Navigating the Storm: Key Technological Skills to Master

In addition to building resilience in their business model, successfully navigating the turmoil requires maturity and mastery of technology skills in three key areas, shares Shanker V Selvadurai, VP & CTO at IBM. Discover why data-driven insights, automation at scale, and securing every touchpoint are critical to success.

Something unexpected happened more than two years ago. The COVID-19 pandemic has accelerated the digital transformation that is essential for businesses and organizations to survive during the crisis. This year, as the macroeconomic and geopolitical environment is rapidly changing, business leaders face a confluence of structural uncertainties – supply chain disruptions, labor and skills shortages, rising energy prices and inflationary pressures – that are mutually reinforcing. Organizations large and small are caught in a potentially long and perfect storm. It will be crucial to continue the digital transformation that has benefited them during the pandemic.

With increasing market volatility and competitive pressures, access to the right data at the right time is critical to anticipating trends, mitigating risk and capitalizing on new opportunities. However, complete, current and accurate data is not fully accessible in most organizations. Using incorrect data can lead to legal action, compromised security or biased decisions with serious consequences.

See more: Decision Intelligence and the Future Beyond BI

Preparing for our hybrid multi-cloud reality

Corresponding Capgemini, The Data-Driven Enterprise, 2021, 73% of executives are dissatisfied with their data quality, and 61% of organizations are unable to leverage data to create sustainable competitive advantage. Real-time decisions and actions require data that is hosted in multiple locations, stored in different formats, and sometimes only available through intermediaries under restrictive conditions. Data proliferation in a hybrid and multi-cloud environment is a reality for most organizations. In addition, increasing nationalism around the world is leading to stricter regulatory requirements for data sovereignty and residency.

Addressing these challenges requires rethinking where and how data is collected, processed and stored. A data management architecture is needed that supports consistent data capabilities across the enterprise network—on-premises and across multiple cloud environments. The right data should be available on the tap at the right time and in the right place. There should be no costly movement of data to perform analysis; Different lines of business should own and manage their own data without having to consolidate platforms, and decisions can be made in real-time and closer to the point of interaction or interaction. Rather than point-to-point connections or costly and complex single-hub style data collection, a data structure “straddles” a virtual or logical mesh across disparate and hybrid data sources. The virtual network is built by an intelligent knowledge catalog using active metadata, knowledge graphs, semantics and machine learning. Centrally managed but distributed across the landscape, the catalog orchestrates when and how data is delivered and what is visible to whom to ensure privacy and trust.

Tackle forecast complexities with the right skills

Digital acceleration during the pandemic also caused companies to accumulate more technical debt as they needed to act quickly. Fragmented digitization in a disaggregated IT landscape has increased complexity not only through isolated data, but also through separate processes throughout the application inventory. Corresponding I.D.C, 83% of companies estimate that they will have up to 1,000 applications in their portfolio in five years. It is becoming increasingly difficult for people to effectively manage this complexity on their own. In addition, the workforce is also significantly affected by the Big Layoff, as are the millions of workers who are struggling with the long-term effects of the virus. Employers and workers must grapple with challenges related to immigration disruption, changing worker expectations and mental health issues. This labor volatility directly impacts business productivity and economic growth.

Organizations building capabilities through intelligent automation are poised to address the risks associated with job insecurity and talent supply. The infusion of AI enables automation to scale in industries that are forced to rely on a reduced workforce or intentionally try to minimize human interaction. Intelligent automation systems understand signals from data in a similar way to humans, but can process data much faster. They can also learn from interactions and act accordingly to automate workflows that link processes end-to-end, spanning silos and overlapping functions to unveil new results that differentiate a company from its competitors. The IBM Institute for Business Value estimates that 80% of early adopters of intelligent automation will significantly outperform their competitors. Automated workflows impact revenue by enabling organizations to seamlessly and consistently meet customer demands, especially during times of uncertainty.

When the pandemic began, millions of employees began and continue to work from home. Each remote or hybrid worker who connects remotely expands an organization’s attack surface and creates new openings for threat actors. The increasing digitization of products and services by organizations during this period has added more potential entry points for cybercriminals. A growing number of isolated members are accessing resources—employees, suppliers, partners, and customers—all using multiple devices from distributed and remote locations. Perimeter-centric security models cannot handle the dynamic nature of this incredibly large attack surface. In addition, these attacks move quickly, infecting computers within seconds. The problem is compounded by multiple security tools in the organization generating alerts on the same attack, making the incident difficult to understand.

Organizations need to modernize their threat detection and response capabilities. Silos must be eliminated to gain visibility across data sources – from cloud to core systems. Workflows need to be unified without having to switch between tools. A Zero Trust framework is required to help organizations manage the risks of a disconnected business environment while allowing users access to the appropriate resources. This approach uses context to securely connect the right users to the right data, at the right time, under the right conditions, while protecting the organization from cyber threats. AI and analytics continually validate connections between users, data, and resources. That IBM, Cost of a Data Breach report, 2022found that organizations that have adopted Zero Trust save an average of nearly $1 million in data breach costs compared to organizations that have not implemented Zero Trust.

See More: What Is Data Transformation? Types, Tools and Importance

Insights from the pandemic

The COVID-19 pandemic has taught us that the digital economy can offer significant growth opportunities for businesses and provide resilience against threats to the traditional customer journey. strengthen core Data, automation and security Skills will be critical for businesses to better serve customers and find new sources of value in times of change and uncertainty.

These competencies also form the basis for accelerating innovation by leveraging other exponential technologies such as 5G, Edge Computing, Web3 and Quantum Computing. And they are just as important to support new business needs as sustainability challenges. These are extraordinary times, and it’s important that we consciously and purposefully rethink architectural and technological choices, not just to weather the storm, but to come out of it better and stronger.

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