Technology Alignment: The Key to Thriving in the Digital Age

In Uncategorized by Roger Lewis

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Every single day, an avalanche of interactions and operations introduces 328.77 million terabytes of data in the digital domain. To put that in perspective, this digit-packed figure is as grand as 328,770,000,000,000 bytes.

Yet, having a lot of data means nothing if we do not know how to manage it and integrate its capabilities.

Why Should We Treat Data As Products?

Various data products cater to distinct “consumers,” similar to how software suits users with varying operating systems. With global intangible assets exceeding $200 trillion, the true worth of unreported data assets becomes tempting.

Data consumers, primarily systems rather than individuals, fall into five consumption archetypes, each serving a specific data purpose.

Digital Applications: Specific, formatted data delivered at specific intervals.

Advanced Analytics Systems: Data suitable for machine learning and AI processing, necessitating particular engineering.

Reporting Systems: Governed data for audited use in dashboards or compliance activities, with a shift toward self-service models and real-time updates.

Discovery Sandboxes: Ideal for exploring raw and aggregated data, these support data scientists and engineers in uncovering new use cases.

External Data-Sharing Systems: Stricter data-sharing policies govern these, such as banks sharing fraud insights or retailers collaborating with suppliers.

Aligning Technology With Business Goals

Hendrith Vanlon Smith Jr. once said, “In this new age where data is so abundant, our task as a civilisation now is effective beneficial utilisation. The challenge now is doing good things with that data—things that make our lives and the lives of future generations of people more fulfilling, joyful and prosperous.”

Forward-thinking companies could embrace a digitalisation strategy to transform their operations using new technologies. Nonetheless, a digital strategy emphasising enhanced services might demand a revised or entirely new business plan.

Business goals, strategy and model are tightly interwoven and involve high-level decision makers impacting stakeholders. Here are some steps to consider.

Review Status Quo: Before adopting new processes, assess current IT maturity by focusing on budget, assets and structure.

Spot Innovation Barriers: Identify and address hurdles to change, considering budget, systems and expertise.

Rank Technological Tasks: With limited resources and ongoing alignment demands, organise tech tasks based on urgency and significance.

Assess Tech Alternatives: Choose suitable tech options by gauging their compatibility with existing systems and potential implementation hurdles.

Map Migration: Formulate a flexible plan detailing steps, deliverables, timelines and contingency measures for tech migration.

Fostering A Culture Of Innovation

About 85% of executives say innovation is a top priority, but only 6% are satisfied with their performance. To cultivate a progressive and innovative company culture, leaders should actively promote transparency, setting an example by sharing personal failures and fostering an atmosphere where mistakes are constructively discussed.

However, this entails a paradigm shift where failures are viewed not as setbacks but as valuable learning opportunities, encouraging a deeper understanding and formulation of preventive strategies.

Instead of glorifying end results, we should applaud continuous efforts and growth, normalise the inevitable stumbles and strengthen a medium that champions innovation and resilience.

Integrate AI & Advanced Analytics

Constant connectivity and learning will become the norm. AI and advanced analytics will decipher what appears random, revealing patterns that can be further deployed by businesses. In turn, this will build a future of personalised experiences, challenging traditional norms and driving efficiency.

Faster Data Preparation: Data preparation is traditionally time-intensive—AI expedites this by automating processes like extraction and transformation, ensuring readily usable high-quality data.

Improved Accuracy: AI identifies patterns and relationships with greater precision. Machine learning, depending on its construction, can process vast computations simultaneously.

Boosts Predictive Analytics: Predictive analytics, analysing present data to forecast future trends, is paramount for informed decisions, and AI and advanced analytics can do that in seconds!

Sector-Specific Benefits: AI analytics is invaluable for e-commerce, fintech and telecom sectors. It identifies trends, offers real-time predictions, enhances security and diminishes churn rates.

All companies should remember that data is the gold of the 21st century—and those who know how to deploy it will be the winners of the following decades!

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