Tag Archives: High

Webcast: Introducing the Latest in High Availability from Syncsort

Syncsort has released their latest on-demand webcast: Introducing the Latest in High Availability from Syncsort. In a recent survey of 5,632 IT professionals – on the topic of data protection strategies and IT priorities – 67% responded with data availability as the top measure of IT performance. These statistics clearly state how the impact of downtime on customers, partners and employees is increasingly visible and costly in today’s constantly connected world.

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Syncsort’s market-leading portfolio of high availability and disaster recovery solutions continues to expand and evolve to meet the demands of organizations faced with exploding data volumes, limited IT resources and intensifying pressure for non-stop access to data and systems.

Learn about the latest developments in our IBM i high availability portfolio that can help your organization meet their critical recovery point and recovery time objectives.

View the webcast now!

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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 High fivin


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Expert Interview (Part 3): Tobi Bosede on Being a Double Minority in a High Tech Field

At the Strata Data Conference in New York City in the fall, Paige Roberts of Syncsort had a chance to sit down with Tobi Bosede, Sr Machine Learning Engineer.

In part 1 of this blog series, Bosede spoke on what goes into being a Machine Learning Engineer as well as some of the projects she is currently involved with. For part 2, Bosede described predicting trade volumes and the correlation between volume and volatility.

In this part Roberts and Bosede discusses Bosede’s perspective of being a “double minority” in the tech world.

Expert Interview Part 3 Tobi Bosede on Being a Double Minority in a High Tech Field banner Expert Interview (Part 3): Tobi Bosede on Being a Double Minority in a High Tech Field

Roberts: As a woman of color in a male dominated field, how has your experience been? Has it affected you in any way?

Bosede: Do you mean like as a black woman or…?

Yeah, I’ve got a friend, who works over at Hortonworks, Yolanda Davis, and she was very involved in Black Girls Code. I also interviewed Neha Narkhede who is the CTO of Confluent and a woman of color. You don’t see a lot of women of color as the CTO of companies, and she talked about what it was like to be different from others in her field, especially at her level. My own CTO is from Turkey. At Syncsort, I live in a world where there’s a lot of women in our area, but it’s almost like it’s an isolated world. It doesn’t seem like it’s that way in a lot of other places.

Yeah, I have worked on teams where I the only woman or only black person on my team. The lack of diversity worsens as you become more senior in tech.

Yeah, I’ve been the only woman on my team many times and it’s very odd. I always feel this pressure to be, sort of, one of the guys.

I don’t actually try to be like one of the guys, I intentionally stay true to myself. Because, first of all, I’d be unhappy doing that. And then secondly, the team will never accept me as one of the guys, because I mean, they go out, they play basketball, they never invite me [laughs]. And sometimes I’m like, “Aww, I wish,” but I don’t even like basketball, you know? I don’t even want to play basketball. I just want to be…

You just want to be included.

I just want to be invited.

Yes, I get that.

So, in terms of just going back to the main question, that’s what I tell other people who are trying to break into the field. You don’t have to change yourself, just do what you are there to do. Do what they hired you for. I have had experiences where people doubt or people underestimate you, and you have to just prove them wrong, and keep it moving.

I know it’s tough emotionally, especially if you are just joining a team or you are new, but I also like to seek out sources of emotional support or positive influences. If you are only getting a lot of negativity, that’s going to affect you, so you have to balance it with encouragement.

It hasn’t been easy for me to navigate dynamics that favor certain demographics over another in my career, but I know the value I bring to the table and have been able to thrive because I look for opportunities to showcase my strengths.

Well said. Is there anything that you have going on at the moment that you want to let us know about?

I have actually written a blog post with Pearson about being what I call the double minority in technology, as well as an O’Reilly blog which is basically tips and tricks for someone working with Spark.

I’ll be sure to include links to those! Well thank you, Tobi. It has been great speaking with you.

Thank you.

Download our free eBook, “Mainframe Meets Machine Learning“, to learn about the most difficult challenges and issues facing mainframes today.

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Highly Available Data: Why High Availability Is Not Just for Apps

High availability is a buzzword in IT today. Usually, it refers to applications and services that are resistant to disruption. But it should also apply to your data. Here’s why.

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What Is High Availability?

High availability refers to the ability of an application, service or other IT resources to remain constantly accessible, even in the face of unexpected disruptions.

In an age when a cloud service that fails for even just a few hours can significantly impact the ability of businesses to maintain operations, and when vendors typically guarantee certain levels of uptime via SLA contracts, maintaining high availability is crucial.

Unlike in the past, when users expected infrastructure to fail from time to time, downtime is unacceptable in most contexts today.

In reality, virtually every type of service or resource will fail occasionally. 100 percent uptime is not a realistic goal; even the best-managed services go down sometimes. But uptime on the order of 99.99 percent or higher (AWS famously promises “11 9s” of availability for its S3 storage service, for instance) is now standard. That’s the type of high availability that organizations strive for today.

High Availability for Data

In most cases, when people talk about high availability, they’re thinking about applications and services. Using automated server failover, redundant nodes and other strategies, they design systems that allow applications and services to continue running even if part of their infrastructure fails.

Yet the high availability concept can and should be extended to data. After all, without data to crunch, many applications and services are not very useful. If you plan a high availability strategy that addresses only your applications, you fall short of ensuring complete business continuity.

Achieving Data High Availability

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What does high availability for data look like in practice? All of the following considerations should factor into a data high availability strategy:

  • Servers that host data need to be resilient against disruption. You can, as noted above, achieve this by using redundant servers to host your data, and/or automated failover.
  • Databases should be architected in such a way that the failure of one database node won’t cause the database to be inaccessible. Databases should also be able to restart themselves automatically if they do crash, in order to minimize downtime.
  • If you rely on the network to access data, which you probably do, network availability is an important component in data high availability.

Highly Accessible Data

Data high availability can be taken a step further, too. In addition to keeping your data infrastructure and services up and running, you can build an even more effective high availability strategy for data by ensuring that your data is highly accessible.

Highly accessible data is data that you can work with readily. It’s quality data that is consistent and available in the format or formats that you need it to be in order to work with it. It’s data that is compatible with whichever tools you are using for analysis and interpretation.

By aiming for high data accessibility as well as high availability, you ensure not only that you can always reach your data, but also that the data is ready to use.

To learn even more about the state of disaster recovery preparedness in organizations today, read Syncsort’s full “State of Resilience“ report.

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5 Tips to Ensure a High User Adoption Rate when implementing your New Microsoft Dynamics 365 Solution – After Go-Live

CRM Blog 5 Tips to Ensure a High User Adoption Rate when implementing your New Microsoft Dynamics 365 Solution – After Go Live

The implementation process of a new CRM solution such as Microsoft Dynamics 365 does not end on the go-live date: afterwards, a continuous effort is required to ensure that your users make full use of the system and its functionalities. After all, an implementation project is only truly successful if it helps your organization achieve its business objectives, simplify its processes and increase its resource productivity.

But how do you ensure that users actually use your solution once it has been implemented? Here are 5 tips to drive user adoption in your organization and ensure that the solution keeps evolving with the needs of your users.

  1. Schedule follow-up training and review sessions. It’s important to schedule time for users to bring up any concerns, questions or issues they may have. You can also use the insights obtained during these sessions or in your user surveys to organize additional training sessions to address specific issues that have been brought up.
  1. Consolidate user feedback and data to assess your level of success. Compare the benchmarks and KPIs identified at the start of the process to your current numbers and see what areas need to be improved. Review surveys and listen to user feedback to assess their knowledge of the solution, then work with them on finding solutions.
  1. Encourage your end-users to get involved. Have them share tips and ideas as to how the solution can be used to improve their daily tasks and processes. Promote teamwork so that they can learn by helping each other. Hold departmental meetings where team members can brainstorm ideas to improve business practices.
  1. Have super-users available to answer day-to-day questions to avoid people reverting back to their old ways. Old habits die hard: users might find themselves finding workarounds or going back to their previous methods when they’re stuck. Having super-users readily available will help nip these tendencies in the bud.
  1. Show the positive changes to both end-users and management. Identify success stories and share them with the rest of the team. This will encourage continued adoption from the part of the users while also satisfying management by showcasing the return on your investment and improved KPIs.

With these tips, you will be well-equipped to ensure that your new CRM solution helps your organization achieve its objectives and maximize your return on your investment. For more information, Microsoft offers documentation to help your organization measure the success of its implementation as well as sample user surveys.

By JOVACO Solutions, Microsoft Dynamics 365 specialist in Quebec

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High Wheel Bicycle Race

 High Wheel Bicycle Race

Penny farthing high wheel racing skills.

Wikipedia.

 It was popular in the 1870s and 1880s, with its large front wheel providing high speeds (large distance for every rotation of the legs) and comfort (shock absorption through the wheel)

“Penny farthing race, 1928.”
Image courtesy of https://imgur.com/gallery/QJS13T0.

See:

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Don’t Just Back Up Data. Do It With High Availability In Mind

Do you back up your data with high availability in mind? Or do you just back it up to back it up? A backup strategy that doesn’t lead to high availability falls short of its full potential.

By now, most people know that they should back up data. More than half of them have backup routines in place.

However, simply backing up your data is not necessarily enough to achieve your goals. There is a reason why 75 percent of people who back up data are not able to restore all of it following a failure.

If your backup strategy is designed simply to back up data for backups’ own sake, rather than advancing a broader high availability agenda, you may as well not be doing backups at all.

Data backups won’t help you to avoid serious disruptions when something unexpected happens unless they’re complemented by the following considerations and strategies for achieving high availability.

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Data Restoration Process

To minimize downtime and maximize availability during a crisis, you need to be able to restore data quickly from backups to production systems.

To do this, you must have a restoration plan in place before disaster strikes. You don’t want to wait until your business operations have been disrupted to start figuring out how you move data from backup locations to production systems.

This is why you should develop specific data restoration plans ahead of time. Although you can’t predict every variable that might be at play during a data recovery scenario, you can create general procedures that your team will follow when moving data from backups.

You can also have data migration and transformation tools (like those in Syncsort’s Big Data solutions suite) preinstalled and preconfigured, if you’ll need them as part of the data restoration.

Automated Failover

Even better than having to restore data from a backup location is not having to restore it at all because your workloads automatically move from one host environment to another in the event that the first host environment fails.

This type of functionality, which is called automated failover, is delivered by solutions like Trader’s, which provides automated failover features as part of its high availability platform for IBM i systems.

Distributed Data Replication

Simply backing up your data somewhere is often not enough to achieve high availability. You must back it up in a way that maximizes its chances of remaining available in the event of disruption to your infrastructure.

One way to do this is to replicate your data automatically across a distributed environment of servers or storage locations. With automatic, distributed data replication, your data always exists in multiple locations at once. And because those locations are spread out — in the sense of including either multiple servers within your data center or, better, multiple data centers in different geographic locations — the data will remain intact even if some storage locations fail.

3-2-1 Backup Strategy

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Another handy way of maximizing data availability is to follow what is known as the 3-2-1 data backup rule. According to this rule, you should:

  1. Have at least three distinct copies of your data at all times.
  2. Back up your data to at least two different types of storage (such as an on-premise server and a cloud environment).
  3. Keep at least one off-site copy of your data.

These procedures help to ensure that if one type of data storage fails, or your local storage is wiped out, your data will still be available.

The 3-2-1 backup strategy may not be necessary if you already do automatic data replication across distributed systems. But if you lack the resources for that type of solution, the 3-2-1 approach is an easy and effective way to maximize data availability.

To learn even more about the state of disaster recovery preparedness in organizations today, read Syncsort’s full “State of Resilience“ report.

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Under A Shadow Of Doubt, Self-Driving Cars Shift Into High Gear

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

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AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.

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This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.

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The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

Q118 ft2 image3 automation DD Under A Shadow Of Doubt, Self Driving Cars Shift Into High GearIn fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

Q118 ft2 image5 talkingtoAI DD Under A Shadow Of Doubt, Self Driving Cars Shift Into High GearHow Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.
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Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!


About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

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5 Tips to Ensure a High User Adoption Rate when implementing your New Microsoft Dynamics 365 Solution – User Training

CRM Blog 5 Tips to Ensure a High User Adoption Rate when implementing your New Microsoft Dynamics 365 Solution – User Training

In our previous article, we offered some tips to improve user adoption rate even prior to the implementation of a new CRM solution. That said, proper training tailored to the specific needs of end users is essential to ensure a high user adoption rate for a new CRM solution such as Microsoft Dynamics 365. The users of the solution need to become familiar with its features and processes before they can leverage its benefits to increase their productivity. No matter how much money your organization invests into a new system, if the training fails to address users’ specific realities, they will simply revert to their old habits.

The next 5 tips will help you tailor your user training to ensure that this doesn’t happen and that users obtain a full understanding of the system and how it will simplify their daily tasks.

  1. Give all users an overview of the system. Even if each team only uses part of the system, it’s important that all users get a feel for the entire system and navigate through it. Knowing how the entire system works will help them learn the processes relevant to their specific responsibilities and how they relate to other departments.
  1. Show users how the new solution can facilitate their daily tasks. If possible, introduce them to personalized dashboards and views, as well as advanced searches, so that they are aware of how the system can be tailored to meet their specific needs and facilitate the experience. Use customization options to reduce the time spent navigating between entities or records.
  1. Split training into small teams. This way, the training can be tailored to the users’ specific needs, roles and tasks, ensuring that the information is relevant. Moreover, users will be more likely to ask questions and pay attention if they are a part of small group.
  1. Prioritize tasks and set clear goals for the utilization of the CRM. Having clear objectives and tasks to master before moving on to the next will help users focus on what has to be learned. It also makes it easier to assess whether they fully understand how processes work, making it easier to define and adjust the rest of the training.
  1. Have your super-users give the training. At this point, user questions are usually more about the process and less about features. The super-users of the solution are in the best position to guide end-users through the process and ensure that they have fully internalized it prior to using the solution for their day-to-day activities.

By getting the proper training, your end-users will be able to leverage the functionalities and potential of the new solution to carry out their daily tasks and activities and improve their productivity. Should you find yourself in need of advanced or tailored training sessions, please communicate with JOVACO for more information about our CRM and Microsoft Dynamics 365 training sessions.

By JOVACO Solutions, Microsoft Dynamics 365 specialist in Quebec

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Upcoming Webinar: How To Deliver High Performance Capital Markets CRM Customer Experiences

WEBINAR 300x225 Upcoming Webinar: How To Deliver High Performance Capital Markets CRM Customer Experiences

As the Capital Markets industry continues to grow and as product companies continue to fight for “Top of Mind Awareness” via your channels, it is imperative to differentiate by providing personalized, proactive, and predictive customer experiences. On September 21, PowerObjects, an HCL Technologies Company, Forrester, and Discovery Data will host a webinar to teach you how to create customer loyalty in Capital Markets by leveraging CRM to deliver the right messages, to the right constituents, at the right time regardless of channel, business unit, customer, or employee!

Webinar Details:
September 21, 1-2pm CST
Register here!

Key Takeaways:

  • Learn how Microsoft Dynamics 365 will streamline the way in which you manage complex hierarchies, associations, teams, and relationships
  • Preview how an integrated Dynamics 365 and LinkedIn “Voice of the Customer” record will empower your touch points
  • Learn how Discovery Data can empower your CRM and LinkedIn data by providing accurate performance and data points for interacting with your firm, branch, and contact segmentation

Speakers:
speakers final Upcoming Webinar: How To Deliver High Performance Capital Markets CRM Customer Experiences

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