As a future currency, collecting data is a familiar process for companies. However, the last era of technology and toolsets restricted businesses to simple structured data such as transaction information and customer and call center conversations. From there, the brand will use sentiment analysis to understand how customers feel about a product or service.
New AI tools and capabilities provide companies with an incredible opportunity to go beyond structured data and leverage complex rather than structured data sets to provide customers with greater value. For example, large language models (LLMs) can analyze human interactions and extract key insights from enriching customer experience (CX).
However, there are many steps to prepare for AI integration before an organization can leverage the power of AI, and one of the most important (and easily overlooked) is to modernize its data ecosystem. Here are some best practices and strategies that businesses can leverage to make their data ecosystem AI-Ready.
Master data and property
Businesses must collect and organize their data into a central repository or data property to become AI-Ready. A company’s data property is the infrastructure that stores and manages all data, with the main goal of providing data to the right people at any time when data-driven decisions are needed or a holistic view of its data assets. Unfortunately, most companies don’t understand their existing data heritage, whether it’s due to estate restrictions, isolated data, poor access control, or some combination of reasons.
To give businesses a deeper understanding of their data properties, they should work with partners who can provide AI solutions, such as a unified generative AI orchestration platform. Such a platform can enable enterprises to accelerate experimentation and innovation across LLM, AI-NATITION applications, custom add-ons, and (and most importantly, data storage). The platform can also act as a secure, scalable, and customizable AI workbench, helping companies gain a greater understanding of their data ecosystem, improving AI-driven business solutions.
Having a deeper understanding of your own data legacy can not only improve the effectiveness of AI solutions, but also help organizations use their AI tools more responsibly and prioritize data security. Data continues to become more detailed due to AI-driven processes and capabilities, emphasizing the need for security requirements and technologies that comply with responsible AI best practices.
Improve data governance and security
The enterprise’s data governance framework must undergo a major revision to be ready. The Data Governance Framework is a relatively new invention focusing on more traditional data assets. However, today, in addition to structured data, enterprises also need to use unstructured data such as Personal Identification Information (PII), Email, Customer Feedback, etc., which the current data governance framework cannot process.
Similarly, the generated AI (Gen AI) is changing the data governance paradigm from a rule-based guardrail to a guardrail. Businesses need to define boundaries, rather than relying on difficult rules, because a success or failure does not reveal anything particularly insightful. By defining boundaries, calculating probabilistic success rates on a specific dataset, and then measuring whether the output remains within these parameters, organizations can determine if the AI solution is technically consistent, or if fine-tuning is required.
Organizations must implement and adopt new data governance tools, methods and approaches. Leading brands use machine learning technology to automate data governance and quality assurance. In particular, by establishing policies and thresholds in advance, these companies can more easily automate the implementation of data standards. Other best data governance practices include deploying strict data processing and storage protocols, anonymizing data anonymizes data, and limiting unnecessary data collection.
As the current regulatory landscape around AI-driven data collection continues to evolve, non-compliance can cause serious fines and reputational damage. Browsing these emerging rules will require a comprehensive data governance framework that points to data protection laws specific to the areas of company operations, such as the EU’s AI Act.
Similarly, businesses must improve data literacy across the organization. Companies need to make changes at all levels, not just with technicians, such as engineers or data scientists. Starting with the data maturity assessment, assess the data security capabilities of different roles. For example, if the team does not speak the same business language, such an evaluation will be clarified. Once a baseline is established, businesses can implement plans to improve data literacy and security awareness.
Enhanced data processing capabilities
If not obvious yet, the unstructured data is that the Hill brand will fail or succeed. As mentioned earlier, unstructured data can include PII, email and customer feedback and any data that cannot be stored in regular text files, PDFs, Microsoft Excel spreadsheets, and more. Conduct a search. Most data technology tools and platforms cannot incorporate and adopt large amounts of unstructured data, especially in the context of daily customer interactions.
To overcome unstructured data challenges, organizations must capture this undocumented knowledge, extract it and map it into the enterprise knowledge base to create a complete picture of their data ecosystem. In the past, this knowledge management process was labor-intensive, but AI made it easier and more burdensome by collecting data from multiple sources, fixing contradictions, deleting duplicates, and separating important data from unimportant data. Affordable.
Once AI is integrated with the data ecosystem, it can help automate the processing of complex assets such as legal documents, contracts, call center interactions, and more. AI can also help build knowledge graphs to organize unstructured data, making Gen Gen Gen Gen more effective. Additionally, AI Gen enables companies to collect and classify data based on common similarities to discover missing dependencies.
While these emerging AI-driven data analytics tools can understand and attract insights from chaotic or unorganized data, businesses must also modernize their technology stacks to support these complex data sets. Restarting the technology stack starts with auditing – especially an evaluation of the performance of a system that can be associated with modern innovation and cannot meet the standards. The company also has to determine which existing systems can be integrated with the new tools.
Get help and become an AI-Ready
Access to data ecosystem AI-Ready is a involved, tedious and multi-stage process that requires high levels of expertise. Few companies have this knowledge or skill within their company. If a brand chooses to leverage the expertise of its partners to prepare its data ecosystem for AI integration, then they should prioritize specific qualities in their search.
For beginners, the ideal partner must have technical expertise in multiple interconnected disciplines (not just AI), such as cloud, security, data, CX, etc. As technological changes accelerate, predicting the future is becoming increasingly challenging. For this purpose, the ideal partner should not try to guess a certain state in the future. Instead, it helps the business data ecosystem and human capital become agile enough to adapt to market trends and customer needs.
Furthermore, as mentioned above, AI technology is not only suitable for data science teams. AI support is an effort within the scope. Every employee must be AI text, no matter their level. Partners should help bridge this gap by bringing together business and personnel expertise to help develop the necessary features within the enterprise.
