As we exited the isolation economy last year, we introduced supercloud as a term to describe something new that was happening in the world of cloud computing.
In this Breaking Analysis, we address the ten most frequently asked questions we get on supercloud. Today we’ll address the following frequently asked questions:
1. In an industry full of hype and buzzwords, why does anyone need a new term?
2. Aren’t hyperscalers building out superclouds? We’ll try to answer why the term supercloud connotes something different from a hyperscale cloud.
3. We’ll talk about the problems superclouds solve.
4. We’ll further define the critical aspects of a supercloud architecture.
5. We often get asked: Isn’t this just multicloud? Well, we don’t think so and we’ll explain why.
6. In an earlier episode we introduced the notion of superPaaS – well, isn’t a plain vanilla PaaS already a superPaaS? Again – we don’t think so and we’ll explain why.
7. Who will actually build (and who are the players currently building) superclouds?
8. What workloads and services will run on superclouds?
9. What are some examples of supercloud?
10. Finally, we’ll answer what you can expect next on supercloud from SiliconANGLE and theCUBE.
Late last year, ahead of Amazon Web Services Inc.’s re:Invent conference, we were inspired by a post from Jerry Chen called Castles in the Cloud. In that blog he introduced the idea that there were submarkets emerging in cloud that presented opportunities for investors and entrepreneurs, that the big cloud vendors weren’t going to suck all the value out of the industry. And so we introduced this notion of supercloud to describe what we saw as a value layer emerging above the hyperscalers’ “capex gift.”
It turns out that we weren’t the only ones using the term, as both Cornell and MIT have used the phrase in somewhat similar but different contexts.
The point is something new was happening in the AWS and other ecosystems. It was more than infrastructure as a service and platform as a service and wasn’t just software as a service running in the cloud.
It was a new architecture that integrates infrastructure, unique platform attributes and software to solve new problems that the cloud vendors in our view weren’t addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud.
In addition, we felt this trend pointed to structural change going on at the industry level that supercloud metaphorically was highlighting.
So that’s the background on why we felt a new catchphrase was warranted. Love it or hate it… it’s memorable.
To that last point about structural industry transformation: Andy Rappaport is sometimes credited with identifying the shift from the vertically integrated mainframe era to the horizontally fragmented personal computer- and microprocessor-based era in his Harvard Business Review article from 1991.
In fact, it was actually David Moschella, an International Data Corp. senior vice president at the time, who introduced the concept in 1987, a full four years before Rappaport’s article was published. Moschella, along with IDC’s head of research Will Zachmann, saw that it was clear Intel Corp., Microsoft Corp., Seagate Technology and other would replace the system vendors’ dominance.
In fact, Zachmann accurately predicted in the late 1980s the demise of IBM, well ahead of its epic downfall when the company lost approximately 75% of its value. At an IDC Briefing Session (now called Directions), Moschella put forth a graphic that looked similar to the first two concepts on the chart below.
We don’t have to review the shift from IBM as the epicenter of the industry to Wintel – that’s well-understood.
What isn’t as widely discussed is a structural concept Moschella put out in 2018 in his book “Seeing Digital,” which introduced the idea of the Matrix shown on the righthand side of this chart. Moschella posited that a new digital platform of services was emerging built on top of the internet, hyperscale clouds and other intelligent technologies that would define the next era of computing.
He used the term matrix because the conceptual depiction included horizontal technology rows, like the cloud… but for the first time included connected industry columns. Moschella pointed out that historically, industry verticals had a closed value chain or stack of research and development, production, distribution, etc., and that expertise in that specific vertical was critical to success. But now, because of digital and data, for the first time, companies were able to jump industries and compete using data. Amazon in content, payments and groceries… Apple in payments and content… and so forth. Data was now the unifying enabler and this marked a changing structure of the technology landscape.
Listen to David Moschella explain the Matrix and its implications on a new generation of leadership in tech.
So the term supercloud is meant to imply more than running in hyperscale clouds. Rather, it’s a new type of digital platform comprising a combination of multiple technologies – enabled by cloud scale – with new industry participants from financial services, healthcare, manufacturing, energy, media and virtually all industries. Think of it as kind of an extension of “every company is a software company.”
Basically, thanks to the cloud, every company in every industry now has the opportunity to build their own supercloud. We’ll come back to that.
Let’s address what’s different about superclouds relative to hyperscale clouds.
This one’s pretty straightforward and obvious. Hyperscale clouds are walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their cloud, so they’re meeting customers where their data lives with initiatives such Amazon Outposts and Azure Arc and Google Anthos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, costs and performance they can deliver. The more complex the environment, the more difficult to deliver on their promises and the less margin left for them to capture.
Will the hyperscalers get more serious about cross cloud services? Maybe, but they have plenty of work to do within their own clouds. And today at least they appear to be providing the tools that will enable others to build superclouds on top of their platforms. That said, we never say never when it comes to companies such as AWS. And for sure we see AWS delivering more integrated digital services such as Amazon Connect to solve problems in a specific domain, call centers in this case.
We’ve all seen the stats from IDC or Gartner or whomever that customers on average use more than one cloud. And we know these clouds operate in disconnected silos for the most part. That’s a problem because each cloud requires different skills. The development environment is different, as is the operating environment, with different APIs and primitives and management tools that are optimized for each respective hyperscale cloud. Their functions and value props don’t extend to their competitors’ clouds. Why would they?
As a result, there’s friction when moving between different clouds. It’s hard to share data, move work, secure and govern data, and enforce organizational policies and edicts across clouds.
Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations and share data safely irrespective of location.
Pretty straightforward, but nontrivial, which is why we often ask company chief executives and execs if stock buybacks and dividends will yield as much return as building out superclouds that solve really specific problems and create differentiable value for their firms.
Let’s dig in a bit more to the architectural aspects of supercloud. In other words… what are the salient attributes that define supercloud?
First, a supercloud runs a set of specific services, designed to solve a unique problem. Superclouds offer seamless, consumption-based services across multiple distributed clouds.
Supercloud leverages the underlying cloud-native tooling of a hyperscale cloud but it’s optimized for a specific objective that aligns with the problem it’s solving. For example, it may be optimized for cost or low latency or sharing data or governance or security or higher performance networking. But the point is, the collection of services delivered is focused on unique value that isn’t being delivered by the hyperscalers across clouds.
A supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud and using its own specific platform-as-a-service tooling, creates a common experience across clouds for developers and users. In other words, the superPaaS ensures that the developer and user experience is identical, irrespective of which cloud or location is running the workload.
And it does so in an efficient manner, meaning it has the metadata knowledge and management that can optimize for latency, bandwidth, recovery, data sovereignty or whatever unique value the supercloud is delivering for the specific use cases in the domain.
A supercloud comprises a superPaaS capability that allows ecosystem partners to add incremental value on top of the supercloud platform to fill gaps, accelerate features and innovate. A superPaaS can use open tooling but applies those development tools to create a unique and specific experience supporting the design objectives of the supercloud.
Supercloud services can be infrastructure-related, application services, data services, security services, users services, etc., designed and packaged to bring unique value to customers… again that the hyperscalers are not delivering across clouds or on-premises.
Finally, these attributes are highly automated where possible. Superclouds take a page from hyperscalers in terms of minimizing human intervention wherever possible, applying automation to the specific problem they’re solving.
What we’d say to that is: Perhaps, but not really. Call it multicloud 2.0 if you want to invoke a commonly used format. But as Dell’s Chuck Whitten proclaimed, multicloud by design is different than multicloud by default.
What he means is that, to date, multicloud has largely been a symptom of multivendor… or of M&A. And when you look at most so-called multicloud implementations, you see things like an on-prem stack wrapped in a container and hosted on a specific cloud.
Or increasingly a technology vendor has done the work of building a cloud-native version of its stack and running it on a specific cloud… but historically it has been a unique experience within each cloud with no connection between the cloud silos. And certainly not a common developer experience with metadata management across clouds.
Supercloud sets out to build incremental value across clouds and above hyperscale capex that goes beyond cloud compatibility within each cloud. So if you want to call it multicloud 2.0, that’s fine.
We choose to call it supercloud.
Well, we’d say no. That supercloud and its corresponding superPaaS layer gives the freedom to store, process, manage, secure and connect islands of data across a continuum with a common developer experience across clouds.
Importantly, the sets of services are designed to support the supercloud’s objectives – e.g., data sharing or data protection or storage and retrieval or cost optimization or ultra-low latency, etc. In other words, the services offered are specific to that supercloud and will vary by each offering. OpenShift, for example, can be used to construct a superPaaS but in and of itself isn’t a superPaaS. It’s generic.
The point is that a supercloud and its inherent superPaaS will be optimized to solve specific problems such as low latency for distributed databases or fast backup and recovery and ransomware protection — highly specific use cases that the supercloud is designed to solve for.
SaaS as well is a subset of supercloud. Most SaaS platforms either run in their own cloud or have bits and pieces running in public clouds (e.g. analytics). But the cross-cloud services are few and far between or often nonexistent. We believe SaaS vendors must evolve and adopt supercloud to offer distributed solutions across cloud platforms and stretching out to the near and far edge.
Another question we often get is: Who has a supercloud and who is building a supercloud? Who are the contenders?
Well, most companies that consider themselves cloud players will, we believe, be building superclouds. Above is a common Enterprise Technology Research graphic we like to show with Net Score or spending momentum on the Y axis and Overlap or pervasiveness in the ETR surveys on the X axis. This is from the April survey of well over 1,000 chief executive officers and information technology buyers. And we’ve randomly chosen a number of players we think are in the supercloud mix and we’ve included the hyperscalers because they are the enablers.
We’ve added some of those nontraditional industry players we see building superclouds such as Capital One, Goldman Sachs and Walmart, in deference to Moschella’s observation about verticals. This goes back to every company being a software company. And rather than pattern-matching an outdated SaaS model we see a new industry structure emerging where software and data and tools specific to an industry will lead the next wave of innovation via the buildout of intelligent digital platforms.
We’ve talked a lot about Snowflake Inc.’s Data Cloud as an example of supercloud, as well as the momentum of Databricks Inc. (not shown above). VMware Inc. is clearly going after cross-cloud services. Basically every large company we see is either pursuing supercloud initiatives or thinking about it. Dell Technologies Inc., for example, showed Project Alpine at Dell Technologies World – that’s a supercloud in development. Snowflake introducing a new app dev capability based on its SuperPaaS (our term, of course, it doesn’t use the phrase), MongoDB Inc., Couchbase Inc., Nutanix Inc., Veeam Software, CrowdStrike Holdings Inc., Okta Inc. and Zscaler Inc. Even the likes of Cisco Systems Inc. and Hewlett Packard Enterprise Co., in our view, will be building superclouds.
Although ironically, as an aside, Fidelma Russo, HPE’s chief technology officer, said on theCUBE she wasn’t a fan of cloaking mechanisms. But when we spoke to HPE’s head of storage services, Omer Asad, we felt his team is clearly headed in a direction that we would consider supercloud. It could be semantics or it could be that parts of HPE are in a better position to execute on supercloud. Storage is an obvious starting point. The same can be said of Dell.
Listen to Fidelma Russo explain her aversion to building a manager of managers.
And we’re seeing emerging companies like Aviatrix Systems Inc. (network performance), Starburst Data Inc. (self-service analytics for distributed data), Clumio Inc. (data protection – not supercloud today but working on it) and others building versions of superclouds that solve a specific problem for their customers. And we’ve spoken to independent software vendors such as Adobe Systems Inc., Automatic Data Processing LLC and UiPath Inc., which are all looking at new ways to go beyond the SaaS model and add value within cloud ecosystems, in particular building data services that are unique to their value proposition and will run across clouds.
So yeah – pretty much every tech vendor with any size or momentum and new industry players are coming out of hiding and competing… building superclouds. Many that look a lot like Moschella’s matrix with machine intelligence and artificial intelligence and blockchains and virtual reality and gaming… all enabled by the internet and hyperscale clouds.
It’s moving fast and it’s the future, in our opinion, so don’t get too caught up in the past or you’ll be left behind.
We’ve given many in the past, but let’s try to be a bit more specific. Below we cite a few and we’ll answer two questions in one section here: What workloads and services will run in superclouds and what are some examples?
Analytics. Snowflake is the furthest along with its data cloud in our view. It’s a supercloud optimized for data sharing, governance, query performance, security, ecosystem enablement and ultimately monetization. Snowflake is now bringing in new data types and open-source tooling and it ticks the attribute boxes on supercloud we laid out earlier.
Converged databases. Running transaction and analytics workloads. Take a look at what Couchbase is doing with Capella and how it’s enabling stretching the cloud to the edge with Arm-based platforms and optimizing for low latency across clouds and out to the edge.
Document database workloads. Look at MongoDB – a developer-friendly platform that with Atlas is moving to a supercloud model running document databases very efficiently. Accommodating analytic workloads and creating a common developer experience across clouds.
Data science workloads. For example, Databricks is bringing a common experience for data scientists and data engineers driving machine intelligence into applications and fixing the broken data lake with the emergence of the lakehouse.
General-purpose workloads. For example, VMware’s domain. Very clearly there’s a need to create a common operating environment across clouds and on-prem and out to the edge and VMware is hard at work on that — managing and moving workloads, balancing workloads and being able to recover very quickly across clouds.
Network routing. This is the primary focus of Aviatrix, building what we consider a supercloud and optimizing network performance and automating security across clouds.
Industry-specific workloads. For example, Capital One announcing its cost optimization platform for Snowflake – piggybacking on Snowflake’s supercloud. We believe it’s going to test that concept outside its own organization and expand across other clouds as Snowflake grows its business beyond AWS. Walmart Inc. is working with Microsoft to create an on-prem to Azure experience – yes, that counts. We’ve written about what Goldman is doing and you can bet dollars to donuts that Oracle Corp. will be building a supercloud in healthcare with its Cerner acquisition.
Supercloud is everywhere you look. Sorry, naysayers. It’s happening.
With all the industry buzz and debate about the future, John Furrier and the team at SiliconANGLE have decided to host an event on supercloud. We’re motivated and inspired to further the conversation. TheCUBE on Supercloud is coming.
On Aug. 9 out of our Palo Alto studios we’ll be running a live program on the topic. We’ve reached out to a number of industry participants — VMware, Snowflake, Confluent, Sky High Security, Hashicorp, Cloudflare and Red Hat — to get the perspective of technologists building superclouds.
And we’ve invited a number of vertical industry participants in financial services, healthcare and retail that we’re excited to have on along with analysts, thought leaders and investors.
We’ll have more details in the coming weeks, but for now if you’re interested please reach out to us with how you think you can advance the discussion and we’ll see if we can fit you in.
So mark your calendars and stay tuned for more information.
Thanks to Alex Myerson, who does the production, podcasts and media workflows for Breaking Analysis. Special thanks to Kristen Martin and Cheryl Knight, who help us keep our community informed and get the word out, and to Rob Hof, our editor in chief at SiliconANGLE.
Remember we publish each week on Wikibon and SiliconANGLE. These episodes are all available as podcasts wherever you listen.
Email email@example.com, DM @dvellante on Twitter and comment on our LinkedIn posts.
Also, check out this ETR Tutorial we created, which explains the spending methodology in more detail. Note: ETR is a separate company from Wikibon and SiliconANGLE. If you would like to cite or republish any of the company’s data, or inquire about its services, please contact ETR at firstname.lastname@example.org.
Here’s the full video analysis:
All statements made regarding companies or securities are strictly beliefs, points of view and opinions held by SiliconANGLE media, Enterprise Technology Research, other guests on theCUBE and guest writers. Such statements are not recommendations by these individuals to buy, sell or hold any security. The content presented does not constitute investment advice and should not be used as the basis for any investment decision. You and only you are responsible for your investment decisions.
Disclosure: Many of the companies cited in Breaking Analysis are sponsors of theCUBE and/or clients of Wikibon. None of these firms or other companies have any editorial control over or advanced viewing of what’s published in Breaking Analysis.
IBM’s Watson is being used by the All England Lawn Tennis Club (AELTC) as it strives to attract and retain digital audiences to the 154-year-old Wimbledon tennis championship.
After more than 30 years of providing the AELTC with technology for collecting statistics, as well as the IT foundations underpinning them, IBM is constantly working to help the organisation automate digital services and engage with fans.
Today, IBM Watson artificial intelligence (AI), sitting in IBM Cloud, is personalising content to encourage fans who try out digital platforms to do so again and again.
“Our main goal is to ensure we are maintaining Wimbledon’s relevance, attracting online audiences and providing them with the opportunity to engage with the event and keep coming back,” she told Computer Weekly.
The partnership with IBM, also a sponsor, has come a long way since the original agreement in 1990 saw IBM generate rudimentary stats for the AELTC. “IBM has helped us ensure we have the foundations to do that from a broader technology perspective, and [with IBM] we are continually challenging ourselves to innovate on what we have today and that we are adapting the way we provide for fans,” added Willis.
Watson is the nucleus of much of the latest innovation, with personalised services. For instance, today Watson is automatically creating highlight reels tailored for individual fans, using a combination of structured and unstructured data.
The ability of AI to automate the creation of personalised reels of match action is perhaps the most overt example of progress. In the past, the creation of highlight reels for broadcasters required humans to manually go through matches and pick out the key moments, which was very time-consuming. But today, Watson can create a reel automatically that is personalised for individual fans.
“These two-minute reels are automatically created by Watson through a combination of stats, listening to the crowd reaction and looking at the gestures of the players,” said Kevin Farrar, IBM UK sports partnership lead. “We then make it available to the Wimbledon digital team.”
Kevin Farrar, IBM
A huge amount of data is generated across the 18 courts at Wimbledon, and without in-depth knowledge, it is difficult for the average digital fan to fully appreciate a game. “It’s all reaching slightly different audiences, which was our goal, rather than preaching to the converted,” said Willis.
This is where IBM data scientists, combined with tennis experts, come in. “We take the tennis stats and combine it with other data sources, such as the Hawkeye system tracking the player and ball movements throughout a rally. We then create insights which are shared to different audiences,” said Farrar.
“We work with the club to bring the beauty and drama of Wimbledon to life for digital fans around the world,” he added. “It is essentially a massive data operation. It all starts with the data. Turning it into meaningful and engaging insights that we can put out on digital global platforms.”
Another popular digital offering is the IBM Power Index which ranks player momentum, form and performance of players in the lead-up to and during the championships. It looks at structured data such as results, but also unstructured data, including the buzz is in the media. It then applies an AI algorithm which comes up with a ranking for players.
“The Power Index was designed to help fans work out who to follow, and there has been good engagement with that,” said Willis. “Then, once fans have taken an interest in a player, we wanted to educate them on what to look out for in a match.” Another tool, Match Insights, presents fans with facts and allows them to challenge Watson and other users in making match predictions based on the detailed stats they receive.
There has been success in building audiences through digital platforms like these, according to Willis. “We have seen steady growth of digital platforms,” she said. “When I started here about 10 years ago, we were getting an audience of about 11 million unique devices. In 2016, we had a record of 21 million unique devices connect, when Andy Murray won. We are on course for a very successful tournament this year.”
“Beyond scale, it is about demographics and location. We are proud to be a global brand and our audience reflects that,” she added. “In terms of a younger audience, we are developing things using AI to help young people better understand tennis, so when they stumble upon it they are fans for life.”
Wimbledon is part of IBM’s global sports portfolio, which includes the Masters golf and the US Open tennis. It has teams that work all year around from the UK and Atlanta, US.
Tim Cook became CEO of Apple in August 2011, succeeding Apple founder Steve Jobs, who passed away in October 2011. Cook had previously served as Apple’s chief operating officer, overseeing the company’s sales and operations across the globe. He first joined Apple in 1998 after a 12-year career at IBM and gigs at Compaq and Intelligent Electronics. As CEO of Apple, Cook has been criticized for overstretching the company, introducing flawed or lackluster products, and losing market share to competitors like Amazon and Google. Under his leadership, Apple posted its first revenue decline in 13 years due to slowing iPhone sales, sparking alarm among investors. But Cook is leading the company’s push into new areas, such as health care, media, and self-driving cars, while continuously working to Strengthen much-loved products like the iPhone, with the goal of ensuring longevity for Apple. Originally from Alabama, Cook came out as gay in October 2014, making him the first openly gay CEO of a Fortune 500 company.
The explosive growth of information is our era’s most significant defining characteristic. In this Age of Information, the amount of data, the uses for that data, the number of data sources and the routes it travels all grow at exponential rates. The growth creates new industries for defining, collecting, accessing, processing and curating information.
In such an environment, everybody recognizes the essence of Information Governance, but how to undertake this massive task is harder to grasp.
Today’s organizations experience explosive growth in the volume and variety of the data they collect, process and store. Unfortunately, many do not understand the types of data they handle and what value it has, which means they cannot use or maintain it properly. As a result, they fail to achieve the success level they would have if they kept proper management over the data.
Organizations can also suffer serious financial, legal and reputational consequences over poor data management. Information Governance helps to avoid a similar fate.
So, what is Information Governance (IG) and what role does it play in today’s business environment? This guide sheds some light on IG – an emerging data management area that focuses on business processes and compliance.
IG refers to a strategic approach to maximize the value of data and mitigate the risks associated with the creation, use and sharing of enterprise information. It recognizes the information as an organizational asset that requires high-level oversight and coordination to ensure accountability, protection, integrity and appropriate preservation of enterprise information.
IG aims to break down silos and avoid any fragmentation in information management, which ensures that it remains trustworthy and that organizations experience ROI in the processes, technology and people they use to manage information.
Information governance has many formal definitions, but Gartner’s is the most widely accepted. It defines IG as an accountability framework that ensures appropriate behavior in the creation, valuation, use, archiving, deletion and storage of information. It includes the standards and metrics, roles and policies, and the processes required to ensure effective and efficient information use and enable organizations to achieve their goals.
IG processes help manage the use of information records, such as customer information, employee records, medical records and intellectual property. Your company’s IG professionals should work with your leadership and any other stakeholders in the creation of policies that specify how your employees should handle all corporate information assets.
The critical goals of Information Governance include the following:
To help you clearly define Information Governance goals and processes, you can develop frameworks to outline your organization’s approach formally. The framework outlines and answers who, what, where, when, how and why questions.
You should tailor your framework to fit your organization’s unique needs, but it should define the areas discussed below:
It is also vital to establish how organizations operate and share information with their partners, stakeholders and suppliers. Your framework should define the policies and procedures established for sharing information with third parties, how the Information Governance process influences contractual obligations and how you will determine whether your partners and third parties meet your IG goals.
What's more, your framework should clearly outline procedures in the event of data breaches, including how to report violations and information losses, disaster recovery processes, incident management specifics, business continuity strategies, and how you will audit these disaster recovery and business continuity processes.
Finally, your framework should outline your process of continuous monitoring. Include plans for quality assurance of IG processes such as how you will monitor information access, measure regulatory compliance adherence, conduct risk assessments, maintain adequate security and review the IG program as a whole.
Many people, and organizations, consider IG and data governance as the same thing. Although both are essential for companies to achieve their business objectives, and despite some overlap between them, they are not identical.
Information Governance gets organizations business value from their data assets. It’s the technologies and activities that organizations employ to maximize their information value while minimizing associated costs and risks.
On the other hand, data governance refers to the control of information at business-unit levels to ensure it is accurate and reliable. Its programs involve procedures to manage data usability, availability, integrity and security.
In short, data governance keeps rubbish from getting in, while IG refers to the decisions you make in using data.
Here are some examples of the types of activities involved in both areas to help illustrate the differences.
Data governance is the responsibility of IT, but IG has a broader scope. You can use IG to meet business and compliance needs concerning the use and retention of data, which makes it a strategic discipline that plays a significant part in your corporate governance.
Applying IG and data governance together can result in information management practices that help you deliver higher business value.
Information is a vital resource in any organization or business. Without it, business operations are not possible. Accordingly, companies make investments in processes, technology and people to ensure that information can support the enterprise.
Due to the significant investments associated with the creation, use, protection and sharing of information, organizations view it as a type of business asset, not unlike the equipment, buildings and financial resources needed to run the business.
Oversight and stewardship of resources or assets is the primary aim of any business governance. What’s more, just like any other asset, the information requires management to ensure that you address its value and associated risks responsibly.
Information Governance provides businesses with a disciplined approach to managing the risks and value associated with information.
Since IG is still an emerging field, numerous questions exist around its role in business processes. However, a properly implemented IG program allows organizations to do the following:
For example, due to the challenges that the healthcare industry is currently facing, with relation to changes in care, payment models, requirements to partner with others, new customer expectations, technology and increased regulation, Information Governance is now more critical than ever. It is the best way for healthcare and related organizations to ensure that their information is reliable and that they can trust it to meet all their diverse needs.
IG allows you to make decisions driven by the needs of your organization and not technology. It also eliminates accidental decision-makers (people who happen to possess data at a particular point during its cycle) because they tend to make decisions independently of other stakeholder needs.
To identify the best place to start your IG initiative, you need to figure out a way to support your organization’s strategic efforts with reliable information and data.
Organizations usually have a mission and vision that guides them along as they conduct business and develop strategies to help achieve their goals. Thus, taking a careful look at those business strategies and goals can deliver you a strong hint about where and how to start your IG initiative.
Since you cannot achieve any organizational goal without useful information, the best place to start your IG initiative is identifying a problem (pain point) with information that requires addressing, or even a business opportunity that reduces costs and enhances revenue.
Such strategic alignment means that you should put your IG needs as part of a broader strategy that will help achieve your organizational goals. Your goals can be extensive and varied, such as better management of space (real estate), expanding service offerings through the acquisition and integration of other businesses, creating new customer service protocols or reducing your costs.
Since IG is a set requirement of responsibility and rights to allow the suitable function of various information aspects, the provision of decision rights determines data ownership and who has the right to make decisions about it.
Therefore, by defining owners and decision-makers, you can assign responsibility and accountability to data decisions, which is probably an essential concept to implement when creating your IG policy. Accountability is vital since as data dependence grows, you can make business decisions by default, usually by selecting the easiest path and often in isolation from other considerations.
You should consider the following key areas when creating your Information Governance policy:
Usage policy: You can contain a lot of security risk using a well-defined usage policy that specifically details who can access data and under what circumstances.
Accountability: You should create a position such as Chief Data Officer or dedicate a department to the creation of standard policies to ensure that someone in your organization is responsible for data-handling policies.
Records Management: Large organizations could store up to 10 petabytes of information annually, which is costly. Using IG, you could save on storage costs by identifying and storing data that has value.
Compliance: Laws, business needs and regulations govern how you keep your information. After that, you should discard it as per an established lifecycle schedule base on legal, regulatory and business requirements.
Education: As with all other company policies, the training of your employees, partners and vendors about your IG program competes the circle.
Technology: A complete IG can also address IT governance. It can provide IT specialists with policies such as the creation of storage hierarchies or obtaining appropriately scaled access schemes.
As corporate data volumes grow and technological innovations continually expand business capabilities, regulations that put strict laws and mandates on the IG process have become the norm. This is true for data security and privacy since personally identifiable information (PIN) has recently become a massive target for nefarious online actors and hackers.
Privacy laws have started expanding globally, creating new information security governance obligations. Many industries have become subject to regulations requiring the retention of electronic communications and records for a minimum period. These regulations include directives from federal agencies such as the Department of Justice and Environmental Protection Agency or the Securities and Exchange Commission.
Regulatory reporting requirements also mandate organizations to provide a detailed annual account of compliance. A sound business records management process provides evidence to demonstrate compliance.
What’s more, compliance rules such as the Foreign Corrupt Practices Act, require organizations to attest the authenticity of their IG programs and records.
There exist numerous industry and government requirements related to data security, records management and data retention that can affect your IG strategy. Below are some of the essential laws that all organizations operating in the USA need to know.
Assessment tools such as the IG Maturity Model and the IG Reference Model help companies measure the progress of their Information Governance progress. The IG Reference Model provides corporations, industry associations, analyst firms and other interested parties a tool that allows them to communicate to and with stakeholders concerning processes, practices and responsibilities of their IG program.
On the other hand, the IG Maturity Model is based on ARMA’s eight Generally Accepted Recordkeeping Principles. The maturity model defines the characteristics of various recordkeeping program levels that range from substandard to transformational IG. The goal of organizations is to reach the top transformational level where IG strategies are integrated into the overall corporate infrastructure or business processes to help boost cost containment, client services and competitive advantage.
IG is a set requirement of responsibility and rights to allow the suitable function of various aspects of information that include creation, valuation, use, storage, deletion and archiving. To use data effectively, IG includes policies, purposes, processes and standards that help organizations achieve their goals.
Information Governance brings organizations significant benefit and value, especially as their data collection and stores grow and regulatory oversight increases. The development and implementation of a sound IG strategy help organizations ensure data availability, control costs, mitigate cyber risks and meet regulatory challenges. Get started today before your organization suffers a security breach, faces a lawsuit, fails an audit or suffers reputational damage.
In 2003, nothing could stop AMD. This was a company that moved from a semiconductor company based around second-sourcing Intel designs in the 1980s to a Fortune 500 company a mere fifteen years later. AMD was on fire, and with almost a 50% market share of desktop CPUs, it was a true challenger to Intel’s throne.
AMD began its corporate history like dozens of other semiconductor companies: second sourcing dozens of other designs from dozens of other companies. The first AMD chip, sold in 1970, was just a four-bit shift register. From there, AMD began producing 1024-bit static RAMs, ever more complex integrated circuits, and in 1974 released the Am9080, a reverse-engineered version of the Intel 8080.
AMD had the beginnings of something great. The company was founded by [Jerry Sanders], electrical engineer at Fairchild Semiconductor. At the time [Sanders] left Fairchild in 1969, [Gordon Moore] and [Robert Noyce], also former Fairchild employees, had formed Intel a year before.
While AMD and Intel shared a common heritage, history bears that only one company would become the king of semiconductors. Twenty years after these companies were founded they would find themselves in a bitter rivalry, and thirty years after their beginnings, they would each see their fortunes change. For a short time, AMD would overtake Intel as the king of CPUs, only to stumble again and again to a market share of ten to twenty percent. It only takes excellent engineering to succeed, but how did AMD fail? The answer is Intel. Through illegal practices and ethically questionable engineering decisions, Intel would succeed to be the current leader of the semiconductor world.
The early to mid 1990s were a strange time for the world of microprocessors and desktop computing. By then, the future was assuredly in Intel’s hands. The Amiga with it’s Motorola chips had died, Apple had switched over to IBM’s PowerPC architecture but still only had a few percent of the home computer market. ARM was just a glimmer in the eye of a few Englishmen, serving as the core of the ridiculed Apple Newton and the brains of the RiscPC. The future of computing was then, like now, completely in the hands of a few unnamed engineers at Intel.
The cream had apparently risen to the top, a standard had been settled upon, and newcomers were quick to glom onto the latest way to print money. In 1995, Cyrix released the 6×86, a processor that was able – in some cases – to outperform their Intel counterpart.
Although Cyrix had been around for the better part of a decade by 1995, their earlier products were only x87 coprocessors, floating point units and 386 upgrades. The release of the 6×86 gave Cyrix its first orders from OEMs, landing the chip in low-priced Compaqs, Packard Bells, and eMachines desktops. For tens of thousands of people, their first little bit of Internet would leak through phone lines with the help of a Cyrix CPU.
During this time, AMD also saw explosive growth with their second-sourced Intel chips. An agreement between AMD and Intel penned in 1982 allowed a 10-year technology exchange agreement that gave each company the rights to manufacture products developed by the other. For AMD, this meant cloning 8086 and 286 chips. For Intel, this meant developing the technology behind the chips.
1992 meant an end to this agreement between AMD and Intel. For the previous decade, OEMs gobbled up clones of Intel’s 286 processor, giving AMD the resources to develop their own CPUs. The greatest of these was the Am386, a clone of Intel’s 386 that was nearly as fast as a 486 while being significantly cheaper.
AMD’s break from second-sourcing with the Am386 presented a problem for the Intel marketing team. The progression from 8086 to the forgotten 80186, the 286, and 486 could not continue. The 80586 needed a name. After what was surely several hundred thousand dollars, a marketing company realized pent- is the Greek prefix for five, and the Pentium was born.
The Pentium was a sea change in the design of CPUs. The 386, 486, and Motorola’s 68k line were relatively simple scalar processors. There was one ALU in these earlier CPUs, one bit shifter, and one multiplier. One of everything, each serving different functions. In effect, these processors aren’t much different from CPUs constructed in Minecraft.
Intel’s Pentium introduced the first superscalar processor to the masses. In this architecture, multiple ALUs, floating point units, multipliers, and bit shifters existed on the same chip. A decoder would shift data around to different ALUs within the same clock cycle. It’s an architectural precedent for the quad-core processors of today, but completely different. If owning two cars is equivalent of a dual-core processor, putting two engines in the same car would be the equivalent of a superscalar processor.
By the late 90s, AMD had rightfully earned a reputation for having processors that were at least as capable as the Intel offerings, while computer magazines beamed with news that AMD would unseat Intel as the best CPU performer. AMD stock grew from $4.25 in 1992 to ten times that in early 2000. In that year, AMD officially won the race to a Gigahertz: it introduced an Athlon CPU that ran at 1000 MHz. In the words of the infamous Maximum PC cover, it was the world’s fastest street legal CPU of all time.
Gateway, Dell, Compaq, and every other OEM shipped AMD chips alongside Intel offerings from the year 2000. These AMD chips were competitive even when compared to their equivalent Intel offerings, but on a price/performance ratio, AMD blew them away. Most of the evidence is still with us today; deep in the archives of overclockers.com and tomshardware, the default gaming and high performance CPU of the Bush administration was AMD.
For Intel, this was a problem. The future of CPUs has always been high performance computing, It’s a tautology given to us by unshaken faith in Moore’s law and the inevitable truth that applications will expand to fill all remaining CPU cycles. Were Intel to lose the performance race, it would all be over.
And so, Intel began their underhanded tactics. From 2003 until 2006, Intel paid $1 Billion to Dell in exchange not to ship any computers using CPUs made by AMD. Intel held OEMs ransom, encouraging them to only ship Intel CPUs by way of volume discounts and threatening OEMs with low-priority during CPU shortages. Intel engaged in false advertising, and misrepresenting benchmark results. This came to light after a Federal Trade Commission settlement and an SEC probe disclosed Intel paid Dell up to $1 Billion a year not to use AMD chips.
In addition to those kickbacks Intel paid to OEMs to only sell their particular processors, Intel even sold their CPUs and chipsets for below cost. While no one was genuinely surprised when this was disclosed in the FTC settlement, the news only came in 2010, seven years after Intel started paying off OEMs. One might expect AMD to see an increase in market share after the FTC and SEC sent them through the wringer. This was not the case; since 2007, Intel has held about 70% of the CPU market share, with AMD taking another 25%. It’s what Intel did with their compilers around 2003 that would earn AMD a perpetual silver medal.
Intel does much more than just silicon. Hidden inside their design centers are the keepers of x86, and this includes the people who write the compilers for x86 processors. Building a compiler is incredibly complex work, and with the right amount of optimizations, a compiler can turn code into an enormous executable which is capable of running on the latest Skylake processors to the earliest Pentiums, with code optimized for each and every processor in between.
While processors made a decade apart can see major architectural changes and processors made just a few years apart can see changes in the feature size of the chip, in the medium term, chipmakers are always adding instructions. These new instructions – the most famous example by far would be the MMX instructions introduced by Intel – provide the chip with new capabilities. With new capabilities come more compiler optimizations, and more complexity in building compilers. This optimization and profiling for different CPUs does far more than the -O3 flag in GCC; there is a reason Intel produces the fastest compilers, and it’s entirely due to the optimization and profiling afforded by these compilers.
Beginning in 1999 with the Pentium III, Intel introduced SSE instructions to their processors. This set of about 70 new instructions provided faster calculations for single precision floats. The expansion to this, SSE2, introduced with the Pentium 4 in 2001 greatly expanded floating point calculations and AMD was obliged to include these instructions in their AMD64 CPUs beginning in 2003.
Although both Intel and AMD produced chips that could take full advantage of these new instructions, the official Intel compiler was optimised to only allow Intel CPUs to use these instructions. Because the compiler can make multiple versions of each piece of code optimized for a different processor, it’s a simple matter for an Intel compiler to choose not to use improved SSE, SSE2, and SSE3 instructions on non-Intel processors. Simply by checking if the vendor ID of a CPU is ‘GenuineIntel’, the optimized code can be used. If that vendor ID is not found, the slower code is used, even if the CPU supports the faster and more efficient instructions.
This is playing to the benchmarks, and while this trick of the Intel compiler has been known since 2005, it is a surprisingly pernicious way to gain market share.
We’ll be stuck with this for a while. That’s because all code generated by an Intel compiler exploiting this trick will have a performance hit on non-Intel CPUs, Every application developed with an Intel compiler will always perform worse on non-Intel hardware. It’s not a case of Intel writing a compiler for their chips; Intel is writing a compiler for instructions present in both Intel and AMD offerings, but ignoring the instructions found in AMD CPUs.
There are two giants of technology whose products we use every day: Microsoft and Intel. In 1997, Microsoft famously bailed out Apple with a $150 Million investment of non-voting shares. While this was in retrospect a great investment – had Microsoft held onto those shares, they would have been worth Billions – Microsoft’s interest in Apple was never about money. It was about not being a monopoly. As long as Apple had a few percentage points of market share, Microsoft could point to Cupertino and say they are not a monopoly.
And such is Intel’s interest in AMD. In the 1980s, it was necessary for Intel to second source their processors. By the early 90s, it was clear that x86 would be the future of desktop computing and having the entire market is the short path to congressional hearings and comparisons to AT&T. The system worked perfectly until AMD started innovating far beyond what Intel could muster. There should be no surprise that Intel’s underhandedness began in AMD’s salad days, and Intel’s practices of selling CPUs at a loss ended once they had taken back the market. Permanently disabling AMD CPUs through compiler optimizations ensured AMD would not quickly retake this market share.
It is in Intel’s interest that AMD not die, and for this, AMD must continue innovating. This means grabbing whatever market they can – all current-gen consoles from Sony, Microsoft, and Nintendo feature AMD chipsets. This also means AMD must stage their comeback, and in less than a year this new chip, the Zen architecture, will land in our sockets.
In early 2012, the architect of the original Athlon 64 processor returned to AMD to design Zen, AMD’s latest architecture and the first that will be made with a 14nm process. Only a few months ago, the tapeout for Zen was completed, and these chips should make it out to the public within a year.
Is this AMD’s answer to a decade of deceit from Intel? Yes and no. One would hope Zen and the K12 designs are the beginning of a rebirth that would lead to a true competition not seen since 2004. The product of these developments are yet to be seen, but the market is ready for competition.
The official answer to that question is simple. UNIX® is any operating system descended from that original Bell Labs software developed by Thompson, Ritchie et al in 1969 and bearing a licence from Bell Labs or its successor organisations in ownership of the UNIX® name. Thus, for example, HP-UX as shipped on Hewlett Packard’s enterprise machinery is one of several commercially available UNIXes, while the Ubuntu Linux distribution on which this is being written is not.
The real answer is considerably less clear, and depends upon how much you view UNIX as an ecosystem and how much instead depends upon heritage or specification compliance, and even the user experience. Names such as GNU, Linux, BSD, and MINIX enter the fray, and you could be forgiven for asking: would the real UNIX please stand up?In the beginning, it was a relatively contiguous story. The Bell Labs team produced UNIX, and it was used internally by them and eventually released as source to interested organisations such as universities who ran it for themselves. A legal ruling from the 1950s precluded AT&T and its subsidiaries such as Bell Labs from selling software, so this was without charge. Those universities would take their UNIX version 4 or 5 tapes and install it on their DEC minicomputer, and in the manner of programmers everywhere would write their own extensions and improvements to fit their needs. The University of California did this to such an extent that by the late 1970s they had released it as their own distribution, the so-called Berkeley Software Distribution, or BSD. It still contained some of the original UNIX code so was still technically a UNIX, but was a significant departure from that codebase.
UNIX had by then become a significant business proposition for AT&T, owners of Bell Labs, and by extension a piece of commercial software that attracted hefty licence fees once Bell Labs was freed from its court-imposed obligations. This in turn led to developers seeking to break away from their monopoly, among them Richard Stallman whose GNU project started in 1983 had the aim of producing an entirely open-source UNIX-compatible operating system. Its name is a recursive acronym, “Gnu’s Not UNIX“, which states categorically its position with respect to the Bell Labs original, but provides many software components which, while they might not be UNIX as such, are certainly a lot like it. By the end of the 1980s it had been joined in the open-source camp by BSD Net/1 and its descendants newly freed from legacy UNIX code.
In the closing years of the 1980s Andrew S. Tanenbaum, an academic at a Dutch university, wrote a book: “Operating Systems: Design and Implementation“. It contained as its teaching example a UNIX-like operating system called MINIX, which was widely adopted in universities and by enthusiasts as an accessible alternative to UNIX that would run on inexpensive desktop microcomputers such as i386 PCs or 68000-based Commodore Amigas and Atari STs. Among those enthusiasts in 1991 was a University of Helsinki student, Linus Torvalds, who having become dissatisfied with MINIX’s kernel set about writing his own. The result which was eventually released as Linux soon outgrew its MINIX roots and was combined with components of the GNU project instead of GNU’s own HURD kernel to produce the GNU/Linux operating system that many of us use today.
So, here we are in 2019, and despite a few lesser known operating systems and some bumps in the road such as Caldera Systems’ attempted legal attack on Linux in 2003, we have three broad groupings in the mainstream UNIX-like arena. There is “real” closed-source UNIX® such as IBM AIX, Solaris, or HP-UX, there is “Has roots in UNIX” such as the BSD family including MacOS, and there is “Definitely not UNIX but really similar to it” such as the GNU/Linux family of distributions. In terms of what they are capable of, there is less distinction between them than vendors would have you believe unless you are fond of splitting operating-system hairs. Indeed even users of the closed-source variants will frequently find themselves running open-source code from GNU and other origins.
At 50 years old then, the broader UNIX-like ecosystem which we’ll take to include the likes of GNU/Linux and BSD is in great shape. At our level it’s not worth worrying too much about which is the “real” UNIX, because all of these projects have benefitted greatly from the five decades of collective development. But it does raise an interesting question: what about the next five decades? Can a solution for timesharing on a 1960s minicomputer continue to adapt for the hardware and demands of mid-21st-century computing? Our guess is that it will, not in that your UNIX clone in twenty years will be identical to the one you have now, but the things that have kept it relevant for 50 years will continue to do so for the forseeable future. We are using UNIX and its clones at 50 because they have proved versatile enough to evolve to fit the needs of each successive generation, and it’s not unreasonable to expect this to continue. We look forward to seeing the directions it takes.
As always, the comments are open.
The UK is seeing its digital and tech economy boom, with a huge growth in job vacancies and wages for roles outstripping the rest of the economy. However, it’s not all good news, as opportunities appear to be mostly going to those coming out of elite education institutions - which make up a small percentage of the population - and there appears to be a shrinking number of vacancies lower down the ranks, making it harder for people to get trained up.
The new data has been published in a comprehensive report by Tech Nation, a government-backed think tank, which outlines that nearly 5 million people now work in the digital tech economy, up from 2.18 million in 2011. That number is also up from just under 3 million in 2019, which highlights the structural shifts the UK economy has been through during the course of the COVID-19 pandemic.
Tech Nation states that the report aims to fill a gap in data around skills in the digital economy, which could be leading to less informed decisions on the part of both employers, employees and the government. The report uses data from Adrian, Dealroom and the Office for National Statistics. It states:
During the uncertain times faced by most people over the last two years, technology has been an enabler for individuals, companies and communities. It has facilitated new ways of working, and kept the economy buoyant. Tech has also been an important source of job creation as we return to a sense of normality. Nevertheless, we are not returning to the economy, or the labour market that we left in 2019.
This significant ramping up of tech economy workers has been due, in part to the permeation and transformation of tech across the economy as a whole. Over 36% of people working in the digital tech economy are in non technical roles, and a further 30% of roles are in tech roles outside of the tech sector.
On the other side of the coin, the tech sector itself has continued to grow at an astounding rate. Between 2020 and 2021 venture capital investment into UK based tech startups and scaleups increased by 130%. This surge of investment creates employment opportunities, to spearhead growth in scaling firms.
What’s clear from the data is that the UK - both the private sector and government institutions - should be fostering the tech and digital economy, as it continues to grow. Jobs in this sector now account for approximately 14% of the UK workforce and more than 2 million tech vacancies were advertised over the last year - more than any other area of the UK labour market.
Tech Nation said that the boom in hiring is reflective of the growth seen in venture capital investment into UK tech companies in 2021, which had a 130% increase to just under $41 billion. This is also being bolstered by an increasing permeation of tech roles across the economy. See the chart below:
Over 36% of jobs in the digital tech economy are in non tech occupations, like product management, user experience, people and sales. Some 36.8% of roles are in non-tech positions, and a further 33% of roles are technical, but outside of the tech sector.
Furthermore, tech vacancies have increased on a month by month basis over the last year, from 145,000 roles advertised in May 2021, to 181,000 roles as of May 2022.
Large tech and professional services companies, many of which are based in the US, such as IBM, Oracle and Amazon, are leading the way in terms of tech job ads. However, ‘UK decacorn’ Ocados is third in the UK hiring rankings, with over 33,500 roles advertised last year.
According to the report, data and architecture are the most in demand tech skills, jumping up the ranking after seeing growth in demand of over 1000% respectively from 2019 to 2021.
And it’s not only vacancies and job roles that are on the up, wages too are higher than the rest of the economy. Tech jobs command an 80% premium on non tech jobs in the UK, with the average salary being £62,000 compared to £35,000 elsewhere.
However, Tech Nation does warn that this could lead to problems, as it states:
With growth in employment as we have witnessed over the last five years, on the one hand, is a good thing from an economic, and labour market perspective. Well paid jobs across the UK are being created. However, if left unchecked, this could pose a potentially problematic situation whereby tech becomes fragmented, or polarises the economy. In its own right, this is a fairly natural phenomenon, but consider that levels of gender, geographical, age and in some cases ethnic diversity remain entrenched in tech, with little movement over the last five years, and we start to see that a polarisation problem may be emerging.
This is not a phenomenon that will inevitably occur if appropriate intervention measures are taken. We know that the tech economy is home to a variety of technical and non technical roles, offering a wide range of opportunities, and creating many new forms of work, and jobs. The message of opportunity for all must be something we collectively emphasise so that no one is left behind.
Despite all the positive indicators in the tech and digital economy, Tech Nation’s report does come with some fair warnings that the UK needs to ensure that the opportunities being seen are equitable.
For instance, awareness of the opportunity to earn more is slim across the UK, as only 26% of people believe that developing a tech skill will allow them to attract higher wages.
In addition to this, demand for senior tech positions have been increasing over the past three years. For every one ‘no experience needed’ role advertised, there are approximately eight senior roles advertised. This is a problem, as the report states:
The sticking point in this dynamic is that demand for senior roles is burgeoning, whilst demand for junior and intermediate level roles has decreased. This may create a supply issue in future, with fewer prospective employees able to gain vital experience in tech, and companies struggling to hire for experienced people. A resolution to this situation will require a reconsideration of roles being hired for by firms, and an acknowledgement that responsibility must be taken to contribute to the skills and experience development of staff.
In addition to this, a huge proportion of senior leaders are highly qualified individuals. The majority of people including this information in their profile have a bachelors degree (74.6%) and just under half have a Masters degree (46.3%).
But what’s particularly interesting, is that world leading educational institutions top the charts for tech C-suite education - highlighting a potentially problematic position. The University of Cambridge and the University of Oxford top the leaderboard for educational institutions making up a combined total of 4.6% of educational experiences. However, just 1% of UK students get a place at Oxbridge. Other red brick institutions make up a big chunk of the education found in the tech C-suite.
Dr George Windsor, data and research director at Tech Nation, writes that the UK needs to be careful of these trends to really take advantage of the opportunity. They said:
This report highlights that senior leaders in tech, those in C-suite and director roles, are overwhelmingly Oxbridge and red brick university educated. This leads to a potentially problematic disjunction in the messaging around opportunity for all, versus the impression leaders often from elite higher education institutions provides.
The financial rationale exists more patently than ever, to pursue a career in the tech economy. On average, tech jobs now command an 80% premium on non tech jobs in the UK, up from around 60% only a year ago.
Yet, only 26% of people surveyed believed that acquiring or developing a new tech skill would position them well to earn more in the future - highlighting an information gap. 44% of UK respondents believe having tech skills are essential for job security and 64% of those working in tech agree. With the fast pace of change in tech, it will be essential to encourage upskilling throughout all people's careers.
Demand for tech roles has never been higher, as the report points out, over 2mn vacancies last year, and ever growing employment in the tech economy has only accelerated over the last year. In parallel, demand for senior tech positions have been increasing over the past 3 years. For every one “no experience” role advertised, there are approximately eight senior roles advertised.
This is again a potentially challenging position. If demand for senior roles is burgeoning, whilst demand for junior and intermediate level roles has decreased this may create a supply issue in future. It will lead to fewer prospective employees able to gain vital experience in tech, and companies struggling to hire for experienced people. A resolution to this situation will require a reconsideration of roles being hired for by firms, and an acknowledgement that responsibility must be taken to contribute to the skills and experience development of staff.
In conclusion, growth inevitably brings challenges - without the right mix of people, capital and innovation, we will not see realised the positive growth trajectory for UK tech we all hope . As such, it is a responsibility of employers, hiring organisations, individuals and support organisations to raise awareness, promote upskilling and in work training, and open doors to those with less experience in tech to pave the way to a brighter tech future for all
An innovative platform enabling access to microservices architecture to build automated end-to-end data pipelines
PLEASANTON, Calif., July 20, 2022 /PRNewswire/ -- SecureKloud Technologies today launched DataEdge, a cloud-based data analytics and AI engineering platform that enables enterprises to power insight-driven decision-making capabilities. Coming from an undisputed leader in the cloud transformation solutions space,the DataEdge platform provides highly modular, scalable, and API-driven solutions to unlock data-powered insights.Configured to HITRUST standards,DataEdge is a zero-code platform, which can be easily deployed in hours with zero development time.
As a growing number of enterprises look toward harnessing the full potential of their data, they are looking for easy-to-use, highly flexible, and scalable infrastructure platforms to speed up their data-driven digital transformation journey. DataEdge platform's advanced analytics capabilities allow the users to process even the most complex queries in minutes rather than days.
Empowering enterprises to embrace and evolve with cloud has always been SecureKloud's mission of changing the world through digital transformation. DataEdge makes it possible for enterprises to make use of their massive amounts of data to drive their business forward through a cloud-based approach. Being platform agnostic, it works perfectly well for all cloud architectures irrespective of them being built on AWS, Azure, Google Cloud, etc.
"With the exponential data generated across industries, there is a need for a scalable, secure, and compliant AI engineering and analytics platform to ensure data and agility are never compromised. With DataEdge, we are meeting the market demands and catalyzing by minimizing OPEX costs by 80% and driving a quicker go-to-market by 90%," said Anand Kumar,Chief Revenue Officer at SecureKloud Technologies.
Fully automated one-click platform deployment
Advanced analytics solutions (AI/ML)
Pre-built pipelines for industry segments
Highly agile, secure, and compliant data infrastructure through data encryption, Edge, and Perimeter security controls,etc.
Leverage interpretability, scalability,optimized performance,and reliability guaranteed by AI Engineering enabled through DevOps & DataOps
Modern microservices architecture handles structured and unstructured data of any modality with ease
About SecureKloud Technologies Inc.:
SecureKloud is a leading Global IT Business Transformations, Secure Cloud Operations and Solutions Provider based in the San Francisco Bay area and a publicly listed company on Indian Stock Exchanges (NSE and BSE). The company is a 3rd party Audited Next-Gen AWS MSP Partner, AWS Premier Partner, GCP Premier Partner, and an ISO 27001 certified cloud service provider.
View original content:https://www.prnewswire.com/news-releases/securekloud-technologies-unveils-dataedge-a-first-of-its-kind-ai-powered-data-analytics-platform-301589864.html
SOURCE SecureKloud Technologies