critical to have simple tasks and limit forms.”
Rapid ROI
Already NetSuite is paying dividends. New product gross margins went from 39 percent to 47 percent, which accounts for roughly $ 100,000 to the bottom line, while gross margins on salvage grew from 50 to 54 percent. The visibility into inventory and finances has also allowed Baker to take a more hands-on approach.
“Each week, I can go and see if there’s a large variance in the store even into the item that’s missing,” he said. “I can send an email to store managers each week. They know we’re watching them now.”
The result is an organization well positioned to weather the coming tsunami.
Watch this short video to see the impact Goodwill of Silicon Valley is making in its community.
Learn about more about NetSuite for nonprofits, and stop by booth 318 at the Goodwill Summer Conference to meet the NetSuite team in person.
In the computational mechanics software (Abaqus, Ansys, Comsol, etc), Voigt notation is always used to represent a symmetric tensor by reducing its order.
Now I would ask How can we get the Voigt Notation from second order tensor or fourth order tensor in a very efficient way in Mathematica.
e.g. second order tensor: Array[Subscript[a, ## & @@ Sort[{##}]] &, {6, 6}] // MatrixForm
PS: ‘(Manual) using hand writing’ is not a good way.
Reference Links: https://en.wikipedia.org/wiki/Voigt_notation
Functioning in the U.S. economy using cash only can be a challenge. Simple daily transactions, such as making an online purchase, going out to eat, or even paying a bridge toll is a lot easier if you have a credit or debit card.
Most people know that having a higher credit score is a positive thing as it can help increase access to credit at more attractive rates. Additionally, most people also understand that not paying your bills on time, carrying a high amount of credit debt, and opening up a lot of credit in a short period of time are behaviors that will likely have a negative impact on their credit scores.
What’s less intuitive is knowing what potential actions they could take to reach a target credit score goal by a target date. For example, if someone currently has a 695 FICO® Score 8 based on Experian data and want to increase that score to 725 in 6 months so they can apply for a new credit card, how would they know if that target score was even possible or what actions could be taken to potentially achieve it?
FICO® Score Planner is a new feature built by FICO scientists that enables an individual to set a target FICO® Score 8 goal and desired time duration to reach their goal. These inputs along with an individual’s current FICO® Score 8 and credit report are analyzed by the FICO® Score Planner algorithm, which produces a set of potential actions consumers could take to help reach their target goal. Consumers can then track their progress to their goal or modify their goals along their way.
Ideal for people with an active credit profile, FICO® Score Planner helps take away some of the guess work many people face when trying to figure out potential actions they can take that may help to achieve their FICO Score goals.
Join Charles Sterling and Sam Gehret as they walk through how Mapbox and Power BI can use location data to tell your story using next generation maps.
When: 7/19/18 10AM PST
If you are not familiar with Mapbox, it is the location data platform for mobile and web applications. They provide building blocks to add location features like maps, search, and navigation into any experience you create.
Sam Gehret is a Solutions Engineer for BI and Data Viz at Mapbox. He currently manages the development and roadmap for the Mapbox custom visual for Power BI. Sam has over 7 years of Business Intelligence experience working in both product and sales at another large BI company. He holds a BA from Dartmouth College and is a graduate of the General Assembly Javascript bootcamp.
But only one book includes a scene where Obama bursts into a motorcycle gang clubhouse in Delaware, casually toting a sawed-off shotgun, to rescue Joe Biden from a mob of angry, heavily armed bikers.
“Looks like you all know who my pal is,” Biden tells his antagonists with satisfaction.
“He’s the guy who killed Bin Laden,” one of the stunned bikers says.
I’m waiting for the sequel with Jason Kander and Kamala Harris with as much optimism as I had hoped for a Hillary-Bernie unity ticket in 2016…
Office 365 is one of the most popular applications to make any business go paperless. Companies that use Microsoft O365 suite don’t have to worry about, documents or files getting misplaced. Here are three ways that any business can leverage Microsoft O365.
Flexibility and Scalability
Microsoft O365 is both flexible and scalable for any business. Microsoft O365 is available on a monthly or annual plan and can be scaled to cater to your business. It is very easy to add and remove users. Here at Websan, it can be done by filling in a form on our support page. Microsoft O365 has customized packages available so the right employees and departments access the best tools.
Moving to the Cloud
Microsoft O365 is in the cloud which means updates are done quickly by Microsoft with little to no interruptions. This will ensure that your business is using the latest versions of O365 and your business email and mailbox will never go offline. When O365 is deployed in the cloud, all the licenses will be active at once, making updating quick and secure by Microsoft.
All-in-one Business Solution
Having the right applications for each department will give your company the competitive advantage to stay ahead of the competition. The O365 suite is available on the web, desktop, and mobile devices. It makes it easier for your employees to quickly access their information, by using their Microsoft Account login.
Every business should have the Office 365 experience. No more filing cabinets or loose papers. With O365, your company can go completely digital. Microsoft O365 has multiple plans available to suit your business. Download our cheat sheet today and see what O036 is all about.
Crystal Williams, Web Marketing Assistant, WebSan Solutions Inc.,
Olay, the popular skin care brand, started using AI to make recommendations to its millions of users almost two years ago, and says it has doubled the company’s sales conversion rate overall.
It’s just the latest retail company that has turned to AI to boost its engagement with users to increase its top line. The traction confirms surveys that show an increasing number of businesses are putting AI investments at the head of their agenda.
True, Olay has an advantage over most companies. The billion-dollar brand is owned by giant Procter & Gamble, and has been using AI in its core product for some time. It has 25 years of expertise in image recognition, which helps it identify skin problems and improvement areas for its users.
In 2016, with renewed excitement growing around the potential of AI in marketing products, Olay leveraged the technology in a new marketing push, launching the Olay Skin Advisor, an online tool that gives women an accurate skin-age estimate and recommendations for care.
The product is based on a single selfie, and leverages Olay’s image expertise. Skin Advisor offers up a personalized product regimen, taking into account problem areas it sees, as well as what the user tells it they are most concerned about (wrinkles, crow’s feet, dry skin, etc.).
It incorporates an AI-powered matching engine built by Nara Logics, a Boston company that specializes in content matching and also serves the CIA, among others. Its technology decides exactly which of Olay’s 100 or so products to recommend, and in what combinations.
We talked with the CEO of Nara Logics, Jana Eggers (see video below), about how Olay doubled its conversion rate with the Skin Advisor product, which now has engaged more than four million customers. Skin Advisor also increased the average basket size, for example increasing it by 40 percent in China alone, and cut the bounce rate of visitors to a third of what it was previously. While P&G doesn’t break out Olay results in its earnings, it recently cited demand for Olay products as a reason for exceeding expected sales.
It’s one of the series of cases we’ve been writing about in the run-up to our Transform event on August 21-22, where we are showcasing real examples of companies using AI to drive their business results. Our motto for the event has become “You can do it too!,” because it’s not just the big tech companies — Google, Amazon, Facebook — that can use AI.
Here are my six take-aways from the interview:
This is just part of the story. Join us at Transform (ticket link here) where Jana Eggers sits down with Procter & Gamble’s Christi Putman, R&D Associate Director & Damon Frost, CIO-Beauty to hear more about how Olay is harnessing the power of AI.”
Thanks to all of our sponsors whose support makes Transform possible: Samsung, Worldpay, IBM, Helpshift, PullString, Yva, TiE Inflect and Alegion.
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Sonobi and LiveRamp have partnered to help marketers improve targeting. The partnership will let brands and agencies build media packages using the Sonobi JetStream multichannel ad platform and LiveRamp’s identity resolution system. “Sonobi has a forecasting and planning engine that understands the visitation patterns of an individual user across the open Web,” said CEO Michael Connolly.
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