Cloud computing is a market that’s consistently increasing. For this reason, there are many cloud computing trends available for us to talk about.
The growth in last year’s cloud computing market was astonishing, and it’s only set to grow even more through 2020 and beyond. It’s for this reason that so many businesses are relying on cloud computing.
Whether you’re looking for VPS cloud hosting, application software, virtual networks or databases, there are plenty of cloud services to grab by the horns and take advantage of.
If speed and security are important factors in your company, cloud computing is definitely the way forward, and I highly recommend you looking further into this world.
General Cloud Computing Statistics
By 2025, the cloud computing market is expected to exceed $650 billion
80% of organisations are expected to use cloud services by 2025
The main reason people turn to cloud computing is due to being able to access data from everywhere
By 2021, cloud data centers will process 94% of workloads (Cisco)
In 2018, cloud infrastructure spending surpassed $80 billion (Canalys)
Cloud Type Statistics
The average business runs 38% of workloads in public and 41% in private cloud (RightScale)
In small to medium-sized businesses, 43% use public cloud (RightScale)
In 2019, the revenue from the global public cloud computing market was set to reach $258 billion (Statista)
Covid-19 forced organizations to rethink the future of physical workspaces. Everything from desk layouts to conference rooms to communal areas needs to be approached with a new lens of employee health and safety. Data plays a critical role in how leaders structure their reopening plans, identify metrics for reopening and measure effectiveness.
Some countries are already reopening offices as the rest of the world watches and learns. One of the biggest lessons from the Asia Pacific region so far, as Gartner suggests, is the importance of “transparency” and “iteration.” As Hernan Asorey, chief data officer at Salesforce explained, “We are always assessing the data we have available to make decisions. For every evolving need, we pragmatically look at what exists from trusted sources, we vet it with experts in the field, and then we assess, augment, learn and adapt.”
Since organizations are faced with entirely new challenges—all dependent on a variety of factors including office location, workspace type and workforce size—leaders need data to inform a flexible approach to planning, informed by data.
There are four areas where data can inform your reopening strategy:
Creating a COVID-19 task force
Tracking regional policies
Informing workspace planning
Analyzing employee survey data
These areas represent a starting point and not an exhaustive list. Since all of these details vary based on your organization, this piece should be used for informational purposes only.
Reopening is a cross-functional effort. Organizations are instituting centralized, assigned Covid-19 task forces—made up of a variety of people with a diverse set of skills and perspectives—to manage details like workplace logistics and employee communications. This group should represent your workforce as a whole.
“At Tableau, we’re bringing together a variety of stakeholders into workplace conversations,” said Debbie Smith, senior manager of workplace at Tableau. “We have perspectives—and data—from all aspects of the company, from security to HR to real estate to marketing to procurement. We’re also bringing in outside experts to inform details like capacity planning and air filtration.”
All of these stakeholders work with different data points to inform their perspectives. For example, health and safety teams might monitor regional policy data, procurement might use data to inform any new equipment purchases, like panels between desks, and IT might work with workplace teams to determine how to replace existing equipment like phones or headsets.
Creating a dedicated team is a foundational step in a reopening strategy, because data is useful only when people can provide context and take action.
Reopening strategies are largely dependent on local policies. In addition to these policies, organizations are also faced with a long list of guidance from the Occupational Safety and Health Administration (OSHA), the Centers for Disease Control and Prevention (CDC), the Environmental Protection Agency (EPA) and more.
Organizations are exploring centralized dashboards to track changing policies and to inform key indicators to determine when it is safe to reopen offices. SC&H Group’s data analytics team, for example, created a sample dashboard that shows what this could look like for a company in the United States. The dashboard highlights legislation on a state-by-state basis alongside a map showing number of cases.
Christopher Adolph, associate professor of political science and adjunct associate professor of statistics at the University of Washington, is curating and maintaining a data set on state policies related to Covid-19 from open source data. He encourages data and analytics leaders to take a focused approach when visualizing local policy data. That might mean considering other visualization types beyond maps to focus on specific, regional metrics that show the impact of Covid-19.
“If I were an organization,” shares Adolph, “I would structure a visualization to show what’s happening in each location associated with my business, with filters that allow stakeholders to sort through stringency of policies, trends in mobility and trends in cases. I would want to see a time series of how policies change over time as cases increase or decrease in a region.”
Data analytics and geospatial services firm Lovelytics created a dashboard template combining Covid-19 case data from the Tableau Covid-19 Data Hub with sample HR data, providing a breakdown of at-risk employees by building, age group and location. Although this example was originally developed for companies looking to stabilize in a crisis, these types of dashboards could also become a single source of truth in the event of another wave of the virus after reopening.
Some of the most complex challenges that employers face in the wake of Covvid-19 are related to workspace layouts. Many organizations have adopted open office concepts, making it difficult to enforce six-feet guidance between employees. They’re also evaluating the use of shared spaces like kitchens, bathrooms and elevators along with high-end air filtration systems to reduce the spread of infectious droplets. One way that employers can start to make sense of all of these logistical decisions is through data.
Some key data points that employers are collecting (or considering collecting) around space utilization are:
Physical distance (between desks and in shared spaces)
De-densification (removing furniture in communal spaces like kitchens and conference rooms)
Air movement and ventilation
Pinch points like elevators and bathrooms
These new challenges are leading organizations to take a new approach to workplace metrics. Salesforce, for example, is analyzing data to model staggered arrival times so they can effectively manage elevator capacity. Salesforce is also partnering with Siemens on key solutions for a “touchless office,” where organizations can manage occupancy and location data to augment their contact tracing process (on an opt-in basis).
Global commercial real estate services firm Cushman & Wakefield noted in its Recovery Readiness guide that organizations may want to “invest in operational building technologies that enhance the integration, visibility, and control of building and workplace systems” (like occupancy sensors or air quality monitoring capabilities). The company also piloted a new office layout in Amsterdam deemed “The 6-Feet Office,” using large circles and visual cues to enforce a six-foot separation between employees.
An example dashboard from Tableau Zen Master Ken Flerlage. Note that this is intended to be an example and not a template. There are a variety of factors in workplace planning that organizations need to consider beyond the six-feet guideline. Interact with the full visualization.
Recently, Tableau Zen Master Ken Flerlage explored what an office space visualization could look like, drawing six-feet circles around each desk. If a desk area doesn’t follow the six-foot perimeter, then the circle turns red and indicates that the company needs to rethink the layout of that office area. In Flerlage’s blog post about the visualization, Amanda Makulec, data visualization lead at Excella and Bridget Cogley, senior consultant at Teknion, explain that this template is a good starting point for people as they rethink office seating arrangements, but that there needs to be additional thinking around the complexities of how people move in an office setting.
To account for these complexities, some companies are hiring external experts to help set these parameters and inform logistics planning. All of these concepts will require additional iteration and flexibility as organizations put them into practice.
Whether or not they can physically return to work, organizations also need to think about employee needs. Are employees comfortable returning to work—and if so, in what capacity? Some employees need to stay home with kids as schools remain closed, others may have compromised immune systems, and some may just be more comfortable working from home until a vaccine is available to the public.
Some companies, including Tableau, are gauging employees’ concerns through regular surveys. They’ll ask questions about general well-being, like how they’re adapting to work-from-home and how the company can support them. Companies in the logistical planning stages might ask questions about whether or not employees are comfortable returning to work to determine reopening schedules.
An example dashboard from the Tableau people analytics team showing results of a COVID-19 work-from-home survey (this dashboard contains sample data). Interact with the full visualization.
With this data at their fingertips, organizations can analyze:
Employee needs like office equipment or childcare support services
Once offices reopen, companies could join this survey data with utilization data to understand how many employees are actually coming into the office on a regular basis. This can help inform whether or not employees are comfortable with new working conditions.
Analyzing the results of these surveys can help organizations develop important metrics around how the pandemic is affecting their employee base and help them determine how to take action.
From connection through collaboration, Tableau is the most powerful, secure, and flexible end-to-end analytics platform for your data. Elevate people with the power of data. Designed for the individual, but scaled for the enterprise, Tableau is the only business intelligence platform that turns your data into insights that drive action.
Organizations today are witnessing an increase in data volumes across various industries that need addressing to maintain a differentiated data management practice and stay competitive. The cloud offers capabilities to address any data management need; however, not all workloads can migrate to the cloud easily. This could be due to legacy application dependencies residing on-premises, data residency regulations or low-latency computation needs, such as in healthcare, financial and manufacturing industries.
Read on to discover:
Constraints that keep data tied to on-premises environments
Why companies should embrace hybrid data management practice
How AWS Outposts meets your hybrid data needs
Data management constraints organizations face
Data residency regulations, low-latency requirements, and complex application migrations are some of the main issues surrounding the management of data. The journey to the cloud also creates challenges for data infrastructure and development teams to design data management models that provide consistent and reliable cloud services on-premises. These challenges can vary depending on the specific industry and operational requirements, but include:
Hybrid cloud benefits
Organizations can deploy cloud infrastructure on-premises, determine data processing priorities, and when ready, migrate towards the cloud.
1. Cloud capabilities on-premises
Amazon EC2 instances featuring Intel® Xeon® Scalable processors brings the same cloud capabilities on-premises.
2.Seamless migration to the cloud
Build an application once and deploy it in the cloud, on-premises, or in a hybrid architecture with consistent performance.
3. Accelerated modernization
Companies can accelerate the adoption of cloud services on-premises across teams.
4.Focus on what matters
Reduce the time, resources, operational risk, and maintenance downtime required for managing IT infrastructure, giving you the ability to focus on what differentiates your business.
AWS offers a hybrid solution to meet data management needs
AWS Outposts catalog includes options supporting the latest generation Intel powered EC2 instance types with or without local instance storage. Organizations can choose from a range of pre-validated Outposts configurations offering a mix of EC2 and EBS capacity which are designed to meet a variety of data management needs.
AWS Outposts options:
Innovate with AWS
Healthcare use case:
Medical professionals manually collect structured data to store and analyze in vital fields such as cancer staging, medical/family history and patient-reported symptoms. AWS Cloud services automates data collection, where using machine learning inference models amplify data processing and extraction of valuable insights. AWS provides the tools, services and APIs to deliver real-time video analytics and pattern matching, while delivering on-premises flexibility and access to cloud capabilities when needed.
Finance use case:
Financial or government institutions that need to comply to specific data regulations use hybrid cloud to meet their contractual obligations with their customers and demonstrate compliance with legal policies. AWS Outposts allow these organizations to maintain data visibility, process sensitive data locally, including collecting local cache and filtering, and when needed connect to Local Zones or send it to AWS Region.
Security use case:
Companies that are interested in using Outposts to run physical security environments, such as video surveillance, badging systems or security systems, can build and run these workflows on Outposts, archiving relevant data to S3/Glacier within the AWS Region for forensic analysis.
Getting started with AWS
With a consistent set of infrastructure, services, tools, and APIs, AWS simplifies your data management and data migration process, reducing the effort and complexity involved. Leverage the latest Intel technology innovations to accelerate modernization at your edge too. Find out more about hybrid data management for your organization using AWS Outposts in our full guide here.
AWS infrastructure solutions allow enterprises across all industries the opportunity to bring AWS services closer to where it’s needed, such as on-premises with AWS Outposts, in large metro areas with AWS Local Zones, or at the edge of 5G networks with AWS Wavelength. These solutions offer enterprises the capability to deliver innovative applications and immersive next-generation experiences using AWS cloud services where they need it. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, speed time to market, and become more dynamic. To learn more about AWS infrastructure solutions, visit aws.amazon.com.
Enterprises are rapidly adopting the cloud for greater agility and cost savings. However, they often find that some applications need to be re-architected or “”modernized”” before they can be moved to the cloud. Others need to remain on-premise due to low-latency or data processing requirements. As a result, enterprises are looking to hybrid cloud architectures to integrate their on-premises and cloud operations to support a broad spectrum of hybrid use cases, such as data center extension, VMware cloud migration, or building and managing applications using a common set of cloud services and APIs across on-premises and cloud environments. In this tech talk, you will learn how you can build your hybrid cloud architecture with AWS. We will cover our extensive portfolio of services that offer seamless integration between your on-premises and cloud environments for any hybrid use case. Learning Objectives: – Discover AWS services that offer seamless integration across on-premises and cloud environments – See how to build the hybrid cloud architecture to support your use case – Learn about new services that bring cloud services on-premises
In early 2020, we published our 2020 Data Trends Report featuring our predictions for the major trends that will shape the future of data and analytics. Little did we know that the world as we knew it would completely change by spring. We recently decided to take a fresh look at these data trends to assess the larger cultural and organizational impacts of Covid-19. What we found was an even greater urgency for data and analytics, to highlight inequities in the world and to help organizations empower collaboration and increase agility.
For this piece, we’ll focus on three trends that have seen rapid acceleration this year—growth in data literacy programs, the emergence of data as a critical resource for advocates highlighting racial inequality, and executive involvement in data culture. For a deeper dive into other topics like artificial intelligence and data storytelling, read the full 2020 Data Trends report.
Early 2020 Prediction: Organizations look to academia as a data literacy incubator
How It’s Evolved: Data literacy remains a foundational mission for agile businesses
Earlier this year, we predicted that data literacy would continue to be high on leaders’ priority lists throughout 2020. Despite a pandemic that led to budget cuts and employees working remotely, data literacy is more important than ever—and leaders are getting creative with their approach to training and development around analytics.
Last year, IDG reported that organizations were making major investments in digital initiatives—an average of $15.3 million in 2019, with 41% of that budget allocated to people and skills. Instead of reducing spending in training and skills in light of COVID-19, leaders are optimizing their training budgets to maximize value from their existing investments.
Before the pandemic, nutritional food company, Huel, prioritized data training and drop-in sessions to upskill employees. This foundation is helping the company adapt to a new business reality. Since many employees understand how to explore data and turn it into insights, they can act with greater speed and clarity on decisions around marketing spend and distribution effectiveness.
In this new world, we’ll see virtual data communities emerge as the preferred method of regular communication among analysts, business users, and line of business leaders. Organizations that already had virtual communities will see them grow as workers need a place to offer inspiration, ask questions, and share best practices in a new remote world. This will set the foundation for more expansive in-person and online training programs in the future.
Early 2020 Prediction: Transparency around workplace data leads to equity and organizational success
How It’s Evolved: Data sheds light on inequality and areas where progress is needed
Previously, we discussed data as a tool instrumental in dismantling inequities in the workplace—but even more, we are seeing data being used to shine a light on inequities in the world at large in 2020. The novel coronavirus has had a disproportionate impact on communities of color and to understand the breadth of the impact and to spearhead solutions, we need data. Data from Kaiser Family Foundation shows that as of August 4, the COVID-19 related death rate among Black people was over twice as high as the rate for White people. People from Latinx communities are also seeing higher rates of infection and hospitalization.
Data can be a key tool in building awareness and inspiring action in the fight against inequality. Headwaters Economics, an independent, nonprofit research group, developed a series of data visualizations that show Census response rates for four minority groups: Black, Asian, Hispanic and Latino, and Native American. The decennial Census determines how much federal funding flows into communities and influences decisions about schools, health clinics, and development programs—but Covid-19 is affecting response rates due to health risks, unemployment, and limited door-to-door outreach.
Headwaters pulled in data on Census self-reporting from the U.S. Census Bureau (updated daily), and overlaid it with demographic data on the racial and ethnic makeup of communities across the U.S. Explore the full visualization.
Patty Hernandez Gude, associate director at Headwaters Economics noted: “All of this adds up to a situation where communities of color stand to be represented even less in the 2020 Census than they have been historically. This would be a monumental step backwards.”
Headwaters is working to get these visualizations into the hands of advocates and nonprofits to improve Census response rates. Working with limited resources, these visualizations help them quickly identify how to make the largest impact. “Hopefully the data can serve a purpose and be used to more effectively direct energy and resources in this critical period of time when it can really make a difference,” shared Gude.
Early 2020 Prediction: Data strategy stretches across the C-suite
How It’s Evolved: Executives extend data-driven decision making to frontline workers
Digital transformation efforts, particularly in the realm of data and analytics, were once the sole responsibility of the Chief Data Officer. But that paradigm is changing, especially in light of Covid-19, as data and analytics is more tightly woven into business goals.
All executives—not just analytics leaders—at the C-level and VP-level are committing to treat data and analytics as a shared responsibility, and functional leaders are expected to empower their employees with the data and the skills they need to do their jobs. McKinsey recently shared six key lessons that have emerged from crisis-response efforts. One of these was the need for frontline teams to have full decision-making rights.
Chemical and consumer goods company, Henkel Laundry and Home Care leverages self-service analytics in their operations and supply chain. Henkel’s frontline workers—ranging from analysts to global managers to factory line operators—all have access to data to track major KPIs like energy efficiency in factories. When some managers had to shift to remote work, the team moved all major KPIs into online dashboards, so they can conduct operations planning and track the availability of personal protective equipment (PPE) in facilities in order to keep employees safe. Dr. Johannes Holtbruegge, Senior Manager of Transformation at Henkel, noted that the ability to track metrics at a local, regional, and global level creates strong alignment between disciplines and increases agility. All these developments are embedded in a long-term digitalization strategy of the company.
Organizations get more value out of analytics when they bring in the people who know the data best—the people that make and execute decisions based on business goals. When these people have the data they need, along with the necessary skills to interpret and act on the data, organizations build a strong data culture and as a result, stronger networks of teams.
If we’ve learned anything during this new normal, it’s that predicting what’s next can be incredibly difficult. But we know that the one constant, reliable way forward is with data.
Data is playing an important role in identifying ways we can improve our communities, our businesses, and our world. Organizations using trusted data are well-positioned for navigating through change and setting themselves up for success in the future. Data empowers people to make better decisions, faster, and that has been one of the main differentiators we’ve seen in organizations that are surviving and even finding new and innovative ways of getting work done during this pandemic.
Data will continue to be an even more important component to finding stability and growth, especially as more operations and services move into the digital space. The potential impact of that data will only get stronger as increased automation, AI, and forecasting models help us better predict and prepare for what’s ahead. Even in a crisis, those who have taken the initiative to shift to a digital-first mindset, driven by data, are better prepared to handle whatever comes next.
Andrew Beers is Tableau’s Chief Technology Officer, and is responsible for Tableau’s long-term technology roadmap and emerging technologies. During his tenure at Tableau, he has led many of the engineering teams, created new products for the company, and personally written pieces of the product code. Andrew has been at the very heart of Tableau’s engineering for most of the company’s existence. Prior to joining Tableau in 2004, Andrew ran the engineering group at Align Technology, makers of the Invisalign system, building software to support large-scale customized manufacturing. He holds a master’s degree in computer science from Stanford University, where he worked in Pat Hanrahan’s (Tableau co-founder) computer graphics research group.
Arizona State University 63.2K subscribers In this first episode of Study Hall: Data Literacy, Jessica Pucci talks us through some of the critical vocabulary we’ll need to become great Data Analysts. And, she lays out the basic ideas behind what it means to be Data Literate and how we can start looking at information and the world a little differently.
Big data as a concept is thrown around a lot. It’s often used as a buzzword to sound tech-savvy and on the ball — but how much do you really know about it? In truth, it’s been around for decades. Businesses have been analyzing their customers’ actions and behaviors and using it to inform their business decisions for a long time; it’s the marker of a strong businessperson. The difference today is that we now have the tools and technology to gather and analyze larger amounts of data faster. Enter big data.
You don’t have to be a tech genius or a data scientist to make big data work for you and your business. Here are seven areas where you can use big data to streamline and optimize what you already have, with key examples and actionable tips to get you started.
1. Website design
To prove that big data is not only for the scientists of the world, let’s start with a more creative example. A well-designed website shouldn’t only look good, it should be part of a subtle conversation going on between you and your customers and leads.
One way to gather useful big data from your website is through heat maps. You can see exactly where the eyes and cursors of visitors to your site spent the most time. If these heat spots aren’t on your CTA button, contact form, or wherever else you most want them to go, you know what needs to be changed. You can achieve similar results with traffic analysis — looking at page views, unique visitors, visit duration and more. Many traffic analysis tools will also let you compare to your competitors’ sites to get a bigger picture of the general landscape.
2. Campaign timing
Have you ever put together a five-star marketing campaign that ticks all of your customer persona boxes, looks great and has a punchy CTA — only to see it flop? The greatest campaigns in the world will get you nowhere if you don’t publish them at the right time.
Whether you’re publishing on social media, email or any other digital platform, there are tools (like Growbots for email or Sprout Social for social media) that will gather data for you about when your audience is most active, when they are most prone to engaging and ultimately when the best time to reach them is. With big data, you don’t have to take a stab in the dark about when to launch a winning campaign.
3. Conversion optimization
There are a lot of variables when it comes to on-site content. While that may seem daunting to some, that really just means that there’s a lot of room for optimization so that your business can do even better than it is now. From headline copy to page color scheme, it can all be tweaked and improved to gather the highest amount of conversions possible.
Big data analytics can help us to understand how leads travel through our sales funnels, where they might get lost and at what point many prospective customers drop off. Data-driven optimization is the fastest and most efficient way to get it right. Even while experimenting, be sure to gather as much data as possible and analyze it in bulk for the most accurate and informative results.
No matter what your business is, at the end of the day it will come down to people making a decision. Big data might seem like a huge and faceless tool, but it can also be used to add more personality and individuality to your marketing and customer interaction.
The fashion brand, H&M, used big data to do exactly that when they integrated it with their chatbot. As it offered options to prospective customers and asked them if they liked the product choice, it learned more and more about what clothing options they liked. Along the same vein, for marketers to make personalized decisions that will have a real impact on leads and customers, we need to learn about them first. Big data is one effective way to do so.
5. Customer retention
A good business person knows how to attract and win clients. A great business person knows how to keep loyal customers. Once again, big data can take the heavy lifting out of this process.
Checking in with your existing customers through quick surveys and polls is one way to be continually staying in touch with how they perceive your company and what they think of your products or services. It’s anywhere between five to 25 times more expensive to find a new customer than to retain an existing one, depending on your industry. Use big data to regularly make sure that you are doing exactly what your existing customers are expecting of you.
6. Informing risk management
Risk is a fact of life for any business. Wouldn’t it be great if we could find a way to make smarter strategic decisions with key data to back up our more risky ventures? With big data, that could be a reality.
UOB Bank in Singapore did it. As a financial institution, making a misstep in risk assessment and management could be catastrophic. The bank used big data to develop a risk management system that cut down their risk analysis time from 18 hours to just a few minutes. Being able to carry out extensive risk analysis in real-time was a game-changer.
Of course, not every business has the ability or the resources to create their own risk management solution from scratch but there are tools out there that help businesses accurately quantify the risks they take on a daily basis, shedding light on one of the trickiest parts of business decision-making.
Think about the most successful businesses in the world — Amazon, Apple and Microsoft, just to name a few. They didn’t get to where they are today by sticking to their first idea and running with it. They diversified, innovated and kept up with other demands from their customers. Often, it was big data that showed them the way.
Let’s look at Amazon’s recent venture, Amazon Fresh. To launch their whole foods service, Amazon focused on big data analytics to not just understand how customers buy groceries, but also how suppliers interact with grocers. Big data helped them understand the whole supply chain and find a solution that streamlined every aspect of it, thereby providing an innovative and helpful service.
By: Sina Fak / Entrepreneur Leadership Network Writer