Unlocking Digital Innovation Through A Succesful Data Strategy
I was surprised to see my previous article, "To Add Value To Your Business, Don't Treat Data And AI As Islands," received an excellent reception. Since its publication, I have had the pleasure of meeting several chief data officers (CDO) from organizations based in Latin America and discussing the recommendation for a data governance framework to facilitate decision-making. Today, I want to write about how an enterprise-ready data strategy can help organizations unlock their digital innovation.
According to research from Harvard Business Review Analytics Services sponsored by Microsoft, 55% of business leaders report data silos and data management difficulties as roadblocks. An Accenture study found that 64% of companies surveyed haven't seen the ROI from their digital investments. For more roadblocks, please read the article "13 Common Mistakes That Can Derail Your AI Initiatives," written by my peers in the Forbes Tech Council. Companies should aim to adopt an approach where their data is fully governed, discoverable, trusted and well-connected.
From my perspective, a modern data strategy should help organizations:
1. Solve business problems: Start by understanding what the business units want to achieve with their data and facilitate the identification of the fundamental business problems to solve through an ROI-driven strategy.
2. Promote discovery and collection: Break down the data silos by bringing together internal and external data sources that provide the highest business value based on its ability to execute.
3. Facilitate data understanding: Enable the organization to deeply understand data across their business value chain and apply analytics to extract business-critical insights.
4. Motivate business value translation: IT leaders can turn data into value with a solid operating model by mapping high-value use cases to crucial business outcomes.
5. Accelerate culture transformation: Organizations should invest in developing data and analytics across the organization, making data available to everyone through self-service tools.
6. Cultivate responsible data governance: Develop a responsible data and AI-ready culture with responsible governance principles, practices, tools and technologies.
7. Ensure measurable outcomes: IT leaders should establish long-term/short-term goals and objectives that describe how data can help the organization archive measurable outcomes and business results.
8. Provide a feedback loop: Having a digital feedback loop can help organizations turn data from customers, products and people into intelligence powers and enable transformed experiences.
However, placing data at the heart of the organization presents challenges, and this is why IT leaders should start by answering the following questions about their current data state:
• How do end-users and business analysts find your data? Master data lives everywhere and nowhere.
• How do users and partners access this data? Is there a lack of clear business ownership?
• How old is the data? Is latency a fundamental problem?
• Why are related reports showing different data? Does your organization have a single source of truth? Is there a latency of data copies?
• How is your data secured, and are users compliant? Does your organization have various levels of governance? What is the risk of leakage potential?
• How can IT manage data more effectively? Is your organization leveraging cloud data services with new capabilities to exploit for data management?
• How does IT respond to changes faster, more accurately? Is IT getting data to the right people at the right time?
Another vital consideration is investing in developing or acquiring a unified data governance service to enable your organization to discover its data quickly, derive meaning from it and maximize its business value. Something I like is having a unified map of the organization's data assets and their relationships for more effective governance.
To close, I want to suggest following three guiding principles when creating your data strategy:
• Preparation: Managing the system or governance. We need to optimize our investments in data with better preparation of data management practices. As a reminder, effective data management provides cost reduction effects, which is realized by reducing duplication of processing and storage. Also, better compliance reduces the risk of compliance breaches, such as the General Data Protection Regulation (GDPR) and Sarbanes-Oxley Act (SOX).
• Agility: Managing the container or architecture. We need to increase the skill of getting value from our data and improve the speed at which insights into business processes are generated. When building new applications or modernizing legacy systems, we should make sure we are infusing business processes with machine learning and AI automation, as well as decision support.
• Resilience: Managing the content or data lifecycle. We need to improve data discovery to support decision-making during sudden change. Don't forget that it is crucial to understand how to interpret data knowing where the data came from, that the data is relevant and that supporting data can be acquired accurately and timely.
IT leaders should ensure their data strategy supports transformational business objectives and drives critical business use cases and objectives and key results (OKRs) within the organization. An effective data strategy should put data at the center and provide the framework to unlock innovation with the least risk and highest ROI.
Author: Pablo Junco