Data is your biggest strategic asset in the AI era. However, a lot of C-suite executives have found that their companies are ill-prepared to use AI because of persistent data issues. Indeed, according to a recent poll, almost half of firms stated that inadequate or poor data quality could hinder their AI efforts.
The lesson is obvious: having the proper data foundation is essential to AI’s success. The definition of a modern data strategy, its importance for AI, and how to determine whether your data infrastructure is AI-ready are all covered in this blog article. We will wrap up with a useful checklist to assess how AI-ready your data strategy is.
What is a modern data strategy?
A complete plan outlining how a business will gather, handle, control, and use data to create value is called a data strategy. In a nutshell, it is a roadmap that synchronizes data-related tasks with more general business objectives.
A contemporary data strategy guarantees that your data is viewed as a strategic asset, facilitating actionable insights through analytics and artificial intelligence. It goes beyond simply storing data.
A modern data strategy:
- Addresses data governance and security (such as tracking data lineage, quality, and usage to build trustworthy AI models)
- Outlines the steps towards gathering, securing, and analyzing organizational data
- Explains how to break down data silos so data can be shared among the whole company
- Unlocks new business value by empowering employees with the right data and tooling to make better decisions
In summary, a modern data strategy defines how an organization turns raw data into business value.
Why AI relies on a good data foundation
The quality of AI systems depends on the data that powers them. It is true what they say: “trash in, garbage out.” Your AI will perform poorly if it is trained on shoddy or unnecessary data. For instance, AI models may produce biased, irrelevant results or incorrect suggestions as a result of low-quality data.
Conversely, the key to excellent AI results is high-quality, contextualized data. In reality, before expecting AI to provide insightful information, enterprises need to make investments in data preparation and quality assurance.
Furthermore, AI models could perform poorly if there is not enough variety and quantity of data. Consider an artificial intelligence system that has only viewed a limited amount of data. Real-world variety will be too much for it to handle.
Additionally, data needs to be organized and readied for AI to use. Eighty to ninety percent of company data is thought to be unstructured. Before AI models can be trained on this unstructured data (such as emails, papers, or photos), it frequently needs to be transformed into structured representations. An AI-focused data strategy must include that conversion and cleaning procedure.
The behavior of the AI will directly reflect any problems with the completeness, bias, or quality of the data. For this reason, forward-thinking businesses view data as the cornerstone of AI initiatives, frequently devoting a significant amount of time to data management and preparation rather than model development. To put it briefly, AI depends on data—and not just any data, but precise, well-managed, and pertinent data.
6 key elements of a modern data strategy
As you build your organization’s modern data strategy for AI, be sure to include these core components.
- Compliance with corporate objectives
Business objectives are the first step in a successful data strategy. Try to respond to inquiries such as: What are your goals? What do you hope your data will teach you?
Instead of hoarding data for its own reason, your data strategy should assist important business objectives. For instance, you can use the data you have gathered to enhance customer satisfaction, streamline business processes, or make new AI-powered products possible. To put it briefly, begin with a business problem or query.
- Compliance and data governance
Unreliable AI models are the result of data bias or misuse. Data governance serves as the foundation of every data strategy and lends credibility to data. It uses procedures and rules to guarantee excellent data security and quality.
Who “owns” the data, who can access it, and how it may be utilized are all determined by strong governance. Remember to address data privacy compliance, such as GDPR or CCPA rules, if you want to use user or customer data.
- A single truth source
For AI algorithms to function, data consistency is essential, but data silos are a problem for many enterprises. The goal of an AI data strategy is to dismantle data silos and compile all data into a single view.
For instance, the electric vehicle manufacturer Rivian discovered that a significant slowdown was caused by disparate systems and walled data. In response, they developed a single source of truth by creating a unified data architecture. A more scalable data base for AI development was the end result.
- Preparation and quality of data
In data strategy, quality is paramount. After all, before submitting data to AI models, it is crucial to make sure it is correct and error-free. Maintaining all of the data that your firm gathers is difficult, though. As a result, businesses have implemented DataOps methods.
The data counterpart of DevOps is called DataOps. Continuous testing and data quality monitoring are the main goals of data operations. For example, you may use automated techniques to find irregularities or discrepancies in receiving data. DataOps should be incorporated into your contemporary AI data strategy as your data landscape expands in order to save time and enhance the quality of your data.
- Sturdy infrastructure and data architecture
To facilitate the deployment of AI, more than half of firms (52%) are still working to upgrade their data infrastructure. How are you going to analyze, store, or absorb massive amounts of data? Describe your infrastructure and AI data architecture.
The majority of businesses now store their AI data in cloud-based data lakes and warehouses. Whichever option you decide on, be sure your data architecture allows for various integrations so you can gather data from a variety of sources. For fast access to models and insights, it is also critical that you can include analytics tools or AI models into your data architecture with ease.
- Culture of people and data
Finally, a data strategy is as much about people as it is about technology. Data-driven businesses that are successful make investments in change management and culture. This entails fostering a culture in which judgments are made using data and analytics rather than intuition. To foster this culture, teach your staff AI and data literacy skills.
Data-related responsibilities, such data owners, should also be defined. These positions are responsible for ensuring that the assets within their domain are stored correctly. Establishing data owners fosters accountability, which enhances the data culture inside your company.
When these components are present, businesses provide a strong basis for creating and expanding AI solutions.
Conclusion: AI data strategy readiness checklist
Building a modern data strategy for AI is a journey—but how do you know if you’re on the right track? Below is a practical checklist to evaluate your organization’s data strategy readiness for AI.
- Clear business alignment: Have you identified specific business problems that you hope to address with AI models that have been trained on your company’s data? There should be a business outcome associated with each data approach. Steer clear of gathering pointless data that just requires time and resources to maintain.
- Data culture: The effectiveness of your data strategy for AI depends on having a strong data culture. Have you developed a data-driven culture, secured C-level support, and devised a strategy to raise staff members’ data literacy? Technology by itself will not produce results if people and procedures are not included in your plan.
- Framework for data governance and ethics: A governance framework that answers issues like: Who is in charge of data quality? should be part of your plan. How are adherence to GDPR regulations ensured? Which data are not suitable for AI model training?
- Data quality assurance: Do you have systems in place to regularly assess and enhance the quality of your data? These could include automatic anomaly warnings, validation criteria, and data profiling tools. Keep in mind that AI magnifies data problems; poor quality data will show up in AI outcomes.
- Integration capabilities: Is it easy to incorporate additional data sources into your AI models so they may be further trained? You should be prepared to integrate data from both internal and external sources into your data strategy. Assess whether your data pipeline and ETL/ELT solutions are appropriate for AI operations. How soon can you integrate a valuable new dataset into your analytics system when it emerges (for example, a third-party product your team uses)?
- Data privacy and security for AI: Are there strong security measures in place at every level of your data strategy? Apply the least privilege principle. Limit authorized personnel and systems’ access to sensitive datasets. Encrypt data in transit and at rest where appropriate.
At start, very few organizations will check all the boxes. Finding your gaps can help you determine where your plan needs to be improved. You will see that your AI models provide increasingly significant outcomes as you check off more boxes.