Key Takeaways:
– Majority of companies are not prepared with the right data strategies to adopt AI technologies.
– Emphasizing on data cleanliness, proper labeling, and improved governance can help organizations become AI-ready.
– These steps demand considerable time and effort but yield richer insights, time savings, and a competitive edge in the long run.
Evolving Generative AI technologies are greeting businesses with exciting growth opportunities and unprecedented challenges alike. The digital landscape is buzzing with possibilities, ushering in a race among companies across industries to successfully incorporate AI. But the pathway to AI readiness is proving to be a stumbling block for many organizations, especially on the data front.
Preparing Data for AI: The Importance of Cleanliness
One of the initial stages of getting a company’s data AI-ready involves the untidy business of cleaning the data. AI programs live and die by the quality of data they receive. Whether it’s spreadsheets riddled with duplicates, inaccuracies, or gaps in information, these flaws can hamper every outcome.
Many leaders overlook the cleaning process, seeing it as a time-consuming task. However, it can lead to far superior results, in-depth insights, and savings of time and effort. Certain companies have even started employing AI to help with data cleansing. Nevertheless, human involvement remains crucial to pilot this process, ensuring the data is appropriately corrected and all gaps are properly filled.
Despite seeming to be a tedious task, the cleaning of data is an indispensable step. Without this foundation, ambitious AI projects are likely to fail; thus the investment of time at the outset becomes a requisite.
Data Labeling: Making Data Relevant for AI
Just as a traveler needs signs to navigate unfamiliar terrains, AI requires a structured path to find and use relevant data. This is where data labeling comes in. It involves processes like annotating, tagging, or classifying data to provide the necessary context for machine learning models to recognize and exploit effectively.
Though labeling is often a tiresome and challenging job, it is also a decisive factor for generating valuable AI results. Companies looking to embrace generative AI must invest time to appropriately label their data, thus enabling algorithms to navigate vast data pools seamlessly.
Data Governance: Foundations for AI Success
More industries are recognizing the importance of good data governance with the rise of big data and digital transformations. As they open up to AI adoption, data governance becomes a vital pillar for success. It starts with developing or enhancing a governance program, setting standards, and empowering data experts to enforce best practices.
A robust governance program not just ensures better accuracy and reliability of AI results but also assists in managing other data components. Once a firm sets its data norms, builds sound enforcement structures, it lays the perfect groundwork to integrate new information, resolve hygiene issues, and bolster data security.
With the rising necessity for superior data governance, the role of data analysts also evolves. Despite concerns about AI replacing human jobs, there remains a crucial need for human expertise to oversee accurate and meaningful use of data.
The Road Ahead
As organisations scramble to adopt new, path-breaking technologies, they face significant hurdles given their existing data challenges. To truly harness the potential of generative AI and other machine learning-driven projects, an overhaul of their data hygiene practices becomes non-negotiable.
With a thorough cleaning of data, ensuring proper data labeling, and reinforcing data governance structures, businesses can position themselves at the forefront in the AI revolution. Only by laying a solid data foundation can the full potential of next-generation tools be capitalized upon.
About the author: Ben Schein, who serves as the Senior Vice President of Product at Domo, and is responsible for product design and strategy, including product management, UX design, product led growth, and strategic architecture across the company.
