For industry, the pandemic changed the game

2020 forced certain industrial sectors, once seemingly invincible, to grapple with their mortality. Oil prices fell below zero for the first time in history. Millions of jobs were lost. Worst-case scenarios were no longer just hypotheticals—they would be distinct possibilities without massive efforts to transform, address vulnerabilities, build resilience and digitalize.

Suddenly, once-analog traditional industries like oil and gas, power and utilities, and manufacturing began adopting digital tools at a faster pace, from remote work to robotics.

Initiatives quickly ran into roadblocks

For too many industrial companies in the Kingdom of Saudi Arabia and the wider Middle East, data is still inaccessible and disconnected. It takes more time to find the data than to build solutions that create value. Compare that to the consumer world, where data is instantly accessible anywhere, anytime, and applications use machine learning to learn from our behavior and make recommendations.

To solve this fundamental data problem in industry, we need new ways of working with data. One absolutely essential tool is the new, rapidly growing discipline of data operations (DataOps) – a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization.

What is DataOps?

DataOps can be defined as an agile, process-oriented methodology for developing and delivering analytics. It typically comprises a team of data experts (think: data scientists, analysts, architects, and the like) who exist to “provide the tools, process and organizational structures to support the data-focused enterprise.”

The goal of DataOps is to create predictable delivery and change management of data, data models and relative artifacts, using technology to automate data delivery with the appropriate levels of security, quality and metadata to improve the use and value of data in a dynamic environment.

DataOps engineers and teams are tasked with bringing data to data customers throughout the organization, internal and external, and making their data do more for the company.

The origins and evolution of DataOps

The term DataOps dates back to the 2010s. In a 2014 blog post, Information Week’s contributing editor Lenny Liebmann looked at the emergence of big data and saw a need for a set of best practices to ensure that data science happens efficiently and reliably.

Liebmann called this discipline “DataOps,” or “the set of best practices that improve coordination between data science and operations.”

Seven years later, DataOps is a “top trend,” according to VentureBeat, and the race is on to sharpen the definition and claim a piece of the market:

Two strong common threads across these definitions are the importance of collaboration and the focus on integrating data across different parts of an organization.

DataOps vs. DevOps

DataOps, DevOps, MLOps—it can be difficult to keep track of the latest buzzwords out of the world of IT. Here’s a clarification:

DataOps is not DevOps. DevOps has been around for years. Despite some superficial commonalities, DevOps and DataOps are fundamentally different. Both are methodologies used to enhance operational practices, but that’s where the similarity ends.

The focus for DataOps is the delivery of business-ready, trusted, actionable, high-quality data, available to all “smart engineers” or “data consumers.” One goal is automation efforts, centered on data governance and integration. Another goal is alignment between IT system support, operations, and the business.

The focus for DevOps, in comparison, is software and application development. Automation efforts center on the development cycle, software delivery processes, and waste elimination. The alignment of developers, operations, and the business is a major aim of DevOps.

What is Industrial DataOps?

A more specific version of the new discipline, Industrial DataOps is about breaking down silos and optimizing the broad availability and usability of industrial data generated in asset-heavy industries such as oil and gas, power and utilities, and manufacturing.

Here are three key truths about Industrial DataOps:

1. Industrial DataOps depends on collaboration with domain experts

Individuals and interactions (far more than processes and tools) are essential to make data valuable and useful for data consumers across an organization—in other words, the domain experts in different fields and departments.

It’s important to remember that DataOps is a practice, a way of engaging and collaborating across the organization to both share and reap greater value from the data.

2. Data is only as valuable as the analytics behind it and the scale of people who use it

The convergence of data and analytics has made Industrial DataOps an operational necessity. To be used broadly, data requires context. Automating the data process and creating one central, contextualized source of truth is the only way to make sure that live data triumphs over static documentation and reports in the decision-making process.

3. Extracting the value of the data requires an agile approach

Industrial DataOps isn’t about documenting, reporting, or extensive up-front design. It’s a far more agile process in which experimentation, iteration, and feedback are essential.

Creating business value isn’t a one-way transaction between data scientist and department. It’s a joint effort that requires both sides to participate, share, and develop solutions that hold transformative potential. The data is alive, and so are the means of working with it.

What are the benefits of Industrial DataOps?

Industrial DataOps is already becoming a driving force in industrial transformations, helping accelerate digital maturity, enabling data teams to deliver more digital products, and realizing more operational value at scale.

In a 2020 survey of global companies, McKinsey found organizations that embedded DataOps could see the volume of new features increase by 50% because data automation enables quicker development iterations. Specific benefits include:

Are you ready for Industrial DataOps?

CNTXT is the exclusive reseller of Cognite Data Fusion, the leading Industrial DataOps platform, in the MENA region. With Industrial DataOps, your organization can achieve record time to value:

In addition to offering enterprises in the Kingdom of Saudi Arabia exclusive access to world-leading DataOps, we also provide a global top-three hyperscale cloud platform provider and some of the best developers, data experts, and digital transformation solutions.

For more information about how Cognite Data Fusion is addressing the most difficult industrial data challenges to provide open, contextualized data, download the Cognite Data Fusion fact sheet here.

The cloud is here, so what happens next? 

Cloud computing has evolved as a disruptive innovation to traditional computing and is fast becoming the new normal. In October 2020, Saudi Arabia’s Ministry of Communications and Information Technology published KSA’s Cloud First Policy to accelerate the adoption of cloud computing services. This policy directs governmental entities to consider cloud options when making new IT investment decisions. The private sector was encouraged to follow the same exercise by creating an internal cloud-first Policy. The policy was defined in line with the key pillars of KSA’s ambitious Vision 2030. 

What may be less clear, is how you create that could-first strategy.

What is a cloud-first approach?

Cloud computing is defined by National Institute of Standards and Technology (NIST) as a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics: On-demand self-service, Broad Network Access, Resource Pooling, Rapid Elasticity, and Measured Service. 

For organizations in the public and private sectors, adopting this model has numerous benefits. It enables them to:

What about regulatory challenges?

Personal data protection law and cyber security legislation and regulations are impacting cloud adoption in heavily regulated sectors. Demands for local data centers have increased to address compliance and data residency challenges. Bringing data centers closer to the user also reduces the distance in which the data has to travel which significantly reduces latency and thus increases quality and customer satisfaction.

To date, there are 22 registered cloud service providers in Saudi Arabia, according to the Communications and Information Technology Commission. Nine more cloud service providers are under the qualification process. The region is also witnessing increasing penetration of leading global hyperscale cloud providers including AWS in Bahrain, Azure in Emirates, Google Cloud in Qatar and recently announced in Saudi Arabia by 2023. Google will deploy and operate the cloud region in Saudi Arabia with its local strategic reseller and distributor, CNTXT. 

What is a cloud adoption framework?

All the leading cloud service providers offer an adoption framework to guide organizations through their cloud adoption journey and mitigate possible risks. These frameworks offer a baseline to start with when migrating to the cloud, using a standardized approach with actionable, best-practice-based instructions and clear and customizable documentation for each step of the journey.

Google, for example, offers a cloud adoption framework designed to build a structure on the rubric of people, technology, and processes. The framework provides an assessment of where you are in your cloud adoption journey and actionable programs that get you to where you want to be. 

How can Google’s framework guide your cloud adoption?

The Google Cloud Adoption Framework takes a dynamic approach to help organizations navigate their cloud adoption journey. This framework operates around four themes and three phases, allowing organizations to measure their cloud maturity level by examining each theme within the context of the three phases. The four themes are:

For each of the above four themes, the assessed business practices will fall into one of the following three phases:

To support organizations’ data ecosystems and enable data-driven transformation, Google also provides several data cloud products with built in AI such as BigQuery, Cloud Spanner, Looker, and Vertex AI for ML. Google Cloud is open and standards-based. It offers best-in-class integration with open source standards and APIs and supports multi-cloud and hybrid environments. 

How do you measure an organization’s cloud readiness?

You can use Google Cloud’s Adoption Framework yourself to make an assessment of your organization’s readiness for the cloud. Google provides a free Cloud Maturity Assessment tool part of Google Cloud Adoption Framework to help you determine where you are on your cloud journey today, plan your progress, and mature your cloud capabilities. It does this by assessing your current business practices along the four themes – Learn, Lead, Scale and Secure – and providing recommendations on how to move from the tactical phase to the strategic and ultimately to the transformational phase of cloud maturity.

If you’d like a partner on that journey, CNTXT is here for you. Founded in 2022 and based in Saudi Arabia, CNTXT is a joint venture between Saudi Aramco and Norwegian software firm Cognite. Delivering premium cloud and digital transformation services in MENA, CNTXT’s digital offerings include Google Cloud and Cognite Data Fusion, enabling customers to achieve greater efficiency, sustainability, and profitability throughout their digital transformation journeys. To get started, talk to us today.

The robotics revolution is here

While robots have long been present in industrial settings, we have reached an inflection point where organizations are moving from proof-of-concept to full-scale implementation. This transition has the potential to revolutionize the way industries operate. Companies that do not embrace this transformation run the risk of falling behind in the pursuit of efficiency, productivity, and competitiveness.

How have we reached this point? Simply – advancements in technology. Robots are no longer limited to performing repetitive, simple tasks: they are becoming increasingly capable of complex operations, learning from data, and adapting to dynamic environments. Cutting-edge technologies like artificial intelligence, machine learning, and computer vision enable robots to perceive, analyze, and respond to their surroundings with greater precision and efficiency.

Balancing the scales on cost-effectiveness

As technology improves, the cost of implementing robotics solutions is decreasing. The initial investments required for deploying robots in industrial settings are becoming more affordable, making them accessible to a broader range of companies. Additionally, the return on investment is becoming more evident as robots offer increased productivity, reduced downtime, improved product quality, and enhanced safety standards.

The cost-effectiveness of robot scale-up makes it an attractive proposition for forward-thinking companies.

Plugging the gaps on labor shortages

Industries across the globe are facing a shortage of skilled labor, an aging workforce and rising labor costs. Embracing robotics offers a viable solution to address these challenges. By automating repetitive and physically demanding tasks, robots can alleviate the burden on human workers, reduce the risk of injuries and fill the gaps in labor shortages. This allows employees to focus on higher-value activities that require creativity, problem-solving and critical thinking.

Boosting productivity and efficiency

As well as increased safety, the potential for improved productivity and efficiency is one of the primary drivers behind the move toward robot scale-up. Robots can operate around the clock, with consistent precision and speed, without needing breaks or rest. They can handle repetitive tasks with high accuracy, minimizing errors and waste. What’s more, robots can be easily reprogrammed and reconfigured to adapt to changing production demands, ensuring optimal resource utilization.

Securing a competitive advantage

As every industrial organization knows, you have to continuously innovate to gain a competitive edge in today’s highly competitive market. Embracing robotics at scale provides an opportunity to streamline operations, increase production capacity, enhance product quality and reduce time-to-market. Companies that embrace this technological shift can establish themselves as industry leaders, while those that resist risk falling behind their more agile and adaptable competitors.

Robots are changing the game – are you ready?

The industrial sector finds itself at a transformative inflection point, where the scale-up of robots is becoming a game-changer. Technological advancements, cost-effectiveness, labor challenges, and the promise of enhanced productivity all contribute to the urgency of embracing robots on a larger scale. Companies that fail to recognize and seize this opportunity risk being left behind in an increasingly competitive landscape.

To thrive in the future, businesses must embrace the power of robotics and leverage its potential to optimize operations, unlock new efficiencies, and secure a strong position in the industrial sector. Ready to take action on your industrial robotics journey? Reach out to CNTXT’s experts today.

Automated factories – the future of manufacturing?

I recently had the honor of participating in a workshop titled ‘‘Accelerating the Impact of Industrial IoT for Small and Medium-Sized Enterprises (SMEs),’’ with the Center for the Fourth Industrial Revolution (C4IR) represented by Dr.Ibrahim ALShunaifi. The collaborative workshop was hosted by the advisor to the Minister of Industry and Mineral Resources, Dr. Majid Algwaiz, to identify the challenges and opportunities related to the vision of automating 4,000 factories.

It was amazing to see the engagement across the different stakeholders at the table, from the technology vendors, the manufacturing companies, and the representatives of the regulators.

What are the challenges?

While the ambition level is there, there is significant work to be done to enable manufacturing companies to adopt new technologies and move up the digital maturity ladder toward automation.

My key takeaways from the workshop are:

Strategies for success

Automating 4,000 manufacturing facilities is an impressive ambition. For Saudi Arabia to succeed in this ambition, I would propose the following strategic initiative: Establish and fund an alliance of companies that can deliver Smart Factory-as-a-Service to accelerate digitalization for small and medium sized industrial enterprise

This alliance should deliver four core services:

  1. Value engineering of digital use-cases
  2. Vertically scalable solutions and use-cases
  3. A manufacturing data model that can support scaling powered by a DataOps platform
  4. Connectivity services, both connecting brownfield sensors and retrofitting sensors to equipment

In addition to accelerating the digital journey of SMEs in Saudi Arabia by alleviating these first barriers to success, this initiative can potentially empower a fundamental change in business models in industry by enabling data sharing between equipment vendors and operators.

The need for a shift in business models

Given the constraints above, it is clear that moving from calendar-based and incident-driven maintenance to predictive smart maintenance will not be driven by SMEs themselves. It requires a shift in business models and incentive structures between actors in the value chain.

Equipment manufacturers have the deepest knowledge of the equipment. However, they are not incentivized or able to optimize maintenance. If they had access to data supported by regulation and a business model that pays based on measurable performance, they would be more inclined to invest in centralized expert ML/AI teams to optimize operations.

The “as a Service” revolution that changed consumer software forever will eventually hit industrial companies. We are already seeing the first few examples of pumping- or rotation-as-a-service sold by the equipment manufacturers, and it is an obvious win/win.

Making the initial investment in a centralized effort for smart-factory-as-a-service would accelerate The Kingdom of Saudi Arabia towards Vision 2030 by significantly lowering the cost, and effort, of entry into the journey towards the fourth industrial revolution for SMEs in manufacturing.

Achieving hyperscale is essential to Vision 2030

Hyperscale is a term we will soon be hearing much more in the Kingdom of Saudi Arabia (KSA). It’s about reaching a massive scale in computing, typically through cloud services or other networking and internet solutions. We are now on the way to digitalizing the Kingdom, but hyperscale with Google Cloud means that we can advance 10 times faster than we do now.

I believe that our Vision 2030 is only possible with a hyperscaler like Google in the mix. It’s a bit like setting the foundation before you build – everything that comes after is dependent on it. Once the foundation is laid, an ecosystem can grow. But building an ecosystem is not the job of CNTXT alone. It takes a Kingdom-wide effort, with public and private sectors working together, to inject the digital possibilities and ignite innovation opportunities.

We’re taking a enabling role in our future

I see our role at CNTXT as that of an enabler. From healthcare, to education, to banking – cloud services are at the core of the inevitable transformation of every industry. It’s what will spark new solutions, innovative applications and better insights that lead to even smarter decisions for customers.

I have witnessed this firsthand as part of the digital transformation team at Aramco. We knew early on that digitalization was essential to our global competitiveness, our efficiency and our long-term sustainability. That’s why we began working with Cognite, using their software to extract efficiencies and uncover sustainability solutions to meet constantly rising targets for both production and climate. The combination of moving to the cloud and Cognite’s data platform was a game-changer for Aramco, which is exactly the kind of digital ‘win’ we want to spread across the Kingdom.

Digitalization isn’t a ‘big bang’

For companies that have not entered the digital realm, it can seem overwhelming. But true to KSA’s Vision 2030, we cannot do things like we’ve always done them. To modernize and become a truly digital global player, companies must be proactive, open-minded and ready to embrace change. First, it’s a mindset shift, and then comes the shift in the way of work. There is no ‘big bang’ when it comes to digitalization. Rather, it’s a constant and steady evolution, one that requires leaders at the top to sponsor the change and drive its momentum.

Three guidelines for digital transformation

For companies in KSA that are on the brink of transformation, with leaders who see the potential and are willing to invest in a long-term, digital future, I have three pieces of advice for you. This advice stems from my decades in Aramco, and our work to bring the cloud and transform this massive company into the digital energy giant it is today.

1. Embrace the cloud

If your IT team is tasked with maintaining your traditional infrastructure, they will lose focus on what matters for true, long-term success. As a company, you’re spending effort, time, and money on something that can now be done in a better, more efficient way. By moving your operations to the cloud, you redirect your focus to what matters most for the future of your company.

2. Embrace technology to drive new solutions

There is no shortage of new technology and a plethora of partners who are ready and willing to help you develop solutions that transform your business. From banking apps to digital healthcare solutions, technology can help hone your focus on serving your customers in better and more innovative ways. Partnership is essential to move in this direction, blending your industry-expertise with their technology prowess can be a perfect match.

3. Get more value out of your data

With a fully digital operation, suddenly you can see the bigger picture. The historical silos of information are erased, and now the data is connecting the dots. This can help you understand where there are inefficiencies, opportunities for collaboration, or even insights into who you can serve your customers in new and better ways.

Transformation is a journey, not a destination

I have seen transformation efforts succeed and I have seen some fail. And the ones that failed did not know where they were headed in the first place. Success requires clear objectives and goals, knowing where you are going and accepting that the journey will not be a short one. 

Digital transformation is not a project. It never stops. It’s a journey of constant improvement and innovation. After 2030, I believe that KSA will be a different place – a truly digital and modern Kingdom that knows how to use data and technology to continuously shape a better future.

When data turns into disaster

We live in a digital world where data is a critical constituent for business. But sometimes a disaster event affects good data.

Disaster may come in all forms and sizes and may happen due to multiple reasons: natural disasters, hardware failures, human errors (inadvertent or unauthorized modifications) or cybercrimes. Ultimately, any event that prevents a workload or system from fulfilling its business objectives in its primary location is classified as a disaster.

In Google Cloud’s Architecture Framework, the “Reliability” pillar provides sets of practices, guidelines and recommendations on how to architect and operate reliable services on Google Cloud. This helps customers to be prepared for disaster events.

Disaster recovery planning

When talking about disaster recovery and business continuity it can be easy to fall under the impression that these terms represent the same thing. Well, is there any difference?

The short answer — yes, there is! ​​Disaster recovery is a subset of business continuity planning.

Assume you are using Gmail for sending and receiving emails within and outside your organization. A disaster happened due to whatever reason, and all servers are not available. To give more illustrative examples of DR and BC, have a look at the below:

Disaster recovery is a part of business continuity planning, and both expressions are used as BCDR in the industry. Both answer the “what if a disaster happened?” question and together determine what steps you need to take to ensure business continuity.

Business continuity key metrics – RTO and RPO

Recovery Time Objective and Recovery Point Objective are two core parameters that must be considered when planning for BC:

RTO and RPO metrics can differ from one organization to another but should be defined with a business priority to ensure data availability. This can depend on several factors from an organization’s size to its business type, structure, existing in-house resources and other parameters. However, the smaller RTO and RTO values correspond to a higher cost in terms of resources spending, application complexity and operation.

The cost of disaster recovery solutions grows exponentially as RPO and RTO requirements get closer to zero. RTO and RPO values typically roll up into another metric: the service level objective. SLO is a key measurable element of an SLA, although SLAs and SLOs are often conflated:

An SLA can contain many SLOs. RTOs and RPOs are measurable and should be considered SLOs.

Why Google Cloud?

The cost associated with fulfilling RTO and RPO requirements when implementing DR can be highly reduced on Google Cloud compared to traditional on-premise, and easier to implement.

Many elements need to be considered when planning traditional on-premise DR, including:

The disadvantages of traditional on-premise DR include:

Google Cloud Platform helps in overcoming most if not all of these challenges and disadvantages. As well, GCP offers multiple tools and capabilities that allows organizations to efficiently plan their disaster recovery. Undoubtedly, there are certain benefits of DR in GCP, such as:

Common DR patterns

The diagram below shows the DR patterns that are considered on Google Cloud. Different RTO and RPOs indicate how readily a system can recover when something goes wrong.

From left to right, patterns become more resilient and more costly. The naming refers to data temperature and how ready it is to be used by compute infrastructure on the secondary region (or zone).

Pervasive HA is HA between regions with transparent failover and load balancing. Customers may use a different terminology, for example, Geo HA, Active/Active, Disaster Avoidance and Business Contingency Group.

Architecture of the DR Patterns

A deep dive into DR patterns and building blocks are beyond the scope of this post. For now, let’s look at examples of the architecture of the various DR patterns.

Cold DR

Below is an example of an architecture for cold DR patterns, moving a VM instance in a new zone (backup-and-restore).

The simplest approach to resilience with zonal resources and recovery through snapshots with sets of building blocks has been selected to perform zone DR with zonal disks and snapshots:

Recovery operations are performed by:

Synchronous replication between dual zone (active-standby) setup is used to achieve zone DR.

Warm DR

To improve RPO, regional resources can be leveraged to avoid snapshot-based restore operations. This could require:

Depending on the setup this pattern, recovery operations are performed by:

Hot DR

Synchronous replication between dual zone (active-active) setup is used to achieve zone DR.

In order to improve RPO and even leverage three zones, regional resources can be used to avoid snapshot-based restore operations. This could require:

Depending on the setup this pattern, recovery operations are performed by:

To achieve dual region resilience combined with synchronous and asynchronous replication, this could require:

Recovery operations are performed by:

Pervasive HA

In the journey to fully leverage cloud-native features, there is the possibility of moving from a recovery to a disaster-avoidance mentality. This leverages cloud constructs and active-active services where multi-region and multi-zone resilience can be enabled:

This setup would minimize or avoid manual recovery steps, achieving HA regardless of the failure radius, by leveraging:

Final words

Disaster events pose a threat to your workload availability but, by using Google Cloud services, you can mitigate or remove these threats. By first understanding business requirements for your workload, you can choose an appropriate DR pattern. Then, using Google Cloud services, you can design an architecture that achieves the recovery time and recovery point objectives your business needs.