Deloitte Middle East Tech Trends 2021 report provides insights and inspiration for the digital journey ahead and the technologies that will drive new plans during the next two years and beyond.
Everyone has access to similar technologies, but it’s how a business uses them that gives them a competitive advantage. Needless to say, technological advances have made strategy development even more complex — the choices are more nuanced and intertwined. How business strategy is engineered to modernise core assets and evolving supply chains into value enablers, how new data optimisation techniques will turbocharge machine learning and zero trust will revolutionise cybersecurity architecture to protect that data, technology will enable the fundamental business processes.
Deloitte Middle East Tech Trends 2021 report identified insights around a strategy to empower technology leaders and business leaders.
Here are the trends that are likely to transform businesses in the next two years.
Strategy, engineered
Savvy corporate strategists are looking beyond their organisation’s current technological capabilities and competitive landscape to consider a broader range of future possibilities about how technology can expand where they play and how they win. In fact, in a Deloitte-Wall Street Journal Intelligence survey, 40 per cent of CEOs said their CIO or tech leader would be the key driver of business strategy — more than the CFO, COO, and CMO combined.
Strategists are turning to strategic technology platforms equipped with advanced analytics, automation, and AI. Organisations are using these tools to continually identify internal and external strategic forces, inform strategic decisions, and monitor outcomes. As a result, companies are transforming strategy development from an infrequent, time-consuming process to one that’s continuous and dynamic, helping strategists think more expansively and creatively about the wide range of future possibilities.
- Tech-savvy C-suite. C-suite executives and board members should have a broad understanding of the critical technologies in which the company is, or should be, investing to gain competitive advantage and to build resilience against disruption.
- Business-savvy tech leaders. IT leaders and technologists should be engaged in strategy development processes and education that gives them a broad understanding of the business and its strategic objectives.
- Aligned technology and partners. Effective organisations choose their technology platforms and ecosystem partners carefully, aligning their choices and implementation decisions with their strategic goals.
Also Read: The Grand CMO, CIO Alliance
Core revival
Modernising legacy enterprise systems and migrating them to the cloud may help unleash an organisation’s digital potential. But for many, the cost of needed cloud migrations and other core modernisation strategies can be prohibitive. This is about to change. Some pioneering companies are beginning to use clever outsourcing arrangements to reengineer traditional business cases for core modernisation. Likewise, some are exploring opportunities to shift core assets to increasingly powerful platforms, including low-code options.
Finally, many are advancing their platform-first strategies by addressing technical debt in ERP systems and migrating nonessential capabilities to other platforms. In a business climate defined by historic uncertainty, these innovative approaches for extracting more value from legacy core assets may soon become standard components of every CIO’s digital transformation playbook.
Cargill developed a single, unified platform called Maestro for Cargill’s strategic sourcing department, an internal operation that accounts for more than $5 billion in indirect spend annually. The investment in Maestro — a foundational platform that replaced dozens of systems — has modernised its sourcing operation. Cargill achieved significant sourcing improvements over the last few years that are improving the company’s overall results.
Supply unchained
Long considered a cost of doing business, supply chains are moving out of the back office and onto the value-enabling front lines of customer segmentation and product differentiation. Future-focused manufacturers, retailers, distributors, and others are exploring ways to transform the supply chain cost centre into a customer-focused driver of value. They are extracting more value from the data they collect, analyse, and share across their supply networks. Some of these organisations are exploring opportunities to use robots, drones, and advanced image recognition to make physical supply chain interactions more efficient, effective, and safe for employees.
Transforming established supply chains into resilient, customer-focused supply networks will be a challenge, and for most organisations, it will be an ongoing journey. Over the next 18 to 24 months, organisations will take the following steps to capture and analyse more data:
- Leverage IT/OT convergence. The same smart factory applications and Industrial Internet of Things (IIoT) sensor technologies that marry IT networking with operational technology software and machines on the factory floor are finding new applications in smart warehouses, logistics, and sourcing. Aggregating real-time operational data from these and other supply chain functions into a commonly shared data platform enhances end-to-end transparency, live metrics that support human and machine-based decision- making, and operational efficiency. In addition to IIoT sensors, visual, acoustic, and temperature monitoring tools can generate unstructured and nontraditional data streams that, once digitised and analysed, can help maintenance teams identify anomalies and perform predictive maintenance.
- Boost data capabilities at the edge. Time-sensitive data can become essentially valueless after it is generated, often within milliseconds. Therefore, the speed at which organisations can convert data into insights and then into action across their supply chains is often mission critical. Edge computing can turbocharge this process by moving processing and storage capacity closer to the source of data. In this distributed architecture model, data does not have to go to the core or cloud for processing, analysis, and dissemination. For example, digital data generated at the point of manufacture or sale can be analysed in the moment, its insights then disseminated in real time from the edge directly to disparate pockets within the supply chain ecosystem that may not have their own analytics and compute capabilities.
Also Read: Top 6 Retail Tech Trends in 2021
MLOps: Industrialised AI
Enterprises are realising the need to shift to engineered performance to efficiently move ML models from development through to production and management. However, many are hamstrung in their efforts by clunky development and deployment processes that stifle experimentation and hinder collaboration among product teams, operational staff, and data scientists. As AI and ML mature, a strong dose of engineering and operational discipline can help organisations overcome these obstacles and efficiently scale AI to enable business transformation.
To realise the broader, transformative benefits of AI and ML, the era of artisanal AI must give way to one of automated, industrialised insights. Enter MLOps: it is an approach that marries and automates ML model development and operations, aiming to accelerate the entire model life cycle process. MLOps helps drive business value by fast-tracking the experimentation process and development pipeline, improving the quality of model production and makes it easier to monitor and maintain production models and manage regulatory requirements.
MLOps can help organisations by establishing and enforcing program-level guardrails that can drive accountability as a baseline requirement. Within a robust MLOps framework, development and deployment teams will find it easier to adhere to governance and compliance protocols and privacy and security regulations. Similarly, programmatic traceability standards can help ensure model transparency. MLOps tools can automatically record and store information about how data is used, when models were deployed and recalibrated and by whom, and why changes were made. Without MLOps procedures in place, it would be infeasible, if not impossible, to prove proper data handling or use in response to an external inquiry.
Machine data revolution
With machine learning poised to overhaul enterprise operations and decision-making, a growing number of AI pioneers are realising that legacy data models and infrastructure — all designed to support decision-making by humans, not machines — could be a roadblock to ML success. In response, these organisations are taking steps to disrupt the data management value chain from end to end. As part of a growing trend, they are deploying new technologies and approaches including advanced data capture and structuring capabilities, analytics to identify connections among random data, and next-generation cloud-based data stores to support complex modelling. Together, these tools and techniques can help organisations turn growing volumes of data into a future-ready foundation for a new era in which machines will not only augment human decision-making but make real-time and at-scale decisions that humans cannot.
In terms of storage, organisations are becoming less focused on storing clean data that fits neatly into tables, rows, and columns. To feed ML algorithms and advanced analytics tools, many are exploring opportunities to store massive volumes of unstructured data from IoT, social media, and AI in a variety of modern database technologies, including:
- Cloud data warehouses. The cloud-based data warehouse, which a growing array of major and emerging public cloud vendors are offering as a service, aggregates data from disparate sources across an enterprise and makes it available to users for real-time processing and mining. This permissions-based, centralised system eliminates the need for colocated data and data pipelines.
- Feature stores. In the near future, it will be commonplace for an organisation to have hundreds or thousands of data models operating independently of each other, and in parallel. Each of these models will use different feature sets. Feature stores provide a mechanism for allocating compute, sharing features, and managing data efficiently.
• Time series databases. The popularity of time series database technologies has grown considerably over the last two years, with good reason. Unlike relational databases that record each change to data as an update, time series databases track and record them — and the specific time they were made — as a unique insert into a dataset. - Graph databases. As data grows more voluminous and less structured, the number of relationships and interconnections increases exponentially, thus becoming unmanageable (and unsearchable) in traditional database models. Graph databases are designed specifically to address this challenge by storing not only data but information on each data point’s relationships in a native way. With this model, queries about complex relationships among data can be fast, efficient, and more accurate.
Zero Trust
Zero trust is rooted in the concept that modern enterprise environments necessitate a different approach to security: There’s no longer a defined perimeter inside which every user, workload, device, and network is inherently trusted. In zero trust architectures, every access request should be validated based on all available data points, including user identity, device, location, and other variables that provide context to each connection and allow more nuanced, risk-based decisions.
Data, applications, workloads, and other resources are treated as individual, manageable units to contain breaches, and access is provided based on the principle of least privilege.
The automation and engineering required to properly implement zero trust security architectures can help strengthen security posture, simplify security management, improve end-user experience, and enable modern enterprise environments.
But the move to zero trust could require significant effort and planning, including addressing foundational cybersecurity issues, automating manual processes, and planning for transformational changes to the security organisation, the technology landscape, and the enterprise itself.
