Before digital transformation, data took a back seat to experience and intuition. Organizations that let data drive decisions are reaping the business value.
Many organizations have woken up and come to recognize data as a critical asset that can contribute significantly to advancing the business plan. The prerequisite to capturing value from corporate and external digital data is digital transformation that helps turn data into information. Accurate and complete information adds value by optimizing operations, improving decision-making, and building competitive advantages.
In this article, we’ll explore how digital transformation enhances the business value of information for engineers and other disciplines. We’ll compare how engineers and managers sought business value before and after digital transformation, and how the latter’s emphasis on concrete data is proving superior.
Operational efficiency
Before digital transformation, the experience of senior staff and informal institutional knowledge, rather than data, guided decision-making to achieve operational efficiency.
Digital transformation supports automation and process optimization, consistently improving operational efficiency and quality. Efficiency success depends on information that streamlines business processes, reduces manual tasks and enhances staff productivity. Digital data, improved processes and more capable staff lower operational costs and increase business value.
Strategic decision-making
Before digital transformation, strategic decision-making was heavily influenced by general trends, me-too thinking, personal hunches and relationships, sometimes with disastrous results.
Digital transformation gives executives and decision-makers the tools and information they need to make strategic decisions. Access to diverse internal and external data sources, predictive analytics and scenario modelling enables organizations to make informed choices about the future.
Cost reduction
Before digital transformation, cost reduction depended on individual engineers’ and middle management’s leadership and determination to implement the opportunities they directly observed.
Digital transformation can reduce costs and waste by acting on insights identified through data analytics. Digital transformation can also reduce costs associated with traditional data management, such as:
- Physical document storage and retrieval
- Error-prone manual data entry
- Effort-consuming repetitive administrative and production tasks
Together, these cost reductions:
- Produce value from corporate data
- Respond to competitive pressure to reduce costs
- Increase sales margins
- Free up resources that can be reinvested in more profitable aspects of the business
Remote work enablement
Before digital transformation, remote work offered limited functionality and poor ease of use.
Digital transformation facilitates remote work and collaboration for engineers and other disciplines. These capabilities became essential during the COVID-19 pandemic and are now routinely expected workplace features that ensure information is accessible to employees and partners, regardless of their physical location. They also improve disaster recovery and business resilience.
Innovation and new business models
Before digital transformation, innovation relied on educated guesses and expensive physical models and prototypes. Launching new business models and product lines was typically a high-risk initiative.
Digital transformation can lead engineers to discover new revenue streams, business models and risk reduction ideas. It fosters innovation by allowing engineers to experiment with reliable information, emerging technologies and sophisticated forecasting tools.
Supply chain optimization
Before digital transformation, supply chain management was based on the experience of senior staff and was constrained by long lead times. Responding to disruptions to minimize impacts on the flow of goods and components was difficult and expensive.
In today’s globalized business environment, digital transformation can enhance the value of information in supply chain management. Real-time data on inventory, product demand forecasts and logistics status can lead to more efficient supply chains. This reduces costs and improves customer satisfaction by minimizing delays and out-of-stock situations. In a significant disruption, digital data enabled supply chains to respond better by modelling the impact of alternative courses of action.
Customer feedback analysis
Before digital transformation, customer feedback analysis was limited to learning from surveys, direct observation and incidents where the organization disappointed customers.
Digital transformation helps organizations collect, analyze and act on customer feedback more effectively. Customer complaints, product returns, sentiment analysis and social media monitoring provide insights into customer opinions. Analyzing this information enables companies to adapt and improve their products and services.
Business agility
Before digital transformation, business agility was limited. Most often, changes in the business environment or internal crises resulted in bankruptcy or a forced business sale.
Digital transformation makes businesses more agile and adaptable. Supported by better information, businesses can respond more rapidly to:
- Market changes
- Shifting consumer preferences
- New technology opportunities
- Economic trends
- Unforeseen disruptions
Prerequisites to high-value information
Achieving business value from digital information requires organizations to implement the following information infrastructure elements that engineers can champion.
Data centralization and accessibility
Before digital transformation, data was hard to access. It was dispersed among paper filing systems, record management systems and multiple application-specific data silos. Data quality was uneven. Executives, including engineers, based decisions primarily on experience and hunches.
Digital transformation requires accessing disparate data sources or migrating that data into a centralized repository such as a data warehouse or data lakehouse that may be hosted in the cloud. This centralization greatly enhances data accessibility, allowing authorized engineers to retrieve information quickly and reliably for analysis and decision-making.
Advanced analytics and machine learning
Before digital transformation, data analytics, while helpful, was constrained by:
- Modest amounts of available digital data
- Limited depth and breadth of data that software could analyze
- Prohibitive cost and scarce computing resources
Digital transformation paves the way for implementing advanced analytics, generative AI, and machine learning models. These technologies can uncover hidden patterns and insights within data, facilitating predictive and prescriptive analytics. Businesses can make informed decisions, optimize processes and identify new opportunities, ultimately increasing the value of their information assets.
Improved data quality and accuracy
Before digital transformation, organizations paid too little attention to data quality. As a result, many data analytics efforts were preceded by significant data cleanup projects. Even then, the confidence in recommendations produced by data analytics was not high.
Through better data cleansing and integration tools, digital transformation helps improve the quality and accuracy of information. Redundant, misleading and erroneous data can be automatically corrected or removed, ensuring that decisions are based on reliable and clean data.
Improving data quality and accuracy depends on organizations adopting data stewardship processes emphasizing accuracy and completeness as a routine part of all business processes.
Data-driven culture
Before digital transformation, a data-driven culture was not feasible, too expensive or not considered valuable.
A data-driven culture is evident in the collective staff behaviors and values. They routinely practice and encourage the use of data to improve business performance. They reject hunches, gut feel, shoot-from-the-hip and flavor-of-the-month that were widespread before digital transformation. A robust data and analytics culture prioritizes:
- A focus on customer-centricity
- The relentless measurement of KPIs for continuous improvement
- Collaboration and consensus-based work
- Data-driven decision-making
Data literacy
Before digital transformation, data literacy received little attention.
Data literacy is the ability to understand and communicate data and insights derived from data. Organizations improve staff data literacy with a formal training program supported by hands-on experience. Organizations reinforce data literacy by emphasizing data analytics in the preparation of recommendations.
Digital transformation is a powerful enabler for enhancing the business value of information.
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Yogi Schulz has over 40 years of Information Technology experience in various industries. He writes for ITWorldCanada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.
