Navigating Data Enablement: A Strategic Approach for CIOs and IT Directors

It seems that with every new technology trend, many CIOs and IT Directors are prodded by their leadership and peers to jump on it quickly. But focusing on any emerging technology without a clear purpose can yield underwhelming results. As McKinsey & Company found in a recent survey, only 15 percent of companies using gen AI say it has meaningfully impacted earnings before interest and taxes (EBIT). The same could be said of any projects that seek to improve decision-making by combining data in new ways, including through Big Data or the Internet of Things (IoT).

Data enablement is the process of ensuring data is readily accessible, accurate, and usable for decision-making across an organization. It involves gathering, unifying, cleansing, and securing data, while also establishing processes to maintain its quality over time. This approach allows organizations to harness data strategically, driving innovation, improving efficiency, and supporting business objectives with actionable insights. Data enablement turns raw information into a key asset for achieving long-term success.

Establishing a Strong Data Foundation for Projects

Without clearly established objectives, defining the data needs of any project is nearly impossible. Defining and preparing the data properly sets a strong data foundation for projects and ensures the best results. Here are the top five areas to address:

• Data Collection and Unification: Devise a robust strategy for gathering and unifying data from various sources. Consolidate data sources and ensure the data is structured, organized and accessible. This step can significantly streamline the integration process and provide a strong data foundation for the project. The goal is to build a profile that recognizes where data points reflect the same customer, product, brand, and more.
  • First, determine the intersection points of the data. Then determine how to measure and collect data at each touchpoint.
  • Unification occurs when the data points create a complete picture of your customer, product, brand, and more.
  • Unification discloses the customer or product journey across the entire organization, not just within a particular department.
• Data Cleansing: Ensure the data is clean and free from errors, duplicates, and inconsistencies. High-quality data is essential for accurate and reliable outputs.
  • A cardinal rule in the world of IT is that the quality of outcomes is directly proportional to the quality of the data.
  • Before implementation, ensure that the data is reliable, complete, secure, trustworthy, understandable, and actionable.
  • A robust data foundation is the bedrock of optimal results. It prevents the infamous 'garbage in, garbage out' scenario which compromises the efficacy of applications.
• Data Annotation: Label the data appropriately to reveal context and relationships.
  • Data that represents the highest reward and lowest risk is the best type for pilot or early-stage projects.
• Data Security and Privacy: Protect sensitive data and comply with relevant regulations. Implement robust security measures to safeguard data from breaches and misuse. Organizations need to consider the data implications in three specific areas:
  • Identify and prioritize security risks to the enterprise’s proprietary data.
  • Manage Personally Identifiable Information (PII).
  • Track regulatory changes closely.
• Data Quality Assurance: Continuously monitor and maintain the quality of the data. Regular audits and updates ensure the data remains relevant and accurate over time. Data quality has always been an important issue for organizations. But the scale and scope of data that up-and-coming technology models rely on have made the ‘garbage in/garbage out’ truism much more consequential and expensive. Organizations need to do three things to ensure data quality:
  • Extend data observability programs for applications to better spot quality issues.
  • Develop interventions across the data life cycle to fix issues teams find. Focus on source data, preprocessing, and outputs.
  • Establish a feedback and resolution loop. When any of the above areas recognize a pattern of error, proper groups must be brought in to validate and create a fix to the underlying issue.

To Recap

A strong foundation starts with data. Here are five essential steps for successful data enablement:

• Data Collection and Unification: Starting from a foundation of consistent, unified data can determine success or failure of any project.
• Data Cleansing: Reliable data is critical for accurate outputs and prevents the 'garbage in, garbage out' scenario.
• Data Annotation: Properly label and categorize data to understand its context and relationships. Choose the most valuable data for pilot projects to mitigate risk.
• Data Security and Privacy: Implement robust measures to protect sensitive data and comply with regulations. Regularly update strategies to address emerging threats and regulatory changes.
• Data Quality Assurance: Continuously monitor and maintain data quality through regular audits and updates. Expand the data observability programs and establish feedback loops to address issues proactively.

By focusing on these core areas, CIOs and IT Directors can set their organizations up for success in leveraging new technologies. It’s not just about adopting the latest trends. It’s about doing so with a strategic, data-driven approach that aligns with business goals.