When companies embark on grand investments in high-risk digital solutions, some considerations are critical to ensuring success. One of those is getting your digital house in order before layering on solutions that can lead to long-term costs. The goal for any extensive digital or AI transformation is ease of maintenance, scalability, and continual improvement.

When you scale without addressing the underlying problems, you merely scale those same problems into the ‘new’ solution.

“Garbage in, garbage out!” (GIGO) was first mentioned in a 1957 syndicated newspaper article about US Army mathematicians and their work with early computers. Nothing has changed; GIGO is even more critical today because of a reliance on data, our interconnected systems, and their global impacts.

As with a kitchen renovation, you will remove the old cabinets before adding the new ones. I recommend cleaning your business house first rather than layering on more for a sizeable digital initiative!

Remove The Barriers to Success

Flawed, biased, or poor-quality information or data impacts efforts significantly due to the interconnectedness and interdependencies built into organizational systems today. As a leader in today’s dynamic business environment, sponsoring means asking all the right questions and removing barriers before you sign the go-ahead. Here are some considerations.

Eliminating Technical Debt

Technical debt describes the long-term costs arising from the consequences of moving too quickly on implementation and deployment. It includes legacy code, outdated libraries or dependencies, poor documentation, inconsistent standards, and flawed architecture. Technical debt harms the scalability and longevity of your solutions.

Dear reader, I am sure at least one of you has worked with a software development team forced to prioritize speed over quality under the pressure of leadership that lacked the knowledge about avoiding shortcuts and the criticality of performing a technical cleanse.

Assess and Invest in Cleanup

Before embarking on any new large-scale initiative, assessing and realistically reviewing the effort to clean up technical debt is critical. Sure, competition is a race, yet what long-term cost will you endure if you ignore technical debt? No one wants their legacy to be the fatal crashes of the Boeing 737 Max or Nokia and its massive decline or to absorb an $800M revenue loss and $140M penalty like Southwest Airlines by abandoning upgrades to their systems.

Data Quality and Availability

Data is the backbone of Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) platforms, Industrial Control Systems, and AI models, to name a few. Whether your solution is a customer communication tool using a large language model, performing predictive analysis, or analyzing data for pattern recognition or anomalies, data directly impacts performance, accuracy, and reliability. It doesn’t matter how good an AI algorithm is if the data quality is poor, inaccurate, irrelevant or unavailable.

Poor data quality and availability risk revenue and customer experience and may even impact your ability to react competitively to market changes.

Leaders intent on removing this barrier need to establish good data quality practices:

  • Establish good data governance with clear roles and responsibilities on data quality, security and compliance.
  • Cleanse the data by removing duplicates, errors and duplicate datasets.
  • Profile and organize the data by investigating and summarizing existing data and creating the best methods for storing and managing that data for analysis efficiency.
  • Ensure data accuracy, consistency, completeness, validity and timeliness by ensuring your data is correct, uniform and conform to required standards.
  • Segment and isolate data with well-established stewardship practices to ensure security and privacy using a framework for digital trust.
  • Consider data availability and make decisions on data mesh or data fabric approaches to break down silos to organizational data.

Security Considerations

Data and privacy breaches cost companies significant dollars in fines and penalties. Ignoring security planning at the beginning of your digital innovation or transformation efforts will likely come back and bite organizations later. CSO Online shares a comprehensive list of penalties and settlements from hacks, data thefts, weak security, and coverups, totalling nearly 4.4 billion and counting. These are avoidable issues if companies focus consideration on consumer protection, legal regulations, and compliance bodies. Violating data protection laws happens almost effortlessly when a company fails to invest in constantly reviewing the ever-changing rules in the countries, provinces/states, and areas in which they do business.

Data security that protects sensitive information not only costs penalties but also causes reputational damage and loss of consumer trust, which affect the bottom line. Regulatory bodies are taking data privacy seriously and are paying more attention to violations by organizations of all sizes within their reach.

Here are some considerations for executive leaders of digital innovation initiatives.

  • Types of security threats: Understand what threats exist and how and why they happen.
  • Regulatory Requirements: Gain insight into blurred global boundaries and what that means for your digital innovation.
  • Establish Security Governance: Invest in exploring physical security, corporate policies, and the role of governance in mitigating risk through development and maintenance.
  • Security Role with a Direct Line To the CEO: A CISO or Cybersecurity Specialist maintains the information security and risk management programs. Filter their messages through an IT director or CFO, and you risk missing the critical information you need to make decisions about digital trust.
  • Employee Training: Security is an ‘all business’ effort that requires investment in training, awareness, and advocacy to protect an organization’s information.
  • Recognize the difference between governance and stewardship: Companies govern their corporate data and are merely stewards of others’ personal data. These two types of data will require different types of protection.
  • Be Aware of How Your Solutions Pose Risks to the Public: If your innovation involves using Gen AI or other AI models in consumer-facing products through your company software, third-party software, or communication with consumers, you must understand the risks. Examples are risks posed by bias, inaccuracies, copyright/IP, biometric and personal data, poor customer service, and accusations of fraud or misuse. The European Commission’s Directorate General for Justice and Consumers and the US Federal Trade Commission take these very seriously. Other countries are following suit.

As an executive, your responsibility is to act with care and diligence for the organization, its employees, and the consumers of your products or services. Digital innovation is exciting, especially in these early days of discovering the potential of AI and leveraging today’s remarkable computing power. Make it your mission to invest time and money in the upfront work of asking questions about technical debt, data quality and availability, and security before embarking on a large-scale digital innovation.

Prioritize the well-being and privacy of those who depend on you to protect them from undue risks or security breaches before that initiative gets too far in development. You can start by assessing your readiness with the Digital Transformation Framework and then pulling the right people together to ask the necessary questions to avoid rework, delays, disaster, or regulatory penalties.

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