Developing a data modernization strategy? Answer these seven questions first

Three-time Indianapolis 500 winner Bobby Unser is quoted as saying, “Success is where preparation and opportunity meet.” The same holds true for artificial intelligence (AI). It presents an incredible business opportunity, but to harness its power, you need a thought-out plan.

Organizations that develop a data modernization strategy are best poised to maximize AI’s potential. They’ll anchor this strategy in four key areas: data governance and compliance, data architecture, data integration and accessibility and data analytics and reporting. But there are many other factors to consider.

For example, migrating your data “as is” isn’t the best approach if that means you’re simply moving data from legacy silos into cloud silos. After all, that data contains the business inputs AI needs to deliver insights. Busting your data siloes and integrating this data with data management systems can release the insights it contains.

But how do you tackle an undertaking like this that can seem insurmountable? To develop an effective data modernization strategy, answer these seven pivotal questions. This approach sets the stage for you to devise your data strategy and get your data AI-ready.

#1 – What data should I modernize and move to the cloud?

Focus on data that drives tangible business value. This can mean modernizing and migrating data that aligns with strategic business objectives. For instance, integrating customer demand data with marketing insights can revolutionize manufacturing strategies and inform HR processes.

This could also mean choosing data that contributes to initiatives that increase innovation and collaboration across departments or business units. After all, your cloud migration should drive enterprise-wide connection and more informed decision-making. Another good candidate is data that holds the most promise to unlock actionable insights when fed into an AI model.

With all these possibilities, the goal is to create a data lakehouse – cloud-based storage for structured, semi-structured and unstructured data – and move the data there. This enables a flow of information to transform every aspect of your organization, from personalized customer experiences to predictive maintenance.

#2 – How do I handle growing disparate data?

With projections for data volume growing at an astounding rate each year, organizations must adopt a strategic approach to manage the “three Vs” – volume, variety and velocity. The key is to go beyond merely storing this data to extracting the high-impact, valuable insights it contains. Traditional data warehouses, constrained by physical storage limitations, fall short in this new era.

The solution lies in cloud-based data lakehouses and management systems. These platforms offer the elasticity and scalability for handling massive volumes of diverse data, including real-time streams from Internet of Things (IoT) devices. They provide cost-effective storage, with options to archive less critical data to lower-cost tiers and purge outdated information to maintain efficiency.

However, storage capabilities are just the start. Focus on intelligent curation and utilization of data. Implement policies that prioritize data relevance and value – not all data warrants long-term retention. Use advanced analytics and AI to distill actionable insights from this data deluge. The goal is to transform your data into a strategic asset that drives innovation and efficiency.

#3 – Who is responsible for integrations  and analysis?

A collaborative team of trained data specialists, domain experts and strategic planners experienced in cloud data management best meet the need for effective cross-business-unit integrations, third-party integrations and analysis. Beyond this team, the role of a Chief Data Officer (CDO) is crucial.

Having the right professionals in place is a must. Otherwise, your data lakehouse could devolve into a data swamp cluttered with outdated and low-value data. Take HR’s strategic planning in a global company as an example: accurate, current data on workforce trends, such as remote versus on-site job preferences, is vital. Using pre-2020 data, for instance, would lead to flawed strategies as it wouldn’t consider the huge increase in remote work.

To further strengthen your strategy, adopt data operations and machine learning operations methodologies. These approaches help operationalize and optimize data workflows. Make sure your cloud data lakehouse and analytical tools are scalable and supported by the right talent. Whether you build this expertise in-house or outsource it, these steps are a necessity to extract real value from your data modernization initiatives.

#4 – What steps should I take to secure our data?

It is crucial to maintain data security and meet regulatory compliance standards when upgrading or changing data systems. Safeguarding sensitive and personal data is paramount to avoid financial penalties, irreparable harm to reputation and erosion of customer trust. Remove or anonymize personally identifiable information (PII) and sensitive data to adhere to a myriad of regulations. These vary based on geographic region and industry and include laws like GDPR and HIPAA.

That’s another advantage of migrating data to data lakehouses. Cloud storage inherently offers a robust layer of security, including meticulous regulation of user access privileges, specific role definitions and strategic data storage locations. The built-in security of data lakehouses makes them more secure and resilient than siloed, legacy data solutions.

Strengthen the resilience of data lakehouses and protect against breaches by leveraging advanced technology tools, including:

• Comprehensive encryption strategies
• Zero Trust access management frameworks
• Continuous, real-time monitoring with immediate remediation
• Geolocated cloud mirroring
• Data backup solutions

#5 – What’s the most effective way to manage data governance, quality and ethical use?

Effective data governance, quality management and ethical use play a central role in any organization. To develop a robust governance framework, delineate clear roles and responsibilities, ensuring unambiguous ownership and well-defined access controls. Make compliance with regulatory standards a cornerstone of this framework, alongside rigorous protocols for data stewardship and auditing to uphold data integrity. Embed ethical sourcing and handling protocols into all data practices.

Quality assurance demands a proactive approach. Schedule regular audits and feedback loops to fix data discrepancies and increase the reliability and accuracy of insights derived from the data. Keep the framework dynamic and responsive by regularly assessing governance effectiveness and adaptability to evolving regulations and standards. This approach engages stakeholders and fosters a culture of collaboration and ethical data handling.

Your employees can be your biggest advocates for successful data governance and ethical data use. Train your team members to value ethical data practices and maintain data quality. And never stop finessing your data approach as an organization. By prioritizing governance, quality, and ethics, you build trust and mitigate risks associated with data usage. Position your organization as a responsible and ethical data steward.

#6 – How can I prepare data for effective AI use?

The diversity, quality and quantity of your data form the bedrock of AI's potential. Preparing data for effective AI use is a critical step so AI models can detect patterns, predict outcomes and yield actionable insights. AI training relies on new, pertinent data, forming a loop of machine learning that refines algorithms and enhances their precision over time.

Prepare data effectively for AI use by focusing on the following:

• Data quality: Introduce processes like removing or correcting inaccuracies and inconsistencies, dealing with missing values and normalizing data to ensure uniformity.
• Data structure: Consider the specific requirements of the AI model you plan to use when choosing how to structure and format your data. This could involve transforming the data into a suitable format like CSV or JSON, or organizing it into tables or databases.
• Data labeling: Apply accurate labels to ensure your AI system learns correctly from the data. To supply a more comprehensive dataset, be prepared to augment this data, especially if the original dataset is limited or biased. This step is especially important for supervised learning models.
• Data splitting: Split the data into training, validation and test sets to evaluate the performance of your AI model. This allows you to train the model on one set of data, fine-tune it using another and test its performance on a third.

Your attention to detail when preparing AI data can help ensure that the data you feed into your AI systems is as accurate and representative as possible.

#7 - How can I effectively integrate new technologies with our existing systems?

Many organizations face the challenge of integrating cutting-edge data technologies with their existing, often outdated, systems. Consider technological and human factors when weighing the right approach for your organization. A balanced approach can minimize disruption to current operations. When it comes to integration goals, ensure data compatibility, prepare your workforce, manage risks and aim for a scalable and future-proof infrastructure.

While unique to your organization, your strategy should cover:

• Choosing between gradual integration or a complete overhaul
• Ensuring data compatibility with the new technologies
• Training employees to work with the new systems alongside the old ones.

Develop your data modernization strategy with Unisys expertise

Benefiting from AI starts with developing a data modernization strategy. Ask yourself seven questions to get to the who, how and what of making your goal a reality and realizing the full potential of your data. Explore how Unisys can help you develop your data modernization strategy or reach out.

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