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By the end of 2023, some 68% of the world’s population was vaccinated against a deadly pandemic. These lifesaving Covid-19 vaccines were possible only because of data-driven industrial collaboration of an unprecedented global scale and speed.
The rapid and effective development of the coronavirus vaccines has set a new benchmark for today’s industries – but it is not the only one. Increasingly, savvy enterprises are starting to share industrial data strategically and securely beyond their own four walls, to collaborate with partners, suppliers and even customers.
Together, they are uncovering new synergies. They are reducing waste and sparking new opportunities. And because so many industrial organizations help deliver life’s essentials— medicines, water, food, energy, infrastructure and more—their collaboration has the potential to solve not only industrial challenges but also human ones: from disease and hunger to climate change.
We believe this opportunity promises to transform how industries operate in the coming decades. Imagine a completely connected world, where industrial teams are able to collaborate using integrated data from diverse sources—not only with colleagues, but also with suppliers, partners and even customers.
According to Leif Eriksen, research vice president, Future of Operations at IDC, “Data-driven operations is a journey, but this should not be interpreted as a reason to be complacent. The pace of change in operations is beginning to accelerate and will result in significant realignments across a range of industries. Organizations that recognize the opportunity will thrive; those that fail to see it will not survive.”1
In this world, you can connect people in real time, transforming complex value chains into agile, sustainable growth networks. At AVEVA we call this data-driven networked innovation, the connected industrial economy.Networked innovation to tackle humanity’s greatest problems
Consider global warming. Here again, the connected industrial economy offers the potential to accelerate effective, collaborative action.
We see that power companies are using strategic data sharing to support customers’ transition to renewable energy. Cities are using it to reduce emissions, traffic and pollution. Manufacturers are tapping it to drive down resource consumption (and even meeting duration).
By enabling the easy transfer of trusted information and insights, these businesses are facilitating innovative “co-operation in an increasingly fragmented world” – which is the theme of the World Economic Forum’s annual meeting in Davos this year.
Worldwide, almost nine out of 10 (87%) business executives at larger industrial companies cite a need for the type of connected data that delivers unique insights to address challenges such as economic uncertainty, unstable geo-political environments, historic labour shortages, and disrupted supply chains. In fact, executives report in a global study that the most common benefits of having an open and agnostic information-sharing ecosystem are greater efficiency and innovation (48%), increasing employee satisfaction (45%), and staying competitive with other companies (44%).Shopfloor to top-floor oversight for efficiency and energy gains
Integrating previously siloed equipment and supply chain information can also improve product quality, operational efficiency and even customer delight.
Schneider Electric’s World Economic Forum lighthouse smart factory in Batam, Indonesia, uses Industry 4.0 technology to connect and aggregate data from the cloud for shopfloor to top-floor oversight of operational and asset performance. Immediate gains include increased efficiency through real-time performance tracking and digital escalation for quicker decision making.
The multinational saw downtime drop by 44%, while on-time delivery for customers grew 40%. Besides more intelligent use of resources, the facility was able to cut its energy use by a fifth.Two-way data sharing to support joint innovation
When real-time data is shared securely between organizations, such as with a principal and its service partner, the association can deliver much-needed innovation.
Automating real-time data sharing over a bidirectional highway in the cloud now enables Allied Reliability to analyze centrifuge operations on the go. REG, in turn, is able to reduce equipment downtime by up to 90%, ensuring peak performance of equipment and leading to reduced emissions.Share energy source data to reduce greenhouse gas emissions
Providing real-time information on power generation can reduce greenhouse gas emissions and help achieve net-zero commitments.
US-based Dominion Energy leverages data connectivity to support the sustainability goals of its utility customers. Using a cloud-based data platform, the power and energy leader shares information about its green energy mix with customers, reassuring them of their own low-carbon energy consumption and in turn enabling them to share that proof with auditors and investors.Digital solutions for sustainable growth in the connected industrial world
The industrial world is at an inflection point as two incredibly disruptive revolutions are underway simultaneously.
On the one hand, innovative companies are looking to maximize the digital value and manage systemic disruption. On the other, regulatory, financial and customer pressure is driving businesses to rethink their operations with environmental goals at the heart of their value chains. All this is playing out in a dynamic and increasingly fragmented global economy.
The path to driving sustainable value through digital thinking is clearer than ever before. Are we ready to shift our mindsets?
The World Economic Forum (WEF) Annual Meeting 2023 starts in Davos on Monday. AVEVA CEO Peter Herweck will be speaking at the event in Switzerland to promote a sustainable industrial digital transformation in support of a lower carbon and socially just world.
IDC FutureScape: Worldwide Future of Operations 2023 Predictions, October 2023 (ref: #US48669222)
You're reading Our Connected Future: How Industrial Data Sharing Can Unite A Fragmented World
Cybersecurity is in the news, but the risks posed by weak and outdated security measures are hardly new. For more than two decades, organizations have struggled to keep pace with rapidly evolving attack technologies.
Connectivity Creates Opportunities and Challenges
Emerging technologies, particularly the Internet of Things (IoT), are taking global connectivity to a new level, opening fresh and compelling opportunities for both adopters and, unfortunately, attackers.
IoT poses a significant new challenge, Al-Abdulla observes. “As new devices are connected, they represent both a potential ingress point for an attacker as well as another set of devices that have to be managed,” he says. “Unfortunately, most of the world is trying to achieve the promise provided by IoT projects as rapidly as possible, and they are not including security in the original design, which creates greater weakness that is very, very hard to get back after the fact and correct.”
Al-Abdulla also notes that many organizations are unintentionally raising their security risk by neglecting routine network security tasks. “Every time our assessment team looks at the inside of a network, we find systems that haven’t been patched in 10 years,” he says. “Sometimes, it’s IoT devices.”
Al-Abdulla’s team has observed devices with “a flavor of Linux or Windows embedded” that have not been updated since they left the factory. Security cameras, badge readers, medical devices, thermostats and a variety of other connected technologies all create potential attack gateways.
“It’s a very complicated world that we live in right now, because the attacker and defense problem is highly asymmetrical,” Roesch adds.
The Human Factor
While following security best practices is essential to network security, many organizations remain unaware of, or pay little attention to, the weakest link in the security chain: people.
Weighing Risk Against Benefits
Security boils down to measuring risk against anticipated benefits. “One of the fascinating things about risk is that low-level engineers know where the risks are, but they don’t necessarily tell anybody,” Waters says. As an example, he cites Operation Market Garden, a World War II Allied military effort (documented in the book and movie A Bridge Too Far) that was fatally hampered by poor radio communication. “People knew those radios weren’t going to work when they got over there,” Waters says. “They didn’t tell anybody because they didn’t want to rock the boat.”
Once a risk is identified, users and IT professionals must be committed to addressing it, with the support of executives. Across all departments and in all situations, calm person-to-person communication is always a reliable and effective security tool. “If we’re running around with our hair on fire all the time, they don’t want to talk to us,” Waters adds. “We want everybody to be able to talk with us and share their risks, so we know to prioritize and trust them.”
Too much caution blocks or degrades benefits, particularly when security mandates unnecessarily interfere with routine activities. Simply telling people what not to do is rarely effective, particularly if what they’re doing saves time and produces positive results. “We talk about Dropbox and things like that,” Waters says. “If your policies are too restrictive, people will find a way around them.”
The Danger of Giving in to Ransomware
Ransomware is like a thug with a gun: “Pay up, or your data gets it!”
Problem solved? Not necessarily, says Michael Viscuso, co-founder and chief technology officer of endpoint security provider Carbon Black, who sees no easy way out of a ransomware attack. “It’s still surprising to me that people who have paid the ransom think that the game is over,” he says. “The reality is that the attacker has access to your system and is encrypting and decrypting your files whenever he wants to – and charging you every time.”
James Lyne, global head of security research at security technology company Sophos, notes that many ransomware attackers hide code within decrypted data, allowing them to reinfect the host at a future date. “Because if you’ll pay once, you’ll pay twice,” he explains.
Effective backups: IT staff can save themselves trouble and money by implementing regular backup practices to an external location such as a backup service. In the event of a ransomware infection, backup data can get organizations back on their feet quickly.
Deployment of security solutions: Measures such as anti-malware, firewalls and email filters can help detect ransomware and prevent infections.
In much the same way that organizations boost their results through ambition and innovation, cybercriminals also are improving the way they operate. “The bad guys are entrepreneurial,” says Martin Roesch, vice president and chief architect of the Cisco Security Business Group.
Most successful cybercriminals are part of large and well-structured technology organizations. “There’s a team of people setting up infrastructure and hosting facilities; there’s a team of people doing vulnerability research; there’s a team of people doing extraction of data; there’s a team of people building ransomware; there’s a team of people delivering ransomware; there’s a team of people doing vulnerability assessment on the internet; there’s a team of people figuring out how to bypass spam filters,” says Michael Viscuso, co-founder and CTO of Carbon Black.
Roesch says organizations have found it “very difficult to respond and be effective against the kind of threat environment that we face today,” but says security experts within Cisco have specifically targeted cybercrime organizations and achieved some success in shutting them down.
Question: I wondered whether you might be able to point me in the direction of some info on web behaviour?
We’ve recently been looking at “drop off” rates for some of our online content and seeing if we can compare it with external sites to help gauge whether the behaviours we are seeing could be considered as “typical”.
The metrics we’ve been looking at include time spent on a page (0-10, 10-20 seconds etc) and would like to compare to external websites such as BBC, YouTube, etc.
Do you know how it is possible to obtain this data, or any external websites who may have it?
We measure it, but have no external reference as to whether it is good, bad or indifferent.
Smart Insights Expert Answer: Yours is a familiar problem for all analytics users. We hear “What does good like”? “How do we compare”? questions often. So, you need some context, otherwise the answer is always “it depends”.Metrics for evaluating initial engagement with a site
In analytics tools, the main measures for initial engagement with a site are:
2. Duration or dwell time. measured as Average Time on Page or Average time on site
3. Pages per visit. Pages viewed divided by the number of visits.
Which is the best to use to review performance of sources of traffic or the ability of a site to engage will depend on your goal. I personally find bounce rate most actionable for seeing whether your providing a good, relevant, experience. So with search marketing you can compare bounce for different keywords entering the site on different landing pages and see which are underperforming compared to others. So benchmark against your own site average – Google Analytics has a good chart for for this.
You might think that duration would be the opposite of bounce, but average time on page can be a good indication for engagement with an article like a blog post or article.
Pages per visit is better for reviewing overall engagement with content on a community site and there are some good sites on thisBenchmarks for engagement
Finally, to the main part of your question, where do we find benchmarks for these?
1. Bounce rate.
Jan 2012 Update: Google published a compilation of bounce rates by source in summer 2011 in their Analytics Enewsletter – Malcolm Coles did a summary of how bounce rates vary by source and country.
Published bounce rates aren’t widely available, but if you use Google Analytics, one option was to enable the benchmarking facility to compare to a similar sized site in your sector. Here I share ours for Smart Insights:
See the last section of this post about the danger of averages where I present some other examples of typical bounce rates.
3. Pages per visit. You can see that by dividing Page Views by Visits we can also calculate this measure; around 5.4 in this case. You can also benchmark out of sector. For example, if your site has a community section – how does it compare to Facebook which has a typical pages per visit value of 30.
So I hope that answers your question Nicola. Clearly these engagement measures aren’t going to differ radically from week to week, there more useful for benchmarking before and after major site design or content changes. But don’t forget…The danger of averages – you HAVE to segment for meaningful engagement
To make these engagement measures more meaningful you have to segment – these are our recommendations on the main segmentation options for online marketing and analytics. Of these, the most important ones to test and action for bounce rates are:
1. New vs returning visitors
2. Brand search (they know you – bounce rate can be less than 10%) against non-brand search (they don’t know you – bounce rate can be higher than 80% if you’re not credible)
3. Different media channels – bounce rates from Email campaigns tend to be lower
4. Customer and non-customers (if you’re using custom variables)
5. Entrance page – home page entrances tend to be lower than landing pages for example.
I would look at average bounce rates for all of these – they give you a starting point to improve on and you can focus initially on the keyphrases or pages with a high volume that don’t engage. That’s the power of bounce rate – it will vary dramatically across your referrers and pages so is highly actionable.
You may also be interested in this compilation of average bounce rates for a range of mainly UK sites tracked with the Analytics SEO software. The average bounce rate is 48% across these sites.
Today’s world is hyper-connected, and data is a currency arguably more valuable than any other. By 2023, there will be 27B networked devices in use worldwide, a quarter of which will be smartphones. Integrated within each of those devices is the power of location data, which is enhancing many of the technologies that consumers have come to rely on. Consumers today expect their digital experiences to be not only personalized for their tastes and habits but also tailored based on context – where they’ve been and where they are in the world at any given moment. Understanding, analyzing and leveraging location-based data enable businesses to provide highly personalized experiences that consumers have come to expect. Here are three examples of how location data can help businesses become more competitive.Rideshare services
Rideshare is a rising mode of transportation, with companies like Lyft, Uber, Bird, Lime and many others gaining market share in cities worldwide. , user penetration is at 17.8% in 2023 and is expected to hit 23.1% by 2023. Rideshare services rely on two types of data in order to deliver a smooth user experience. The first is accurate point of interest (POI) data, like businesses, restaurants, hotels, tourist locations, and many more. When a rider inputs a destination, they want the app to know exactly where they are and where they want to go, without having to identify individual addresses. Quality POI data, including numerous place attributes, makes that possible. Second is good predictive analytics data, which ensures that rides (or modes of transportation) are available where and when users demand it. What good is a dockless scooter if there are none around when you need to pick one up for a ride to work? In order to provide a good user experience, rideshare apps need to supply vehicles based on forecasted demand – a problem that location data uniquely solves. By understanding human traffic patterns, identifying popular pickup/drop off points, popular times of day and day of the week, rideshare companies can ensure they meet the demands of their consumers.Competitive retail analytics
Though 96 percent of Americans browse online, the majority (65 percent) of the average shopper’s budget is still spent in the store. What’s more, don’t make a purchase online because they can’t see, touch and feel the product. Brick and mortar locations are still incredibly important to retailers, and the placement of their stores is critical in the competitive shopping landscape. Sometimes a store visit will be based on convenience – close to work, home, or other frequented areas. In other instances, shoppers may travel a great distance for a store that’s important to them. Location data can uncover these insights, and help retailers in better understanding who is visiting their store, when, and compare foot traffic to competitive stores.Site Selection
Understanding nearby building density and business categories, foot traffic patterns and competitor locations is an important factor in making smart, data-driven decisions when selecting a new location for any brick and mortar asset.Factual Data
Not all data are created equal. Quantity does not equal quality, and data is constantly changing as the real-world evolves. Stores open and close. People move continuously. Device lifecycles become increasingly short. Curating massive amounts of continuously changing data is no small feat. High-quality data is essential in giving businesses a competitive edge, and location data is among the most powerful available. That’s why Factual offers high-quality places and people movement data to businesses worldwide. Factual’s Global Places data includes more than 130MM points of interest (POIs) across 52 countries in 29 languages, and its Observation Graph delivers movement data from more than 650MM verified devices worldwide.
How Big Data Can Secure User Authentication
A combination of traditional password and multi-factor authentication system is being given to users, but multi-factor authentication system is optional because many users find it inconvenient.
Two-factor and multi-factor authentication have limited user acceptance.
Many companies have started using user’s fingerprints, voice and face recognition to authenticate the user.
Big data builds user profile without the user knowing about it. The profile is regularly updated and used to authenticate the user.Various Working Processes
Irrespective of various new authentication systems coming up, the main system remains the same, that is, matching user inputs with the available data in the system. The different authentication systems are described below:
Password-based system: The password inputted by the user is usually matched with the encrypted one stored in the database.
Multi-factor system: The system matches multiple passwords with the inputs provided during the access request. Some of them are stored in the database and the remaining are dynamically generated.
Biometric system: The system collects data from a person’s voice, fingerprints or iris and uses that data to authenticate the user. The iris is a thin and circular structure in the eye. It is responsible for controlling the diameter and size of the pupil and thus enables the amount of light reaching the retina. Eye color is defined by that of the iris.
Big-data-based system: The system creates a profile of the user based on the data it regularly collects. It authenticates access requests by matching access inputs with the data in the profile. Any mismatch or deviation from the profile could set off a warning about unauthorized attempts.
However, organizations have been facing some problems:
Financial and technical challenges in moving from purely password-based systems to more secure authentication systems.
The users prefer to avoid layered authentication if given an option.
The user uses a physical keyboard or a virtual keyboard?
The level of security permissions does the user have?
Number of attempts, user takes to enter the correct password?
An average number of system access, a user makes in a day?
Number of times, the password has been reset?
Organizations have been reaping benefits of this approach already. The data authentication system establishes whether the identity presenting a claim is real, and then verifies whether the identification is owned by the person making the claim.Conclusion:
Many organizations have been watching the developments with both interest and caution, especially those deals with a lot of confidential data, like, banking and finance, defense and healthcare.Quick Reaction:
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As buzzwords go, big data is currently one of the most powerful—and one of the most perplexing. It sounds like something only multinational conglomerates can afford. But the concept of analyzing very large amounts of data and looking within it for patterns, trends, and insights is one that nearly any business, large or small, can use to help make better decisions.The big data you might not know you have
The key word in “big data” is big: The more data you have, the better it works. Want to gain insights into your finances? A few hundred sales records might tell you something, but a few million will turn up more trends and help you gain a deeper understanding of where your money is coming from. Want to get a bird’s-eye view of your customer base? Thousands of customers and prospects fed into a big-data tool will be more valuable than an analysis of, say, your top ten buyers.How small companies are profiting from big data
The goal of any big-data effort is to improve your business. If you’ve traveled through a major airport lately, for instance, you’ve probably seen Vino Volo, a small chain of wine bars that can now be found in 28 airports. Vino Volo is using big data in the form of a mobile app developed by Punchh, which works as a loyalty program and referrals system. Punchh co-founder Sastry Penumarthy says Punchh “crunches lots of real-time data from mobile, social, and POS to automatically provide brands (in real-time) 360-degree insights about their customers and stores, including visits by location and time of day, orders including specific menu items, reviews and sentiments of reviews, and campaign response rates,” among other insights.
Riviera Partners uses big data for recruiting. The job placement company keeps a huge database of potential candidates that is constantly being updated. Searching this database for the right candidate involves not merely searching for keywords on resumes but by aggregating its own data on a candidate and cross-referencing it with public information (like LinkedIn profiles). Candidates are then scored based on all of these factors on a job-by-job basis before being further vetted and presented to the client.How can your small business use big data?
If you’re a typical small business, just crunching along from day to day without any real strategic direction, the ability to finally get your arms around your business by digging into the data you already have probably sounds enticing. But big-data service providers don’t make it easy. There are literally hundreds of companies out there, all of which promise to open your eyes to your company’s future by “harnessing big data.”
These companies can be wildly dissimilar. For example, Tranzlogic provides a Web portal for merchants where they can track sales, how various locations are performing, and whether promotions are paying off. It uses “big data” analysis of your credit card transaction data to do this. Or consider MaxxCAT, which makes a network appliance and accompanying software to pluck data from your internal servers and hook those results into processing systems. It’s also a big-data service, but the two companies couldn’t be more different.
Knowing what kind of big data service to work with depends on the type of data you’re looking to analyze.
Big-data companies vary widely in scope and scale. This overview will help you understand the types of companies out there. Some of them are large-scale providers that can analyze data from a wide variety of sources. Others work in extremely narrow niches. Again, choosing a big-data partner depends entirely on the data. There’s no sense in signing with a provider that specializes in slicing and dicing Salesforce databases if you don’t use that system.
InsightSquared: This service is designed to analyze sales and the selling process, with a distinct focus on hooking into Salesforce and similar apps to examine your CRM database. You can further refine this by adding in data from QuickBooks, Zendesk, Google Analytics, and other sources. InsightSquared provides sales forecasts, a pipeline visualization, a marketing cycle report, and more. Pricing starts at $99 per month.
Canopy Labs: Canopy is designed to predict customer behavior and sales trends, offering a variety of scenarios for the future that you can use to help guide marketing and promotional efforts. (For example: Should you target loyal customers or try to bring back those who haven’t shopped with you for a while?) Supports Constant Contact, Salesforce, MailChimp, and more. Pricing ranges from free (up to 5,000 customers) to $250 per month (up to 100,000 customers).
Radius: A big-data tool primarily used to help identify sales targets and aid with lead generation, especially for businesses working with a large number of prospects. A big focus is correcting outdated customer information, so sales reps don’t go calling on shuttered businesses. The company says it aggregates data from more than 30,000 sources. Pricing is $99 per user per month.
Qualtrics: Big data comes to customer surveys, such as those “Tell us how we can do better” pop-ups you get at the end of a Web browsing session. Insights driven by Qualtrics can help with product and market research, ad testing, and even performance evaluations at the office. Pricing varies.
Qualtrics online survey platform helps with everything from product research to performance evaluation.
However, if your business decides to embrace big data, it doesn’t have to mean making a huge commitment to a service provider—contractually or financially. Identify one problem area—sales, finances, Web performance, etc.—and start mining your data for insights. In no time, you’ll be turning big data into big opportunity.
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