Is Your Data Ecosystem AI-Ready? How Companies Can Ensure Their Systems Are Prepared for an AI Overhaul

Is Your Data Ecosystem AI-Ready? How Companies Can Ensure Their Systems Are Prepared for an AI Overhaul

As the currency of the future, collecting data is a familiar process for companies. However, the previous era of technologies and toolsets restricted businesses to simple, structured data, such as transactional information and customer and call center conversations. From there, brands would use sentiment analysis to see how customers felt about a product or service.

New AI tools and capabilities present an incredible opportunity for companies to go beyond structured data and tap into complex and unstructured datasets, unlocking even greater value for customers. For instance, large language models (LLMs) can analyze human interactions and extract crucial insights that enrich customer experience (CX).  

Nevertheless, before organizations can harness the power of AI, there are many steps to prepare for an AI integration, and one of the most important (and easily overlooked) is modernizing their data ecosystem. Below are some of the best practices and strategies businesses can leverage to make their data ecosystems AI-ready.

Mastering the Data Estate

Businesses must gather and organize their data into a central repository or data estate to become AI-ready. A company’s data estate is the infrastructure that stores and manages all data, with the primary goal to make data readily available to the right people when they need it to make data-driven decisions or gain a holistic view of their data assets. Unfortunately, most companies do not understand their existing data estate, whether because of legacy constraints, siloed data, poor access control or some combination of reasons.

For businesses to achieve a deeper understanding of their data estate, they should work with a partner that can provide AI solutions, like a unified generative AI orchestration platform. Such a platform can enable enterprises to hasten experimentation and innovation across LLMs, AI-native applications, custom add-ons and — most importantly — data stores. This platform can also function as a secure, scalable and customizable AI workbench, helping companies reach a greater understanding of their data ecosystem, improving AI-driven business solutions.

Having a deeper understanding of one’s data estate not only enhances the effectiveness of AI solutions but also helps organizations use their AI tools more responsibly and in a way that prioritizes data security. Data continues to become more detailed thanks to AI-powered processes and capabilities, underscoring the need for technical conformity with security requirements and adherence to responsible AI best practices.

Elevating Data Governance and Security

Businesses’ data governance frameworks must undergo a significant facelift to be AI-ready. Data governance frameworks are a relatively recent invention focused on more traditional data assets. However, today, in addition to structured data, businesses need to use unstructured data such as personally identifiable information (PII), emails, customer feedback, etc., which current data governance frameworks can’t handle.

Also, generative AI (Gen AI) is changing the data governance paradigm from rule-based to guardrails. Businesses need to define boundaries, rather than relying on hard rules since one success or failure doesn't reveal anything particularly insightful. By defining boundaries, calculating a probability success rate on a specific set of data and then measuring if outputs remained within those parameters, organizations can determine if an AI solution is technically conforming or if it needs fine tuning.

Organizations must implement and adopt new data governance tools, approaches and methodologies. Leading brands use machine learning techniques to automate data governance and quality assurance. In particular, by establishing policies and thresholds beforehand, these companies can more easily automate the enforcement of data standards. Other best data governance practices include deploying rigorous data processing and storage protocols, anonymizing data where possible and restricting unwarranted data collection.

As the current regulatory landscape around AI-powered data collection continues to evolve, non-compliance could cause serious fines and reputational damage. Navigating these emerging rules will require a comprehensive data governance framework that notes those data protection laws specific to a company’s regions of operation, such as the EU’s AI Act.

Likewise, businesses must improve data literacy across the organization. Companies need to make changes at every level, not just with technical people, like engineers or data scientists. Start with a data maturity assessment, evaluating the data security competencies across different roles. Such an assessment can ferret out if, for example, teams aren’t speaking the same business language. After establishing a baseline, businesses can implement plans to boost data literacy and security awareness.

Read the full article here to learn more about getting help to become AI-ready.

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