This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.
Get ready or get left behind.
Artificial intelligence (AI) is no longer a novelty. In fact, if the story of AI integration into modern business was a summer blockbuster, you’d already be watching the first explosion.
Over 40 percent of enterprise-level organizations are already incorporating AI into their day-to-day business framework — and that number includes even more traditional and analog industries. If you focus that evaluation on more forward-thinking sectors like information technology (IT), over three-fourths of these businesses are already integrating AI Supply Chain into their policies and procedures. If “late-adopter” organizations wait much longer, the metaphorical movie is going to be halfway over, and they’ll be stuck playing catch-up.
How does a business start integrating AI into its payment analytics? For those arriving to the movie a little late, what’s the best way to get caught up? The answer to both these questions is data optimization. Clean, structured data can get your organization started in no time. Conversely, unstructured data that is confined to different silos and plagued by multiple sources of truth can force your company to lag even further behind.
It is imperative for every organization to find its own ways of leveraging the power of machine learning. However, in order to succeed in this integration, every organization also needs to come to terms with the past, present, and future of its data management.
Most Data Simply Isn’t Ready
You can’t have successful AI integration — and the high-powered, adaptable analytics that go along with it — without accurate, well-governed data or Master Data Governance. In addition, the more comprehensive and exhaustive your organization’s data, the more capable your AI tools will be at highly advanced functions like long-term decision analysis and predictive forecasting.
The truth of the matter is that most organizations’ master data management practices haven’t prepared them for this sort of demand. In fact, nearly 80 percent of organizations who embark on AI integration will run into problems related to unstructured data. Why? Because the average company’s data governance is rife with inconsistencies and subpar standards, and this often results in multiple sources of truth, a lack of robust automation and validation, and a long list of manual data entry errors. Without proper data optimization, large-scale AI integration will never succeed as intended.
Things to Remember
🔵 Over 60 percent of IT leaders say managing unstructured data is a problem for their organization.
🔵 Over three-fourths of professionals say their organization’s data architecture is key to long-term success.
Most Working Cultures Simply Aren’t Ready
AI integration is a highly demanding company-wide effort, and it can’t be achieved without a company-wide culture that is driven by data. There must be a thirst for high-quality data throughout the business, and every team should view incoming data as a truth-seeking tool.
Such a culture might seem pretty straightforward, but institutional change is never as simple as one imagines. For starters, all significant changes will encounter pushback. What’s more, Master Data Management cultures are rooted in technology — a concept that can sometimes feel inaccessible to those who are more analog or old-fashioned. These cultures must also prioritize centralization, uniting the many different analytics platforms, contract management software, databases, and data silos under one connected network.
Building this culture is hard. That’s why 6 out of 10 leaders haven’t been able to establish a data-driven culture throughout their organization. A failure to do so not only make it difficult for your organization to stay ahead of almost any curve but also makes it virtually impossible to capitalize off everything that’s made possible by AI tools.
Things to Remember
🔵 Over 85 percent of digitally mature organizations report having a team dedicated to data culture and data literacy.
🔵 Data-driven cultures also greatly influence product and process innovation, making companies naturally more competitive within their own industries.
Is AI-Powered Data Cleaning a Solution?
It isn’t uncommon for enterprise-scale companies to devote entire teams to addressing their data deficiencies. These manual cleaning efforts require a great deal of time to do well, and they often become a drain on an organization’s return on investment (ROI) for choosing to integrate AI enhancements in the first place. What’s even worse, there is no guarantee that these massive cleaning efforts will result in worthwhile data or that such data sets will be kept in the same structured, interconnected condition over the long term.
AI-powered data cleaning is quickly proving itself to be the future of data management. Not only does recent research show that AI-enhanced data cleaning is more effective than traditional methods, but it is also far more adaptable in its approach. For those organizations managing more dynamic data landscapes or third-party risk management, this adaptability can be critical in avoiding slowdowns and maintaining consistent standards across the board.
Need feature cleaning and label cleaning? How about a combination of uncertainty-based approaches and loss-based approaches on third-party risk management. No matter the situation, AI-enhanced data cleaning can give you every tool you need to effectively clean your data and prepare it to reap the full benefits of an AI-fueled framework.
Things to Remember
🔵 Nearly 68 percent of IT leaders report spending almost a third of their budget on data storage, management, and protection.
🔵 Over 60 percent of IT leaders say managing unstructured data is a problem for their organization.
Why is AI integration So Critical?
AI integration improves data accuracy by up to 80 percent. That improvement, as well as all the secondary improvements this AI integration entails, will have a serious impact on any businesses’ bottom line. Those organizations that can effectively leverage AI tools can reduce costs through procurement management automation, generate new revenue through data-driven insights, and enhance customer experiences. What those improvements do for your business depends on many specific factors, but it invariably means a more sustainable model for success.
🔵 Only a fraction of data is currently evaluated in real-time, which is one of the greatest strengths of AI- powered analytics.
🔵 Nearly three-fourths of all top performers report improved profitability due to AI integration.
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This article originally appeared in Solutions Review. Read the source article here.