A company’s data can either be a source of weakness or untapped strength. The burgeoning era of generative AI is rapidly changing the way that businesses need to handle their sensitive information. In such an era, if an organization fails to properly prepare and manage their data, they will most likely encounter challenges, fall behind in their AI capabilities, and risk losing competitive advantage. By addressing the fundamental first step of data preparation, organizations can gain a significant competitive edge.
This competitive edge comes from the inherent value in finding, enriching, organizing and de-risking organizational data before it’s used for Generative AI services. For instance, GenAI platforms can now be used to draft a full request for proposal (RFP), based on a company’s past RFP responses in a specific service area, instantly. This efficiency boost frees up the time of staff that would otherwise be manually pouring over past contracts and proposals.
Generative AI makes this possible, but such speed and efficiency can only stem from proper data preparation where contracts, proposals and other important documents are properly discovered, classified and managed. This generates a normalized data quality foundation ready for curation through GenAI services, ensuring accurate high quality RFP outcomes that save time and resources.
Without proper information management in this scenario, the generative AI will produce an RFP full of hallucinations, requiring additional attention from managing personnel, creating a lack of trust in the generative AI outputs.
The use-cases for generative AI, where a company can leverage high quality data, are seemingly endless. Most industries, many with massive quantities of data, are leaning in, including insurance, pharmaceuticals, the financial sector and even government agencies.
So, what are the first steps for an organization wishing to harness the inherent value of its data and multiply AI success?
Based on our experience working with clients, we find that effective information management can be broken into four pillars: Discover, Understand, Govern and Use. Each pillar demonstrates how an organization can properly manage, understand and de-risk its data so that it’s prepared for “AI brilliance,” which is the demonstration of remarkable AI capabilities that produce unexpectedly intelligent results in an enterprise.
Discovering relevant data in an enterprise is key, especially as there has been an unprecedented increase in the amount of data within organizations, and there is no stopping point in sight. Because this data tends to be distributed widely across a variety of repositories—think SharePoint & Teams, Salesforce, company databases, file shares, etc.—it is critical to be able to locate and identify information and content. Unstructured data, like documents, emails and social media posts, pose another challenge to data management, as this information needs to be accounted for, as well. Without knowledge of information across repositories and what lies in your unstructured data, it is impossible to classify and manage information effectively and responsibly.
Next comes understanding what data has been discovered across an organization’s repositories. This includes categorizing and classifying the data to grasp its business purpose and relevance. It is here that pieces of data are differentiated from each other, like RFPs from insurance policies or legal contracts.
Here is where managing the data begins. Once it is located and identified, organizations can successfully govern their data, retaining and securing important and sensitive information and managing its retention lifecycle through to disposition. Through this step, data will be organized and structured according to the organization’s regulatory compliance and privacy requirements.
The data, now properly structured and organized, can provide genuine use cases for an organization. Whether generating a client proposal, extracting critical meta data from contracts, or protecting sensitive information, an organization will be able to leverage their data, and take advantage of the myriad of new AI services that are available, to create sustainable competitive advantages.
Once these four pillars of information management and a solid data quality foundation have been established within an enterprise, organizations can multiply the force of AI, unlocking the AI brilliance that sets organizations apart in an era that demands businesses adapt to the continual impact that AI has across all industries.
The potential for AI to completely revolutionize how enterprises handle their data is undeniable. There has never been a better time for organizations to multiply their success and leverage their volumes of highly valuable data to achieve a competitive edge.
About the Author
With a robust background in finance and IT, Jesse drives innovation in information management as the CEO of EncompaaS. He is a seasoned SaaS and professional services executive, specializing in information management innovation and risk mitigation for Fortune 500 companies. His efforts have spearheaded national and international operations and forged strategic partnerships with tier-1 technology consulting firms.
Sign up for the free insideAI News newsletter.
Join us on Twitter: https://twitter.com/InsideBigData1
Join us on LinkedIn: https://www.linkedin.com/company/insideainews/
Join us on Facebook: https://www.facebook.com/insideAINEWSNOW
Check us out on YouTube!
