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A Nation of Immigrants!

February 17, 2019   Humor
 A Nation of Immigrants!
© Jen Sorensen

Not to mention that these same (white) folks have committed more terrorism than any minority group. Law and order starts at home, especially if your home is espousing hatred, racism, and misogyny.

Related

 If you liked this, you might also like these related posts:
  1. A nation of immigrants
  2. How did a country of immigrants start blaming immigrants?
  3. We The Immigrants
  4. The Immigrants Have Won!
  5. What happens when you get rid of illegal immigrants?

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AI examines artery calcium deposits to assess heart disease risk

February 16, 2019   Big Data
 AI examines artery calcium deposits to assess heart disease risk

Cardiovascular disease (CVD) is the leading cause of death worldwide. About 610,000 people die of heart attacks and strokes in the U.S. every year, according to the Center for Disease Control and Prevention, and worldwide, the number stands at about 17.9 million. CVD isn’t impossible to predict, fortunately — there’s a strong risk factor in coronary artery calcium (CAC) deposits that restrict blood flow. Unfortunately, measuring CAC requires experts who can closely inspect computerized tomography (CT) scans for worsening signs and symptoms.

But there’s hope yet for a more automated approach.

A newly published paper on the preprint server Arxiv.org (“Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT“) proposes an artificially intelligent (AI) system that can evaluate and score CAC without human supervision. That’s not especially novel — automated CAC tests have been around for a while. However, the coauthors claim that their system is up to hundreds of times faster than state-of-the-art methods.

“Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT,” they explain. “[Our] method achieves robust and accurate predictions of calcium scores in real-time.”

The researchers’ AI system comprises two convolutional neural networks, a class of deep neural networks commonly applied to analyzing visual imagery. The first takes as input CT scans and aligns the fields of view, and the second performs direct regression — i.e., linear modeling of the relationship between variables — of the calcium score.

The networks were trained on two datasets: one from the University Medical Center Utrecht in the Netherlands containing 903 cardiac CT scans, of which 237 scans were used for training; and 1,687 chest CT scans from the National Lung Screening Trial (1,012 of which were used for training). In experiments conducted on an Intel-based PC with an Nvidia Titan X graphics card, the AI algorithms predicted calcium scores in less than 0.3 seconds, with a correlation coefficient (a measure of strength between two variables, in this case between predicted and manual calcium scores) of 0.98 for both cardiac and chest CT scans.

The new paper comes months after researchers at Florida State University and the University of Florida, Gainesville detailed an AI system that could predict one-year mortality in ICU patients who’d experienced a heart attack, and after Corti, an AI system which detects heart attacks during emergency phone calls, started rolling out to London, Paris, Milan, and Munich. It also follows on the heels of Zebra Medical’s successful bid to obtain FDA 510(k) clearance for its coronary calcium scoring algorithm.

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Microsoft a Leader in Gartner’s Magic Quadrant for Analytics and BI Platforms for 12 consecutive years

February 16, 2019   Self-Service BI

We’re very grateful to our customers, our community members, and our partners for making Power BI what it is today.

Thank you.

The Power BI Team

Get the 2019 Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms report* to learn more.

 

*This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

 

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Integrating MAP With CRM Can Fuel Your Pipeline

February 16, 2019   CRM News and Info

Sales teams are tasked with nurturing leads and driving them to a sale. In order to do this, they need to maintain a clear record of communication with each prospect. This is especially important as the customer journey requires several touchpoints. At the beginning of a customer’s journey, it’s easy for a salesperson to handle introductions via phone. However, as customers get deeper into the journey, strategic face-to-face meetings must take place for success.

Strategic meetings are business-to-business meetings that have the opportunity to generate real business outcomes. In order to close on large deals, strategic meetings are held face to face, and they often include the decision makers at a company. Examples include customer meetings, partner meetings, product demos, briefing center visits, interactions with press, or time with analysts. Events provide a space for these meetings to take place.

Fifty-two percent of respondents to a recent Harvard Business Review study credited event marketing with driving more business value than other marketing channels. However, only 23 percent admitted they were able to calculate an ROI for events.

With the amount of money and clear value in events, it is essential to determine an ROI. This is where a CRM comes into play. Forty-five percent of companies responding to a
recent survey said they used a CRM, but 17 percent of their salespeople found their biggest challenge to be lack of integration with other tools. Integrating your CRM with a Meeting Automation Platform (MAP) allows companies to schedule, manage, and analyze B2B meetings at events.

What Is a Meeting Automation Platform?

A meeting automation platform is software that automates the workflow associated with meetings. It helps with the pre-meeting scheduling process of invitations, agendas and reminders. It also assists the management of the meeting itself through check-in, monitoring of available and needed resources, and tracking its progress. Finally, it allows post-meeting analysis to take place. Surveys can be sent to attendees, metrics can be analyzed, records can be updated, and estimated revenue can be tracked. A meeting automation platform works best with integrations to an ecosystem of software, like a CRM.

The integration of a MAP and CRM is obviously most beneficial at an event where hundreds to thousands of meetings are taking place. Following are my thoughts on the benefits of integrating a MAP with your CRM, and how it ultimately can fuel your pipeline at events.

The Benefits of CRM Integration

The primary benefit of integrating a MAP with your CRM is the ability to schedule meetings on the go. Strategic meetings are crucial to closing any deal, so you do not want to miss out on the chance to schedule one.

This integration allows sales teams to schedule meetings directly from their CRM on the account, opportunity or leads pages.

Pre-schedule Meetings

Typically a sales team has a target number of meetings to schedule at an event in order to close the deals they need. The problem at events is a large majority of the audience in attendance does not align with the target audience of your company. This is why it is helpful for the sales team to pre-schedule meetings at an event.

More strategic meetings scheduled increases the likelihood of opportunities turning into sales. A MAP and CRM integration is a great way to automate the management of the pre-scheduling process without ever leaving the CRM.

Tracking Meetings

Integrations with CRMs, particularly Salesforce, allow meetings to be recorded in the event campaign dashboard. This is beneficial for sellers, so they can include all of the data from the mentioned pages into the meeting request process. All the necessary information is captured both internally and externally.

As a result, when any internal invites go out, the mapped opportunity/customer info is in the body of the invite and acts as a briefing report. External attendees can be added automatically as members under the respective campaigns.

Tracking external attendees who participated in meetings under a campaign helps sellers follow up with buyers to turn opportunities into closed deals. After an event, the results of all meetings can be synced up within the CRM.

Eliminate Email and Calendar Redundancies

Meetings are crucial to the sales process, yet most CRMs do not aid in meeting management on their own. Account representatives constantly face the challenge of securing and confirming meetings. On average, the confirmation process can require up to 14 emails back and forth. This is a lot of wasted time and effort going back and forth between emails, calendars and spreadsheets.

When a MAP is integrated into the CRM it automates the process for the sales team. An account representative can request a meeting without leaving the CRM, and invites will be sent automatically.

Each meeting will have a detailed agenda to ensure meeting attendees are well prepared. If the internal attendees utilize a mobile app for their CRM, they can be updated on changes in the agenda through push notifications. This automation can be a huge time saver as the sales team prepares for an upcoming event.

Access Relevant Data

Obtaining the right data can help sales teams better serve their customers. All the necessary information for the meeting is stored and accessed in the CRM.

Sellers also can gain relevant and actionable insights into where each customer is in the sales pipeline. These insights can be pushed through reports and dashboards to help sellers secure deals and marketers showcase ROI.

Prepare for Meetings

As you access relevant data, you can prepare better for each meeting. At events, you are on the go and there are numerous meetings that have different internal attendees and different customers. This creates a lot of information to track.

CRMs provide insight into who the customer is and what is important to them as well as where they are at in the process of getting a deal finalized. This allows the sales team to make the best use of the time in front of the customer.

Determine Event Value

By combining your CRM with a MAP, marketers can assign a dollar amount to each meeting that takes place. This dollar amount is tracked throughout the sales process until a deal is completed, allowing teams to determine the revenue impacted not only by each meeting but also by the event as a whole.

At each event, you can look at various metrics to determine value. For example, the average deal size helps the team keep a tab on which opportunities are more important to invest time in.

Another key metric is the time spent in meetings. An average B2B meeting could last anywhere from a half an hour to multiple hours. Time is valuable to both parties, especially at an event. Tracking the average time of each meeting, combined with the people in attendance, can help you see if you are making the best use of your time in meetings.

Additionally, tracking provides insight into the use of resources. At any given event you are investing money into meeting rooms. You might find you are wasting money by renting rooms that are not being used. The MAP will track room usage and show if this an area to cut costs for next year.

At the end of the day, integrating your CRM with a MAP further facilitates sales and marketing collaboration and reduces the time it takes to close a deal. There are numerous benefits to this integration. Whether it is scheduling meetings directly from your CRM, a seamless data transfer from the MAP to the CRM, or measuring the impacted revenue.

Automating the workflow associated with meetings can give sales and marketing teams valuable time to focus on other efforts, such as developing personalized content for the sales process. When less time is wasted for your team and all attendees are provided with the necessary information, more quality strategic meetings can occur.
end enn Integrating MAP With CRM Can Fuel Your Pipeline


Ravi%20Chalaka Integrating MAP With CRM Can Fuel Your Pipeline
Ravi Chalaka is CMO at
Jifflenow. Chalaka is a marketing and business development expert who creates and executes business strategies, generating demand and raising brand/product awareness in competitive markets. As VP of marketing at both large and small technology companies, Chalaka has built strong teams and brands, and enabled faster revenue growth for a wide range of solutions based on big data, SaaS, AI and IoT software, HCI, SAN and NAS. Ravi has MBA degrees in marketing and finance and is an expert industry spokesperson and presenter.

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6 Ways Machine Learning Is Revolutionizing Manufacturing in 2019

February 16, 2019   BI News and Info
assembly line 1200 6 Ways Machine Learning Is Revolutionizing Manufacturing in 2019

From the first harnessing of economies of scale to the introduction of the assembly line, the search for new efficiencies has always been at the heart of manufacturing. Today, the greatest new gains come from the innovative combination of hardware and software. In particular, robotics has revolutionized manufacturing, allowing for greater output from fewer workers.

But while robotics has been making an impact for decades now, machine learning is just beginning to live up to its full potential. A 2017 survey by PWC found that only around half of all companies were already using it. Yet when implemented, machine learning can have a massive impact on companies’ bottom lines.

Ultimately, the biggest shift has been from a world where the business impact of machine learning has been largely theoretical to one where it is now quite real. The proven impact of machine learning models has pushed more investment toward their development. Still there are plenty more gains to be realized. To better understand the potential (and how you can harness it for your business), here are 6 ways machine learning is revolutionizing manufacturing.

1. Machine Learning is Revamping Quality Control

You’ve likely seen plenty of clips showing workers sifting through products whizzing by on an assembly line, looking for flaws. This is overwhelming and exhausting work, and you probably wonder how anyone can maintain the focus necessary to find small flaws for hours at a time. In a real world example, quality control was crippling GM throughout the 1970s. This led them to take the Toyota Manufacturing Technique and implement it in many of their factories.

Even when advanced manufacturing techniques are implemented, using humans to spot defects and errors is inherently limiting. Our senses and attention span simply have natural limits far below what machine sensors can offer. What does that difference add up to?

Forbes found that machine learning increased defect detection rates by up to 90%.

Quality control is often done by humans because it’s usually visual. If weight or shape is the main quality factor, it’s far easier for a machine task. Scanning for misaligned labels, off-colors,  shine levels, and even cracks is fairly simple for a human but very difficult for a machine. Machine learning, however, allows algorithms to visually inspect products and identify flaws more quickly.

A case study involving steel manufacturing uncovered the impact that machine learning might have when defects are identified earlier in the process, leading to less waste. Factories are also able to efficiently identify possible causes of these defects. Besides the products themselves, machine learning can even improve the machines that make the products.

2. Machine Learning can Minimize Equipment Failures

Determining when to conduct maintenance on equipment is an exceptionally difficult task with huge stakes. Each time a machine is taken out for maintenance, it’s not doing its job and may even require factory downtime until it is repaired. Frequent fixes mean losses, and infrequent maintenance can lead to even more costly breakdowns. Global costs of equipment downtime adds up to $ 647 billion dollars annually. Looked at another way: The average international cost of said downtime is $ 5,600 per minute.

With those costs in mind, it’s no surprise that preventing even a single unplanned outage can pay for the cost of implementing machine learning. How does machine learning minimize these issues, exactly?

Machine learning algorithms are excellent at balancing multiple sources of data to predict and determine optimal repair time. This can be done simply by identifying errors and defects as they occur so they are addressed immediately – not once a human has discovered them at a later time. In addition, machine learning algorithms utilize historical data to identify patterns of equipment failure, helping them determine when regular maintenance should occur.

Data can also be taken automatically from inside the equipment, eliminating the need for a manual check. Increased speed and efficiency – plus decreased manpower costs – translate into substantial ROI for most firms, but the biggest gains come from a change in how maintenance is conducted.

3. Predictive Maintenance

This data boils down to a shift from reactive to proactive repair work. Generally, maintenance is conducted once a problem occurs, due to the high cost of taking equipment offline to have it manually checked for potential problems. When this occurs, managers constantly face an impossible choice: Take equipment offline and incur a loss now, or risk even greater losses down the line.

The role of machine learning is to identify the ideal moment to make that choice, and remove the costly and stressful guesswork. By using machine learning to predict when equipment breakdowns are likely to occur, your company can be far more proactive and ensure they are serviced before that happens. This results in fewer errors, less downtime, and lower human-capital costs because managers and other workers need to be less involved.

What do those benefits add up to? A recent study by Deloitte found that poor maintenance can decrease production by 5-20%. It’s clear that putting machine learning at the core of equipment care is essential to avoid costly inefficiencies.

The Advantages of Machine Learning Go Beyond the Factory

Even after machine learning has helped with quality control and machine maintenance, the resulting product still has a long way to go. For storing and shipping, machine learning has a role to play in identifying inefficiencies, and here’s how:

4. Supply Chain Optimization

Whether you’re looking at replacement parts for your factory equipment – or the products that equipment produces – reliable supply chains are essential for any manufacturing business. As the global economy becomes more complex, so does the challenge of optimizing these supply chains.

A single shift in weather, damaged ships, or change in fuel prices can reverberate throughout your supply chains, greatly impacting your business. Remember that the average time of equipment that’s down is $ 5,600 per minute. This cost applies just as much if you’re waiting for raw materials as if the equipment is broken.

Machine learning takes all of these complex factors into account and optimizes each element of your supply chain in response. This could mean calculating how much extra time to give a shipment (to account for the probability of a delay and/or its financial impact), or deciding where to ship a product from, based on possible weather patterns or other potential hurdles.

Put simply, a machine learning algorithm can take dozens – or even hundreds – of factors into consideration before making the best possible choice for your business.

The importance of minimizing these delays comes down to inventory and cash flow. If, for example, you can increase the efficiency of your supply chain by 10%, that means you can produce 10% more product while decreasing the level of unpredictability in the production process. Efficient and reliable production are essential for a successful manufacturing business, and machine learning makes both accessible in a way it never has been before.

5. Inventory Optimization

Closely connected with supply chain optimization, machine learning can have a similar impact on optimizing inventory. Holding costs (the cost of storing inventory) are massive, usually hovering around 20-30% of the cost of a product. Even a modest reduction of 10% in holding costs can reduce your per-unit costs by 2-3%. Holding unsold or undelivered products means paying for storage space. This may not sound like a major problem, but its effect on cash flow is immense.

Here, the role of machine learning is to calculate when it makes economic sense to hold on to or sell inventory, or even increase/reduce production of inventory. This is done by monitoring the supply chain elements mentioned above, as well as market prices, holding costs, and production capacity.

Carefully considering and balancing all of these elements has traditionally been a human’s job. With the ever-increasing amount of data reflected in each of these areas, however, humans are a poor choice for the task.

Therefore, the role of machine learning is an obvious one. By analyzing thousands or even millions of bits of information to make decisions, these algorithms go far beyond anything a human analyst is capable of. No surprise then that the results on overall efficiency can be substantial.

Factory-Wide Efficiency Gains from Machine Learning

There are also applications for machine learning that fall further outside of the areas already mentioned. Factories have more inputs than raw materials for production or information for analysis: Factories also run on commodities like electricity.

6. Using Machine Learning for Electricity Consumption

Obviously, one of the greatest inputs for any factory is electricity. While most factories operate 24 hours a day for optimal efficiency, it’s possible to schedule more energy-intensive activities for different times. The idea is to ensure those activities occur when power is cheapest. Depending on its source, this could be during the day (if solar power is prominent) or during the night (when demand is generally lower).

Of course, it’s not quite that simple. You obviously need to take a myriad of other factors into consideration. Once again, this is where machine learning’s ability to process large amounts of data comes into play. By considering energy prices alongside labor costs, equipment maintenance, and minimizing inventory, these algorithms can schedule the perfect time to perform energy-intensive activities for maximum cost savings.

Working backwards, this information can also allow you to intelligently invest in electrical infrastructure, whether that’s energy storage or solar power. Essentially, machine learning algorithms can allow you to precisely quantify the value of your factory’s electricity at any particular moment.

You can more precisely determine where such investments make sense, use your resources more strategically, and get more out of your factories.

Bringing Greater Efficiency to Every Area of Manufacturing

It’s not surprising that machine learning continues to impact manufacturing, but it might be more shocking that does so at nearly every stage. Machine learning can offer substantial cost savings in many areas, from buying raw materials to maintaining equipment. The flexibility of this technology explains its rise in popularity as it has become far more user friendly and less reliant on hiring teams of data scientists.

What’s stopping you from using machine learning today? RapidMiner makes data science more accessible than ever. Year after year, more case studies and research reports get published, providing concrete evidence for its benefits. The challenge is simply understanding how to best apply it to your business. We have lots of experience working with manufacturing organizations, but each company offers its own unique set of complexities and challenges so contact us about your use case and get started today.

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Friday Dance Party!

February 16, 2019   Humor

Posted by Krisgo

 Friday Dance Party!

via

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About Krisgo

I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon icon smile Friday Dance Party!


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Manage your KPIs with Dynamics 365 Goals

February 16, 2019   Microsoft Dynamics CRM

Goal management in Dynamics 365 is a tool which provides the ability to manage KPIs in just a few clicks. You can use goals to compare targets and actuals for a defined period. And given the fact that Goals is an out of the box feature with Dynamics 365, you can set them up in no time at all. Additionally, since Goals are native to the system, they work seamlessly with workflows, business rules and other various Dynamics 365 functions.

Goals Pic 1 Manage your KPIs with Dynamics 365 Goals

To help you understand how goals work, consider the following scenario:

Your company sells drywall, you, as the sales manager, want to set a revenue target for the overall sales team and then targets for each individual sales team rep who will contribute to the sales team target. With the given targets you want the ability to monitor progress throughout the fiscal period.

To get the results you desire you will need to utilize a couple of entities in Dynamics 365 and create a handful of records:

1. Goal Metric: this allows you to set the detailed measurement (amount or count) for the defined goals. You can create multiple goal metric records to measure different elements, revenue and number of panels sold for example.

Goals Pic 2 Manage your KPIs with Dynamics 365 Goals

2. Rollup Fields: the goal metric includes a section where you identify the rollup fields which will be used to track against the metric. You will define rollup fields which will be used to measure both the in progress and actual values.

Goals Pic 3 Manage your KPIs with Dynamics 365 Goals

3. Goal: the entity where you will define who is responsible for the goal, the metric being measured and the target. In the scenario where you are setting an overall sales team goal and then individual goals, you will create multiple goal records (1 for the overall cumulative goal, and then 1 for each sales team member).

Goals Pic 4 Manage your KPIs with Dynamics 365 Goals

All sales records identified will be rolled up against each sales agent’s goal, which is the child goals. Then the child goals will be rolled up to the parent goal, which is the overall sales team goal. You can then set up a dashboard with data components which display goal metrics, allowing you to sit back and monitor results as they start rolling in.

If you need assistance or have any questions while setting up goals, please reach out to our support team at [email protected]. We are always happy to help you increase the productivity of your Dynamics 365 environment!

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Documents show how Google used shell companies to keep datacenter negotiations quiet

February 16, 2019   Big Data
 Documents show how Google used shell companies to keep datacenter negotiations quiet

The negotiations between tech companies and cities to open new offices and/or data centers have long been held behind the scenes. But that secrecy is proving to be an increasing point of contention, as evidenced this week by Amazon’s decision to abandon plans for a big New York City campus, in part due to backlash over how tight-lipped the ecommerce giant was in negotiating an incentive package with the city and state of New York.

Now, new documents obtained by the Washington Post show that Amazon’s not the only company that favors secrecy — Google has also used confidentiality agreements in its bid to secure land for datacenters, even going so far as to create shell companies for the purposes of negotiation. The documents were first acquired by a group called Partnership for Working Families, which is suing San Jose, California over non-disclosure agreements the city has signed with Google.

The report comes after Google announced this week that it planned to invest $ 13 billion in expanding and opening new datacenters and offices across the U.S.

According to the Post, Google used shell companies in negotiations with at least five cities that it ended up building datacenters in. Sometimes, it used multiple shell companies, and negotiated with local officials using code names to avoid revealing it was Google behind the project until months into negotiations. Here’s how they did it, according to the Post, as discovered through documents and an interview with Larry Barnett, president of an economic development organization in Midlothian, Texas, where Google ended up building a datacenter.

In Midlothian, for example, Google created Sharka to negotiate the tax-abatement and the site plans, and used a separate Delaware company, Jet Stream LLC, to negotiate the land purchase with a private owner. In Iowa, Google created Delaware-based Questa LLC for the land sale and Gable Corp. for the development deal.

When Google’s representatives first approached Midlothian in 2016, they used a code name that was not the same as either of the subsidiaries, Barnett said. (He declined to say what it was.) Google also asked Midlothian officials to sign a confidentiality agreement before they knew the developer’s identity, Barnett said. He said Google revealed its identity a year later, as the deal approached.

The story also includes links to non-disclosure agreements Google made officials sign in a number of cities, including Boulder, Colorado, San Jose, and Clarksville, Tennessee.

Google spokeswoman Katherine Williams defended the company’s actions in a statement to the Post, saying that Google employs “common industry practices.”

“We believe public dialogue is vital to the process of building new sites and offices, so we actively engage with community members and elected officials in the places we call home. In a single year, our data centers created $ 1.3 billion in economic activity, $ 750 million in labor income, and 11,000 jobs throughout the United States,” Williams’ statement read.


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Other tech companies, including Apple, Facebook, Microsoft, and Amazon, often require secrecy for at least some duration of the process when negotiating land deals for new space, including for datacenters. Because of that, it’s difficult to say how much more or less secretive Google is compared to other companies. Apple and Microsoft, example, have also used codenames in the past when negotiating with local officials in municipalities they intend to build datacenters in. In 2016, Facebook used a shell company called Greater Kudu LLC in negotiations for a datacenter in New Mexico. In that case, the company behind the development also wasn’t revealed to city council officials until late in the approval process.

But what’s passed for common industry practices in the past may not continue to, especially as local groups have gotten more vocal about protesting tech company developments, like Amazon’s proposed campus in New York City. There, city council meetings that Amazon official attended were frequently protested by pro-union groups.

There is another instance where Google appears to differ from competitors: in some municipalities where it’s built datacenters, Google has declared information about how much energy and water the datacenters use as a trade secret. That’s irked an environmental advocacy group in South Carolina, which has been trying to get information on how many gallons of water a Google datacenter in Berkeley County has been using.

They do know, however, that the Google-created entity that owns the datacenter is among the 10 companies that use the most water in the county. The annual water usage of at least some Facebook and Apple datacenters have been revealed in the past, based on a quick search of local news articles.

VentureBeat has reached out to Google for additional comment, and will update this story if we hear back.

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Incremental refresh & query folding

February 16, 2019   Self-Service BI

As part of our strategy to converge enterprise and self-service BI on Power BI as a single platform, we announced the public preview of incremental refresh last summer. While there is still more work to be done to get it to general availability, we have seen strong uptake of incremental refresh.

Incremental refresh is a great example of how Power BI is modernizing/simplifying complex BI implementations. It enables incremental loading of new or changed data to a BI model without needing to reload the full set of data. This can have the following benefits:

  • Refreshes are faster since you don’t need to load all the data every time and more can be done in parallel. This allows for new data to be added quickly.
  • Less memory may be used during processing (again because you are only loading part of the data).
  • Models can grow to very large sizes.
  • Old data can be dropped when not needed.
  • Existing data can be updated without updating the entire model.
  • Refreshes may be more reliable.

The Power BI service partitions data based on date range. This is what enables only certain partitions to be refreshed incrementally. To make this work, the partition filter conditions are pushed down to the source system by including them in the queries. Using Power Query terminology, this is called “query folding”. It is not recommended that incremental refresh is used when the required query folding cannot take place. For more detailed information on query folding, please see the incremental refresh docs article.

Given the various levels of query-folding support for each data source, it is recommended that verification is performed to ensure the filter logic is included in the source queries. To make this easier, the February update of Power BI Desktop will attempt to perform this verification for you. SQL based data sources such as SQL, Oracle and Teradata should be able to rely on this warning. Other data sources may be unable to verify without tracing queries. If Power BI Desktop is unable to confirm, the warning is displayed.

Query folding Incremental refresh & query folding

Note: if the PBIX file was authored in a version of Power BI Desktop prior to the February release, the warning will be displayed regardless until a data refresh is performed.

Here is a video you might find useful that discusses query folding for incremental refresh.

We look forward to getting back to complete the work to get incremental refresh to general availability, so watch this space!

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When Sales get Serious: As B2C Sales Surge, elope Plans Thoughtful Growth Strategy

February 16, 2019   NetSuite

Posted by Barney Beal, Content Director

Here’s a quick dare. Head to www.elope.com. Search for any of this combination of the company’s custom-designed hats: the light up anglerfish jawesome hat, the racing reindeer plush helmet or the delightfully simple (and best-selling) pig kit.

Now, try to not smile.

Making sure no one could win that dare and that “Everybody’s Laughing on Planet Earth” (which is what “elope” stands for) was the mission of Kevin and Keith Johnson when the brothers launched the company as a kiosk in a Colorado Springs mall decades ago.

The brothers created elope “to share their love of laughter and style with the world,” according to the company’s website. And “more than 25 years later, that same joy of adventure, desire to make people happy and passion for creative costuming inspires every decision we make.”

It’s not too surprising then, that the product line has grown to 1,000 hats, costumes and accessories — half of which are original elope products and half of which include licensed material of the likes of Harry Potter, Dr. Seuss, and dozens and dozens of universally known franchises. The company has also grown to 45 employees at its Colorado Springs headquarters, and continues to expand its contract manufacturer in China.

When it comes to its business, a funny thing about elope is that, despite its B2C beginnings, for much of the first part of its story, it was a B2B shop. It sold products to businesses and retail partners like Walmart and Target, and did quite well. But like so many manufacturers and wholesale distributors, with the advent of ecommerce and its entrance onto Amazon some three years ago, that business model came full circle. Now, a full 50 percent of its sales come direct from consumers, with a strategy focused on growing that segment.

No Laughing Matter

Delivering “fantastical service,” is a key company mantra. But while it may have appeared fantastical to customers, executing that first year on Amazon required something akin to whooshing one of those Harry Potter wands to wield magic on the back end.

That was the year Eli Rustenbach joined the company as the Strategic Analysis Team Lead. She remembers those orders pouring in from Amazon leading up to that first Halloween, the busiest time of the year for the company. With a 12-year-old Sage system that Rustenbach describes as being “manipulated within an inch of its life to do what we needed it to do,” the company got everything out correctly and on time, but it was a Herculean (or perhaps Gryffindor-ean) feat.

“We were manually uploading thousands of orders, minute by minute from Amazon,” she said. “It was a mess.”Elope%20Party 052 When Sales get Serious: As B2C Sales Surge, elope Plans Thoughtful Growth Strategy

After that Halloween, the company knew it needed to change its ERP system. Aside from the inflexibility of the Sage system, there wasn’t a strong partner community building connectors and tools to extend it.

“When Amazon came along, Sage was by no means ready, and they’re not going to be ready for a while,” she said.

SuiteSuccess a perfect fit

Driving process improvements with technology would enable elope to focus on more strategic activities, like conducting keyword research to boost Amazon sales and finding ways to improve its customer experience on its own ecommerce website. At the same time, the company was eying expansions to eBay and Walmart.com.

Attracted to its fixed rate implementation and the opportunity to implement best practices from industry leaders, as well as configure the system to its own needs, elope implemented NetSuite using SuiteSuccess.

“Because we had been with Sage for so long, we lost ourselves in our own band aids, and didn’t know what was needed anymore,” she said. “We were excited to have different ways of doing things from NetSuite’s industry experience.”

Virtual warehouses, real growth

With a single source of data, elope leverages virtual warehouses and real-time stock information for demand planning across 1,000 SKUs for B2B and B2C sales, and to optimize fulfillment from its warehouses in Colorado Springs. With the ability to easily expand its business to eBay and Walmart.com, the company has already paid for the NetSuite implementation.

B2C sales are up 40 percent year over year, while automation, prebuilt reports, and real-time inventory data access enable elope to analyze and plan growth strategy. The company is now eyeing international expansion in Europe.

Reflecting on the Amazon debut, Rustenbach mused about the possible next “game changer,” of course not knowing what that may be. But she does know that this time, elope won’t be coming at it with a system that is old hat.

“I don’t know what that next thing is. But I know they’re going to build a connector for NetSuite,” she said.

For more, get leading practices for wholesale distributors.

Posted on Fri, February 15, 2019
by Barney Beal

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