Tag Archives: Changing

Portals: Changing the Date from the Default Format

pORTALS 1 300x225 Portals: Changing the Date from the Default Format

In Microsoft Dynamics 365, the date for Portals is in a USA format by default. In this blog we’ll show you a simple way to change the date to a UK format without using JavaScript.

1. Navigate to Portals in the dashboard

2. Click on Site Settings

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3. Browse through the Site Setting records to check if you can find a record with “DateTime/DateFormat” in the Name field

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If Record found:

4. Open the record and edit the Value field to the desired date format string
i.e. dd/MM/yyyy

5. Click Save

If no Record found:

4. Click +New to create a new recordUnder General tab enter:
Name: DateTime/DateFormat
Website: Your Portal Name
Value: The desired date format string i.e. dd/MM/yyyy
Description: Custom date format string

5. Click Save

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6. Refresh your portal page with a date field to make sure your change has been applied

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There you go, a simplistic approach to changing the default date format for portals in Dynamics 365! Keeping checking our blog for more helpful Dynamics 365 tips.

Happy Dynamics 365’ing!

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PowerObjects- Bringing Focus to Dynamics CRM

ICYMI: The Changing Landscape of Capacity Management for the Mainframe Webcast

In case you missed our recent live webcast, The Changing Landscape of Capacity Management for the Mainframe, it’s now available on-demand.

During the webcast our expert Nick Varley reviews strengths and weakness of traditional mainframe Capacity Management and why organizations are reviewing their current capacity management strategy and tools.

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He also discusses how changes in scope and processes require a philosophy change amongst capacity management professionals, the incorporation of Business and Service views and planning for new technologies.

blog CMwebcast slide2 ICYMI: The Changing Landscape of Capacity Management for the Mainframe Webcast

Discover how organizations have begun to review, evolve, and mature the CM process to keep up with the demands of today in a world where the mainframe continues to be an integral cog in an enterprise’s IT environment. Watch the webcast now!

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Syncsort + Trillium Software Blog

changing line style from solid to dashed after an intersection

I used this link: Plot that draws a dashed/solid curve depending on the y-value of the curve to help me start this. I have two lines. I want to change from solid to dashed after an intersection. The line with higher slope will have dashed line AFTER the intersection and the line with lower slope will have dashed line BEFORE the intersection. These two lines should be red and blue.

Here is what I have done so far…

In[289]:= y1 = 1.44; y2 = 27.9 - 16000 x;
intercept = x /. Solve[y1 == y2, x][[1]]

Out[290]= 0.00165375

The plot…

Plot[{y1, y2}, {x, .0014, .0019}, PlotRange -> All, 
 MeshFunctions -> {#1 &}, 
 Mesh -> {{0.0014, intercept}, {intercept, 0.0019}}, 
 MeshShading -> {Blue, Directive[Blue, Dashed]}, MeshStyle -> None]

Here was the result…

7Pl5h changing line style from solid to dashed after an intersection

Not sure how to fix this. Please help me.

3 Answers

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Recent Questions – Mathematica Stack Exchange

How To Keep Up With China’s Rapidly Changing Automotive Industry

282010 GettyImages 158259775 super e1504034958793 How To Keep Up With China’s Rapidly Changing Automotive Industry

Also, while WhatsApp has a large following, China’s 768 million WeChat users have access to far more advanced features, such as electronic payments and online gaming. It seems as if WeChat payments are ubiquitous in China and will soon replace both cash and credit card payments.

The bottom line

In a globally connected world, automotive companies are reinventing themselves. China is a great example of jumping technology stages in an effort to become the leader in electric mobility.

Technology companies recognize the gravitas of China becoming a leader in the automotive world. Recognizing also means listening to the specifics of the market, the tech infrastructure ecosystem, and consumer preferences, and providing breakthrough cloud-based technologies that enable fast-moving players to stay ahead of the game.

This story also appeared on the SAP Community. Follow me @ulimuench.

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Digitalist Magazine

Changing B2B Marketplace Calls for New Sales Skills

There are three key moments when salespeople can maximize the value in a customer relationship instead of allowing it to leak out, according to
Corporate Visions, which announced a new sales skills program on Thursday.

Those three turning points occur during deal negotiations, when securing customer renewals, and introducing strategic price increases, the company said.

“Traditional negotiations training programs have focused heavily on deal negotiations,” said Tim Riesterer, chief strategy and research officer at Corporate Visions, “so they’ve covered the first of the three areas.”

Sales Process Evolution

Certain egregious sales behaviors — including indiscriminate discounting to secure a deal — have led both companies that bought training and those that offered it to believe focusing on negotiations was sufficient, “with documented payback in reduced discounting and increased deal profitability,” Riesterer told CRM Buyer.

With the sales, purchase and usage process evolving into more of a Products as a Service experience, he said, “the pressures and the opportunity have shifted to the other two areas — renewals and price increases.”

Training vendors by and large have not updated their intellectual property or programs to reflect this trend, and enterprises “have been slow to realize the need and potential crated by these situations,” Riesterer pointed out.

“Neither have done the research to realize the psychology of those moments requires new skills and competencies to be effective,” he maintained.

Many of the training programs available in the market focus on general sales strategies or negotiation techniques, noted Cindy Zhou, principal analyst at Constellation Research.

Corporate Visions’ Course

For deal negotiations, Corporate Visions’ Capture Value skills program will teach salespeople how to accomplish the following:

  • Creatively manage negotiations from a low-power position to create pricing uncertainty in commoditized markets;
  • Expand deal size by introducing unconsidered needs and capabilities;
  • Drive agreement and consensus in multibuyer decisions; and
  • Avoid unnecessary discounts.

“My research shows that the average number of decision makers involved in a B2B sale has grown from five people to seven over the past year,” Constellation’s Zhou told CRM Buyer. The ability to navigate multiple decision makers and build consensus among them “is a necessary skill set for modern B2B sellers.”

Renewals have become an important part of growth, as more companies sell multiyear agreements, managed services, and other recurring revenue products and services, especially because the first years of the initial agreement usually are the least profitable, Corporate Visions’ Riesterer remarked.

Selling renewals requires “additional skills to reinforce their status quo bias; demonstrate progress, results and business impact; as well as to position your new advances and capabilities,” Riesterer said.

Increasing pricing without jeopardizing the customer relationship “calls on yet additional sales conversation skills in positioning, presenting and securing the desired price increase,” he added.

The Corporate Visions course costs US$ 2,000 per person, with discounts for larger groups. It is offered in three formats.

Automation and the Sale

The typical B2B sales cycle can “be upwards of a year, depending on the size of the purchasing organization,” Constellation’s Zhou noted, and B2B sales are most complex at the enterprise level.

That complexity could be why B2B sales increasingly are being automated and going online — and possibly one reason Amazon Business Marketplace surpassed the 1 million customer mark wtihin 15 months of its launch in April 2015.

Startup firm
Qurious offers an eponynously named artificial intelligence sales platform that shows salespeople real-time battlecards in response to customers’ questions and objections during a sales call.

When the Qurious platform detects a trigger during a phone call, such as a buying signal or objection from the customer, it displays a contextually relevant battlecard to help guide the conversation.

Each battlecard is tracked and linked to outcomes, so salespeople can see what’s working and conduct A/B tests on different battlecards. Qurious also offers best practice templates.

Corporate Visions’ training program would be more for “complex, enterprise B2B field salespeople rather than inside sales reps who are more vulnerable to automation,” Constellation’s Zhou observed.

However, the course’s principles “can be equally applied to inside sellers or account managers,” Riesterer maintained, as well as “customer service or success managers.”
end enn Changing B2B Marketplace Calls for New Sales Skills

Richard%20Adhikari Changing B2B Marketplace Calls for New Sales SkillsRichard Adhikari has been an ECT News Network reporter since 2008. His areas of focus include cybersecurity, mobile technologies, CRM, databases, software development, mainframe and mid-range computing, and application development. He has written and edited for numerous publications, including Information Week and Computerworld. He is the author of two books on client/server technology.
Email Richard.

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CRM Buyer

How Digital Disruptors Are Changing The Automotive Industry

It could be argued that the automotive is currently facing the biggest period of change since Henry Ford created the production line. All major automotive companies, as well as many technology companies, are piling investments into autonomous driving.

While fully driverless cars are still a little way off (2020 seems to be the year when we will start to see them become mainstream), some major digital disruptors are changing the way we buy new cars right now.

For many years, dealerships have mainly been franchises affiliated with one or two brands, located out of town, often on an industrial estate filled with many other car dealerships. As a prospective car buyer, we would visit these dealerships, maybe more than once, book a test drive with a salesperson, spend a lot of time discussing options, haggle, organize finances, negotiate on a trade-in value, and finally, make a purchase.

This is a time-consuming and often daunting process, especially since we are generally restricted to a specific geographical location. Like many of us, you may have stood in a dealership on a Saturday morning waiting for a dealer to become free and give you some attention. You are likely to have your eye on only one or two brands and will visit those specific dealerships rather than browsing through all.

Two particular companies are changing this model drastically and have the potential to completely change the way we buy new cars.


Carwow is based on a simple but very effective idea: As a prospective new car buyer, you visit the Carwow website and input details of your desired car. The website then emails your requirements to dealers that have registered with them. The dealers respond with an offer of a price for that car, while the site displays only the first five offers so that shoppers aren’t overwhelmed with options.

Through the site customers can communicate with the dealers, view their ratings and reviews, and even negotiate with the dealer further to get a lower price. The offers cover all purchasing options, including cash and finance, and most of the dealers deliver your car to you for free.

 How Digital Disruptors Are Changing The Automotive Industry

The very simple Carwow concept offers a radical way to purchase a new car. Why be restricted by geographical location when most of the dealers will deliver for free? Does it really matter where your car is coming from? Why play the negotiating game with one or two dealers when you can let a large number of dealers compete with one another for your business? Ultimately, you are likely to pay less for a new car when buying through Carwow than if you buy from a dealer in person, and it can involve a lot less hassle.

This model has the potential to completely change the way a dealership operates. You may be surprised to hear that a dealer’s typical gross profit on a new car sale is around 10%. When you then factor in the high overheads of having a physical dealership, the net profit is reduced to around 1-2%. You can now imagine a scenario where a dealer may be entirely digital and not actually have a physical presence. This would allow them to significantly lower their overheads and be even more competitive on price.

As you can imagine, this has not gone down all that well with some automotive brands, as it can eat into margins. In 2016 Carwow reported BMW to the Competition Markets Authority (CMA), claiming that BMW was preventing dealers from selling through Carwow. After an investigation by the CMA, BMW made a u-turn and allowed their dealers to participate.

Today’s consumers are much more tech-savvy and are likely to have significantly researched their car purchase online before visiting any dealer. In recent years, most car manufacturers have simplified their offerings where most options are bundled into packages rather than requiring the purchaser to choose from a long list of extras. Car brands have been investing in their websites, creating slick user experiences and usable configuration tools.

All of this has meant that the consumer’s reliance on face-to-face contact with a physical dealer has become less and less, which makes a service like Carwow more viable than ever before. We are all used to self-service on the web, whether it is managing our own finances to booking a flight or a holiday. Why can’t it be the same for a car?

Of course, you will still want to test-drive the car. I doubt many of us would buy a new car without test-driving it, and this is where we are constrained by a geographical location and are likely to go to a local dealer. However, if you use Carwow, you are less likely to actually buy it from them. Maybe in the future, dealerships will primarily serve as test-drive and service centers rather than focusing on actually selling new cars. After all, current dealers make most of their money on extras, financing, and servicing.

It will be interesting to watch what impact Carwow and any other imitators will have on the automotive industry in the next few years.


Rockar is an omnichannel car dealer based online and currently within stores in two shopping centers in the UK: Bluewater and Westfield Stratford City. Yes, you read that correctly: shopping centers. They originally partnered with Hyundai, but recently partnered with Jaguar Land Rover, with the new Rockar Jaguar store opening in October 2016. Rockar aims to completely change the way we shop for and purchase new cars. Rockar was set up by Simon Dixon, a veteran of the car industry, a few years ago. Rockar’s aim is to build a business model that is entirely focused on the customer buying experience, rather than the traditional car sales model and brings the buying experience into the digital age.

 How Digital Disruptors Are Changing The Automotive Industry

The Hyundai Rockar website is more akin to a car hire website or traditional e-commerce site than a traditional car dealer’s site. As well as finding the right car, users can book a test drive or book their service through the website. Buyers can buy outright or arrange finance all online in simple and easy steps.

Since Rockar was created as an omnichannel business right from the start, the website experience is carried through into the physical stores. In a Rockar store, you will find a small number of models and plenty of touch-screen kiosks, which effectively run a version of the website. They have an office base in the shopping centre car park, where you can pick up your car for a test drive. The store does not have the usual salespeople you would expect to see in a traditional car dealership. Instead, they have people they call Rockar Angels. The aim of these staff is not to sell or make a deal, but to advise the customers on the cars.

 How Digital Disruptors Are Changing The Automotive Industry

This model works especially well for a brand like Hyundai. It is fair to say that South Korean brands such as Hyundai or Kia have not had the best reputation for quality or luxury in the past. Over the last few years, both of these brands have significantly improved their quality, especially inside the cabin, to the extent that they are certainly rivaling Japanese brands, and even knocking on the doors of German brands.

However, the perception of lower quality persists, and many of us might not consider buying from one of these brands. Therefore, in the traditional model, we would not be likely to visit one of their dealerships. This is where Rockar changes things. By placing an inviting looking showroom in a shopping center like Bluewater or Westfield, they draw in customers who would not otherwise visit an out-of-town Hyundai dealership.

Once this customer is drawn in, they realize that these cars are actually really quite nice. The quality and finish is nothing like they imagined. The staff are very inclusive and welcoming, and the buying experience is pleasant. This is known as a conquest sale, and it is making other car brands very nervous. They are losing sales to brands like Hyundai, and I suspect we will soon see more automotive brands in shopping centers very soon.

The future

Over the next few years, I expect to see other major disruptors enter the UK automotive market. Once fully autonomous cars become mainstream, we are likely to see a major shift in many consumer’s perception of cars. Imagine being able to hail an autonomous car whenever you want to take you wherever you want to go. Why would you own a car yourself? Would you really care about the brand of car?

Maybe we will see Google, Samsung, or Apple begin to dominate the market. During your journey, your car would be gathering data on traffic conditions, air quality, and weather. This data could be be valuable and be sold to subsidize the cost of your journey. You could even be sold premium services on your journey such as movies, news, food, or drinks, which will further subsidize the journey to the extent that it is actually free. The decade ahead is going to be a very interesting and exciting one for the automotive industry.

For more on technology and the automotive industry, see How Social Media Has Changed The Automobile Industry.


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Digitalist Magazine

How Machine Learning Is Changing Recruiting

Dan McCaffrey has an ambitious goal: solving the world’s looming food shortage.

As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting.

“By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things,” McCaffrey explains. “That’s what excites me.”

“The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer,” McCaffrey adds.

But to deliver that insight, Climate needs data—and lots of it. That means using remote sensing and other techniques to map every field in the United States and then combining that information with climate data, soil observations, and weather data. Climate’s analysts can then produce a massive data store that they can query for insights.

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Meanwhile, precision tractors stream data into Climate’s digital agriculture platform, which farmers can then access from iPads through easy data flow and visualizations. They gain insights that help them optimize their seeding rates, soil health, and fertility applications. The overall goal is to increase crop yields, which in turn boosts a farmer’s margins.

Climate is at the forefront of a push toward deriving valuable business insight from Big Data that isn’t just big, but vast. Companies of all types—from agriculture through transportation and financial services to retail—are tapping into massive repositories of data known as data lakes. They hope to discover correlations that they can exploit to expand product offerings, enhance efficiency, drive profitability, and discover new business models they never knew existed.

The internet democratized access to data and information for billions of people around the world. Ironically, however, access to data within businesses has traditionally been limited to a chosen few—until now. Today’s advances in memory, storage, and data tools make it possible for companies both large and small to cost effectively gather and retain a huge amount of data, both structured (such as data in fields in a spreadsheet or database) and unstructured (such as e-mails or social media posts). They can then allow anyone in the business to access this massive data lake and rapidly gather insights.

It’s not that companies couldn’t do this before; they just couldn’t do it cost effectively and without a lengthy development effort by the IT department. With today’s massive data stores, line-of-business executives can generate queries themselves and quickly churn out results—and they are increasingly doing so in real time. Data lakes have democratized both the access to data and its role in business strategy.

Indeed, data lakes move data from being a tactical tool for implementing a business strategy to being a foundation for developing that strategy through a scientific-style model of experimental thinking, queries, and correlations. In the past, companies’ curiosity was limited by the expense of storing data for the long term. Now companies can keep data for as long as it’s needed. And that means companies can continue to ask important questions as they arise, enabling them to future-proof their strategies.

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Prescriptive Farming

Climate’s McCaffrey has many questions to answer on behalf of farmers. Climate provides several types of analytics to farmers including descriptive services, which are metrics about the farm and its operations, and predictive services related to weather and soil fertility. But eventually the company hopes to provide prescriptive services, helping farmers address all the many decisions they make each year to achieve the best outcome at the end of the season. Data lakes will provide the answers that enable Climate to follow through on its strategy.

Behind the scenes at Climate is a deep-science data lake that provides insights, such as predicting the fertility of a plot of land by combining many data sets to create accurate models. These models allow Climate to give farmers customized recommendations based on how their farm is performing.

“Machine learning really starts to work when you have the breadth of data sets from tillage to soil to weather, planting, harvest, and pesticide spray,” McCaffrey says. “The more data sets we can bring in, the better machine learning works.”

The deep-science infrastructure already has terabytes of data but is poised for significant growth as it handles a flood of measurements from field-based sensors.

“That’s really scaling up now, and that’s what’s also giving us an advantage in our ability to really personalize our advice to farmers at a deeper level because of the information we’re getting from sensor data,” McCaffrey says. “As we roll that out, our scale is going to increase by several magnitudes.”

Also on the horizon is more real-time data analytics. Currently, Climate receives real-time data from its application that streams data from the tractor’s cab, but most of its analytics applications are run nightly or even seasonally.

In August 2016, Climate expanded its platform to third-party developers so other innovators can also contribute data, such as drone-captured data or imagery, to the deep-science lake.

“That helps us in a lot of ways, in that we can get more data to help the grower,” McCaffrey says. “It’s the machine learning that allows us to find the insights in all of the data. Machine learning allows us to take mathematical shortcuts as long as you’ve got enough data and enough breadth of data.”

Predictive Maintenance

Growth is essential for U.S. railroads, which reinvest a significant portion of their revenues in maintenance and improvements to their track systems, locomotives, rail cars, terminals, and technology. With an eye on growing its business while also keeping its costs down, CSX, a transportation company based in Jacksonville, Florida, is adopting a strategy to make its freight trains more reliable.

In the past, CSX maintained its fleet of locomotives through regularly scheduled maintenance activities, which prevent failures in most locomotives as they transport freight from shipper to receiver. To achieve even higher reliability, CSX is tapping into a data lake to power predictive analytics applications that will improve maintenance activities and prevent more failures from occurring.

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Beyond improving customer satisfaction and raising revenue, CSX’s new strategy also has major cost implications. Trains are expensive assets, and it’s critical for railroads to drive up utilization, limit unplanned downtime, and prevent catastrophic failures to keep the costs of those assets down.

That’s why CSX is putting all the data related to the performance and maintenance of its locomotives into a massive data store.

“We are then applying predictive analytics—or, more specifically, machine-learning algorithms—on top of that information that we are collecting to look for failure signatures that can be used to predict failures and prescribe maintenance activities,” says Michael Hendrix, technical director for analytics at CSX. “We’re really looking to better manage our fleet and the maintenance activities that go into that so we can run a more efficient network and utilize our assets more effectively.”

“In the past we would have to buy a special storage device to store large quantities of data, and we’d have to determine cost benefits to see if it was worth it,” says Donna Crutchfield, assistant vice president of information architecture and strategy at CSX. “So we were either letting the data die naturally, or we were only storing the data that was determined to be the most important at the time. But today, with the new technologies like data lakes, we’re able to store and utilize more of this data.”

CSX can now combine many different data types, such as sensor data from across the rail network and other systems that measure movement of its cars, and it can look for correlations across information that wasn’t previously analyzed together.

One of the larger data sets that CSX is capturing comprises the findings of its “wheel health detectors” across the network. These devices capture different signals about the bearings in the wheels, as well as the health of the wheels in terms of impact, sound, and heat.

“That volume of data is pretty significant, and what we would typically do is just look for signals that told us whether the wheel was bad and if we needed to set the car aside for repair. We would only keep the raw data for 10 days because of the volume and then purge everything but the alerts,” Hendrix says.

With its data lake, CSX can keep the wheel data for as long as it likes. “Now we’re starting to capture that data on a daily basis so we can start applying more machine-learning algorithms and predictive models across a larger history,” Hendrix says. “By having the full data set, we can better look for trends and patterns that will tell us if something is going to fail.”

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Another key ingredient in CSX’s data set is locomotive oil. By analyzing oil samples, CSX is developing better predictions of locomotive failure. “We’ve been able to determine when a locomotive would fail and predict it far enough in advance so we could send it down for maintenance and prevent it from failing while in use,” Crutchfield says.

“Between the locomotives, the tracks, and the freight cars, we will be looking at various ways to predict those failures and prevent them so we can improve our asset allocation. Then we won’t need as many assets,” she explains. “It’s like an airport. If a plane has a failure and it’s due to connect at another airport, all the passengers have to be reassigned. A failure affects the system like dominoes. It’s a similar case with a railroad. Any failure along the road affects our operations. Fewer failures mean more asset utilization. The more optimized the network is, the better we can service the customer.”

Detecting Fraud Through Correlations

Traditionally, business strategy has been a very conscious practice, presumed to emanate mainly from the minds of experienced executives, daring entrepreneurs, or high-priced consultants. But data lakes take strategy out of that rarefied realm and put it in the environment where just about everything in business seems to be going these days: math—specifically, the correlations that emerge from applying a mathematical algorithm to huge masses of data.

The Financial Industry Regulatory Authority (FINRA), a nonprofit group that regulates broker behavior in the United States, used to rely on the experience of its employees to come up with strategies for combating fraud and insider trading. It still does that, but now FINRA has added a data lake to find patterns that a human might never see.

Overall, FINRA processes over five petabytes of transaction data from multiple sources every day. By switching from traditional database and storage technology to a data lake, FINRA was able to set up a self-service process that allows analysts to query data themselves without involving the IT department; search times dropped from several hours to 90 seconds.

While traditional databases were good at defining relationships with data, such as tracking all the transactions from a particular customer, the new data lake configurations help users identify relationships that they didn’t know existed.

Leveraging its data lake, FINRA creates an environment for curiosity, empowering its data experts to search for suspicious patterns of fraud, marketing manipulation, and compliance. As a result, FINRA was able to hand out 373 fines totaling US$ 134.4 million in 2016, a new record for the agency, according to Law360.

Data Lakes Don’t End Complexity for IT

Though data lakes make access to data and analysis easier for the business, they don’t necessarily make the CIO’s life a bed of roses. Implementations can be complex, and companies rarely want to walk away from investments they’ve already made in data analysis technologies, such as data warehouses.

“There have been so many millions of dollars going to data warehousing over the last two decades. The idea that you’re just going to move it all into a data lake isn’t going to happen,” says Mike Ferguson, managing director of Intelligent Business Strategies, a UK analyst firm. “It’s just not compelling enough of a business case.” But Ferguson does see data lake efficiencies freeing up the capacity of data warehouses to enable more query, reporting, and analysis.

sap Q217 digital double feature3 images6 How Machine Learning Is Changing RecruitingData lakes also don’t free companies from the need to clean up and manage data as part of the process required to gain these useful insights. “The data comes in very raw, and it needs to be treated,” says James Curtis, senior analyst for data platforms and analytics at 451 Research. “It has to be prepped and cleaned and ready.”

Companies must have strong data governance processes, as well. Customers are increasingly concerned about privacy, and rules for data usage and compliance have become stricter in some areas of the globe, such as the European Union.

Companies must create data usage policies, then, that clearly define who can access, distribute, change, delete, or otherwise manipulate all that data. Companies must also make sure that the data they collect comes from a legitimate source.

Many companies are responding by hiring chief data officers (CDOs) to ensure that as more employees gain access to data, they use it effectively and responsibly. Indeed, research company Gartner predicts that 90% of large companies will have a CDO by 2019.

Data lakes can be configured in a variety of ways: centralized or distributed, with storage on premise or in the cloud or both. Some companies have more than one data lake implementation.

“A lot of my clients try their best to go centralized for obvious reasons. It’s much simpler to manage and to gather your data in one place,” says Ferguson. “But they’re often plagued somewhere down the line with much more added complexity and realize that in many cases the data lake has to be distributed to manage data across multiple data stores.”

Meanwhile, the massive capacities of data lakes mean that data that once flowed through a manageable spigot is now blasting at companies through a fire hose.

“We’re now dealing with data coming out at extreme velocity or in very large volumes,” Ferguson says. “The idea that people can manually keep pace with the number of data sources that are coming into the enterprise—it’s just not realistic any more. We have to find ways to take complexity away, and that tends to mean that we should automate. The expectation is that the information management software, like an information catalog for example, can help a company accelerate the onboarding of data and automatically classify it, profile it, organize it, and make it easy to find.”

Beyond the technical issues, IT and the business must also make important decisions about how data lakes will be managed and who will own the data, among other things (see How to Avoid Drowning in the Lake).

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How to Avoid Drowning in the Lake

The benefits of data lakes can be squandered if you don’t manage the implementation and data ownership carefully.

Deploying and managing a massive data store is a big challenge. Here’s how to address some of the most common issues that companies face:

Determine the ROI. Developing a data lake is not a trivial undertaking. You need a good business case, and you need a measurable ROI. Most importantly, you need initial questions that can be answered by the data, which will prove its value.

Find data owners. As devices with sensors proliferate across the organization, the issue of data ownership becomes more important.

Have a plan for data retention. Companies used to have to cull data because it was too expensive to store. Now companies can become data hoarders. How long do you store it? Do you keep it forever?

Manage descriptive data. Software that allows you to tag all the data in one or multiple data lakes and keep it up-to-date is not mature yet. We still need tools to bring the metadata together to support self-service and to automate metadata to speed up the preparation, integration, and analysis of data.

Develop data curation skills. There is a huge skills gap for data repository development. But many people will jump at the chance to learn these new skills if companies are willing to pay for training and certification.

Be agile enough to take advantage of the findings. It used to be that you put in a request to the IT department for data and had to wait six months for an answer. Now, you get the answer immediately. Companies must be agile to take advantage of the insights.

Secure the data. Besides the perennial issues of hacking and breaches, a lot of data lakes software is open source and less secure than typical enterprise-class software.

Measure the quality of data. Different users can work with varying levels of quality in their data. For example, data scientists working with a huge number of data points might not need completely accurate data, because they can use machine learning to cluster data or discard outlying data as needed. However, a financial analyst might need the data to be completely correct.

Avoid creating new silos. Data lakes should work with existing data architectures, such as data warehouses and data marts.

From Data Queries to New Business Models

The ability of data lakes to uncover previously hidden data correlations can massively impact any part of the business. For example, in the past, a large soft drink maker used to stock its vending machines based on local bottlers’ and delivery people’s experience and gut instincts. Today, using vast amounts of data collected from sensors in the vending machines, the company can essentially treat each machine like a retail store, optimizing the drink selection by time of day, location, and other factors. Doing this kind of predictive analysis was possible before data lakes came along, but it wasn’t practical or economical at the individual machine level because the amount of data required for accurate predictions was simply too large.

The next step is for companies to use the insights gathered from their massive data stores not just to become more efficient and profitable in their existing lines of business but also to actually change their business models.

For example, product companies could shield themselves from the harsh light of comparison shopping by offering the use of their products as a service, with sensors on those products sending the company a constant stream of data about when they need to be repaired or replaced. Customers are spared the hassle of dealing with worn-out products, and companies are protected from competition as long as customers receive the features, price, and the level of service they expect. Further, companies can continuously gather and analyze data about customers’ usage patterns and equipment performance to find ways to lower costs and develop new services.

Data for All

Given the tremendous amount of hype that has surrounded Big Data for years now, it’s tempting to dismiss data lakes as a small step forward in an already familiar technology realm. But it’s not the technology that matters as much as what it enables organizations to do. By making data available to anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

“Companies that do not actively invest in data lakes will truly be left behind,” says Anita Raj, principal growth hacker at DataRPM, which sells predictive maintenance applications to manufacturers that want to take advantage of these massive data stores. “So it’s just the option of disrupt or be disrupted.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

About the Authors:

Timo Elliott is Vice President, Global Innovation Evangelist, at SAP.

John Schitka is Senior Director, Solution Marketing, Big Data Analytics, at SAP.

Michael Eacrett is Vice President, Product Management, Big Data, Enterprise Information Management, and SAP Vora, at SAP.

Carolyn Marsan is a freelance writer who focuses on business and technology topics.


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5 AI trends changing the workplace

 5 AI trends changing the workplace

Presented by Jive Software

By now you’ve heard that Artificial Intelligence is poised to either destroy civilization as we know it or miraculously solve all of society’s problems. If you’re a fan of films like 2015’s Ex Machina or the HBO series Westworld, you’ll be forgiven for believing that intelligent machines will inevitably lead to a dystopian nightmare. It’s also unlikely that AI will result in a nirvana of synthetic emotional intimacy or effortless commutes any time soon either. The truth about AI, as with most things, lies somewhere between doomsday and panacea.

In the near term, AI’s greatest potential to affect our lives is in the one place most of us find ourselves every day — at work. These are the five AI trends that will have the greatest impact on the workplace in the near term.

1. Wrangling big data

Data is the grey matter for AI, so for machine learning to succeed it needs access to lots and lots of information. With big data expected to grow to 44 zettabytes of storage by 2020, information is no longer a barrier. That means fragmentation is the remaining enemy of AI. With traditional stack vendors continually adding new tools to their suites and conversational apps siloing information in a mishmash of narrowing message threads, getting at that data is becoming more challenging than ever. Forward-looking organizations are using connected hub solutions and open APIs to unlock that data so future AI systems can get at it.

2. Making your company smarter with “dumb” things: The IoT

Today, there are nearly twice as many “dumb devices” connected to the Internet as there are people on the planet. By 2020, that number is expected to triple. McKinsey estimates that within the next decade the Internet of Things (IoT) could create more than $ 11 trillion dollars annually in global economic value. Along with that flood of cash will be an unprecedented deluge of data. It will be up to AI to make sense of all of that information, but it’s people who will decide how to deal with it. The benefits of the IoT for business are pretty straightforward. For instance, sensors might determine that a customer needs a replacement part, so your sales team will reach out and sell them an upgrade. Internally, the IoT has the potential to deliver even greater business value.

3. Gaining competitive advantage with predictive analytics

Today, capturing information within a company is easy, but gaining insights from the interactions between that information and the systems and people that rely on it is where AI will really begin living up to its lofty promises. As employees move away from routine tasks toward more agile work, processes will become increasingly nimble too. Predictive analytics can take the pressure off by not only contextually serving up the right information at the right time, but by identifying amplifiers, drivers, and experts from across organizations, regardless of role or location.

4. Uncovering valuable insights with the work graph

Decision-making is the next evolution of enterprise AI, but don’t look for the self-aware, vengeance-seeking androids of film; this new era will be all about the work graph. A hub that captures the conversations, content, sentiment, and actions of individuals, groups and teams across multiple collaboration tools will allow predictive analytics to do its stuff. Only when leaders gain insight into those dynamic relationships will they be free to focus on the non-routine tasks that will propel their organizations into the future. Expect to hear a lot more about the work graph in the coming months and years.

5. Increasing efficiencies with interactivity

Voice assistants like Amazon’s Echo are all the rage for consumers, but, as Mary Meeker noted in her most recent Internet Trends report, speech recognition technology is ready for the office as well. In fact, don’t be surprised if voice-first becomes the next big interface for business. Soon you’ll be ordering your very own AI assistant to prioritize your meetings, organize your inbox, and create content, all without clicking on a mouse, typing on keyboard or swiping a touchscreen. Be on the lookout for virtual reality to invade the workplace as well. Once presumed to be strictly for gaming, VR is finding plenty of use cases in business. AI-powered VR systems will allow customers to try before they buy, speed employee onboarding, and supercharge innovation by allowing experts to interact with would-be products while they’re still in the conception stage.

While today’s AI is more likely to recommend nail-biting films about AI to your Netflix queue than to unleash the robot apocalypse, its expectations for business (if not yet its advantages) are high. A well-thought-out strategy will be key to unlocking the benefits we’ve planned for, rather than the consequences we haven’t.

John Schneider is VP of Product Strategy at Jive Software.

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“Changing The System…Was Not A Question Of Contesting And Polemicizing, But Of Blowing Everything Up”

Julius Evola “Changing The System…Was Not A Question Of Contesting And Polemicizing, But Of Blowing Everything Up”

Steve Bannon seems like one of those voracious if prejudiced consumers of media who might be persuaded that ancient aliens helped Hitler become Führer. He has read widely not to debunk his crackpot theories and deep-seated bigotry but to collect fuel for them. The Chief Strategist reminds me of a line from the jacket of Blaise Cendrars’ Moravagine: “He is a monster, a man in pursuit of a theorem that will justify his every desire.”

Until recently, I never connected him to a 2004 film I reviewed called In the Face of Evil: Reagan’s War in Word and Deed,which is one of the more deranged, heavy-handed and paranoid pieces of propaganda I’ve ever seen, Riefenstahl included. I remember thinking then that the director must be a unhinged person badly in need of mental help. Until the last five years or so, I watched 250 to 300 films a year during my entire adult life, and because of the quantity I don’t remember many of them, even some good ones, but I still can vividly recall how delusional and chilling this work was.

When not busy making his pseudo-documentaries, Bannon was a decade ago trying tosell virtual goldfor real money, then peddled tin-pot despotism at Breitbart, and now he’s trying his hand at political alchemy, a white nationalist in the White House, serving as a Rasputin orAlexander Dugin to Trump. As Jason Horowitz of the New York Timesreports, one of the Oval Office insider’s influences is the monocled Italian philosopher Julius Evola, who swayed Mussolini and embraced Hitler, and now serves as a hero to Internet-friendly neo-Nazis.

An excerpt:

Born in 1898, Evola liked to call himself a baron and in later life sported a monocle in his left eye.

A brilliant student and talented artist, he came home after fighting in World War I and became a leading exponent in Italy of the Dada movement, which, like Evola, rejected the church and bourgeois institutions.

Evola’s early artistic endeavors gave way to his love of the German philosopher Friedrich Nietzsche, and he developed a worldview with an overriding animosity toward the decadence of modernity. Influenced by mystical works and the occult, Evola began developing an idea of the individual’s ability to transcend his reality and “be unconditionally whatever one wants.”

Under the influence of René Guénon, a French metaphysicist and convert to Islam, Evola in 1934 published his most influential work, “The Revolt Against the Modern World,” which cast materialism as an eroding influence on ancient values.

It viewed humanism, the Renaissance, the Protestant Reformation and the French Revolution all as historical disasters that took man further away from a transcendental perennial truth.

Changing the system, Evola argued, was “not a question of contesting and polemicizing, but of blowing everything up.”

Evola’s ideal order, Professor Drake wrote, was based on “hierarchy, caste, monarchy, race, myth, religion and ritual.”

That made a fan out of Benito Mussolini.•

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How Digital Transformation Is Changing The Future Of E-Commerce

Business intelligence (BI) tools first appeared on the enterprise technology scene several decades ago, at birth clumsy and difficult to use but ultimately improving the flow of data through organizations from their operational systems to decision support. Data warehousing cut the time it took to access data, but even at their full maturity, BI systems could do little more than produce data and reports in a traditional organized way. The rules-driven software wasn’t actually providing intelligence at all.

But with the advancement of artificial intelligence and—more importantly—machine learning, true business intelligence is actually on its way to the enterprise. Such self-learning software will run on servers, be built into bots, drive decision-making systems, be embedded into cars or aircraft, and become the beating heart of mobile devices.

Increased data-processing power, the availability of big data, the Internet of Things, and improvements in algorithms are converging to power this actual business intelligence. To be clear, this will be an evolution rather than a revolution. There are a number of factors that could limit the progress of machine learning and its integration into business, from quality of data and human programming to cultural resistance. However, the question is when, not if, the BI tools of today become a quaint relic of earlier times and real business intelligence emerges.

Beyond sci-fi AI

Artificial intelligence (AI), a term dating back to the 1960s, is tossed about quite a bit these days. It’s an umbrella descriptor that refers to computers capable of doing things that a human typically would. It’s often inaccurately used interchangeably with machine learning. Machine learning, however, is a specific subset of AI that uses statistical methods to improve the performance of a system over time. Any programmer can write code to develop a program that more or less acts like a human. But it’s not machine learning unless the systems is learning to how to behave based on data. Machine learning comes in several flavors, sometimes referred to as supervised learning (the algorithm is trained using examples where the input data and the correct output are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning (the algorithm is rewarded for penalized for the actions it takes based on trial and error). In each case, the machine is able to learn from data—structured and increasingly unstructured in the future —without explicitly being programmed to do so, absorbing new behaviors and functions over time.

Insert2 How Digital Transformation Is Changing The Future Of E CommerceGartner recently placed machine learning at the height of “inflated expectations” in its report, noting that this emerging capability is two to five years from mainstream adoption. But those immersed in machine learning development are grounded in reality. And the reality is that they are making significant strides. Machine learning mimics human learning; it takes time.

The big advantage machines have over us is that they can handle massive amounts of data, take advantage of ever-faster processing power, and run (and thereby) improve 24 hours a day. Over just the last four years, the error rate in machine learning-driven image recognition, for example, has fallen dramatically to near zero—practically to human performance levels.

Still, every instance of machine learning is different. Just as, for us, learning to play piano is different from learning how to crawl, each instance of machine learning is different. It may take longer for a computer to learn to analyze text than it takes it to recognize the meaning of a furrowed brow.

Machine learning for the rest of us

Digital giants are leading the way in machine learning development. Google has more than 1,000 machine learning projects underway, including its Google Brain project. IBM continues to make headlines with Watson. Microsoft uses neural networks to powers its search rankings, photo search, and translation systems while Facebook translates 2 billion user posts in more than 40 languages each day in the same manner. In the last year alone, venture capital firms have poured approximately $ 5 billion into machine intelligence startups.

At this early stage, there are no concrete baselines for machine learning adoption rates in the rest of industry. Consumer adoption of machine-learning technologies has taken off with the success of Amazon’s Echo and Apple’s Siri. It’s an important component in fraud detection and surveillance, image and voice recognition, and product recommendations. But, as a recent report from 451 Research pointed out, but enterprise adoption is less pervasive. To broaden the enterprise use of machine learning, some of the biggest tech players in the field, such as Google, Microsoft, Intel, and Facebook make their older machine learning systems and designs available to the open source community.

Machine learning could bring significant value to the business: improving the core functionality of existing software and analytics, uncovering previously inaccessible insights hidden in large data sets unstructured data formats, and taking over tasks like image recognition, text analysis, and repetitive knowledge work. The potential use cases are seemingly endless, from supply chain and risk detection to logistics and technical support to behavioral analysis and customer support.

Limiting factors

Machine learning is not a silver bullet and there are a number of issues that companies must address. Because it is based on algorithms that learn from data rather than relying on rules-based programming, effective machine learning is dependent on relevant and reliable data—and lots of it. Business leaders must take a hard look at available data (the quality of it, the gaps in it, the silos around it) to extract the value of self-learning capabilities.

Insert1 How Digital Transformation Is Changing The Future Of E CommerceWhat’s more, machine learning is ultimately guided by human decision making. Humans will decide what problems the technology will be used to solve. Humans will develop the algorithms to employ. And humans don’t necessarily operate on logic.

Perhaps most importantly, the adoption of machine learning is going to be determined more by organizational and cultural forces than by technical factors. Humans are yet not machine ready. Machine learning will need to be designed with the man-machine interaction in mind. Fear, uncertainty, and doubt about how these self-learning systems will impact our roles and our livelihoods must be addressed, and significant investment must be made in change management as business processes and models are reworked to integrate self-learning systems.

The rise of the machines in business—and beyond

Business leaders have been talking about the importance of context-sensitive systems to the enterprise for several years. Machine learning could finally bring that concept to life—from smart software to smart vehicles to intelligent machines and robots to machine learning-enabled digital assistants and to smart grids that can learn to understand their environment and adapt on their own.

Smart machines will become an integral part of business—and daily life—creating insight from data in ways that humans on their own never could. That will lead to new levels of automation, cost savings, and process change. Gartner predicts that in 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines and customer-facing digital assistants will recognize individuals by face and voice across channels and partners. Self-learning algorithms will introduce unprecedented levels of efficiency in business systems taking over highly repetitive work. On a personal level, smart assistant technology could turn our mobile devices—already capable of voice response, into interactive learning assistants tasked with helping us navigate our daily lives. Machine learning could uncover new efficiencies in our complex and overstressed infrastructure systems including energy, logistics, healthcare, IT, and even education.

The value that machine learning can deliver will be dependent on the degree to which these systems can deal with structured and unstructured data (which remains a challenge) as well as the availability of useful data and quality algorithms. Taking over the mundane and repetitive tasks within business systems and for consumers is all but guaranteed. Organizations are starting to collect unstructured und unprocessed data in so-called data lakes. If companies open up more of their self-learning data and designs, that shared insight will result in ever better algorithms and more accurate and effective machine learning capabilities.

If machine learning matures to the point that it can handle unstructured data, organizations openly share data, and algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines and software. That will enable highly context-sensitive insight at both the large scale and individual level. We can only guess about the level of automation and support that will result, but the impact on business—and society—will be significant.

However this evolution plays out, it will take time. But business leaders can prepare now for the rise of machine learning, taking a hard look at data structures and availability, freeing up information from siloed systems, identifying the richest areas for machine-fueled insight and improvement, and addressing the cultural and change management challenges that will be required to take advantage of this real business intelligence.

Download the executive brief Rise of the Smart Machines

ML Thumb How Digital Transformation Is Changing The Future Of E Commerce

To learn more about how exponential technology will affect business and life, see Digital Futures in the Digitalist Magazine.

For more on next-generation business intelligence in the enterprise, see An AI Shares My Office.


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