AI-Ready or AI-Absent?

Why Data Optimization Is Essential for Today’s Artificial Intelligence 

Imagine you’re standing at the beginning of a foot race alongside several others. Which of you will get the best start? What exactly makes the difference? If you plan to compete in the race, you need clear-cut answers to both of these questions.

The integration of artificial intelligence (AI) into everyday business is becoming more essential every single day. For enterprise-scale companies, the days of “early adoption” are already over. In fact, about 42% of enterprise-level organizations have already actively deployed AI within their business. If you narrow that selection down to companies focused on information technology (IT), the number skyrockets to over 76%. To put it simply, the race toward AI integration is well underway, and some companies are already leading the pack.

Much like any race, some got off to a better start than others, and that head start has made all the difference. What made those accelerated integrations possible? Data optimization. If AI integration is a sprint, then data optimization is that extra layer of readiness that gets your organization off the blocks as quickly as possible. If a business has mass amounts of unstructured data confined inside a series of unconnected silos, it’s going to get off to a sluggish start. If it has “clean” data that is both interconnected and free of redundancies, it will shoot off like a rocket.

These are the realities of integrating machine learning into any business framework. Are you and your organization prepared to confront them? Are you even currently capable? Let’s find out.

Most datasets aren’t up to snuff.  

Accurate and complete data is the foundation of all high-quality analytics. Beyond that, data that is comprehensive and long-term is essential to some of the more advanced functions made possible by AI integration, such as predictive modeling and weighted decision making. These are some of the primary reasons 77% of professionals say their organization’s data architecture is key to any sort of sustainable success. Unfortunately, the clean and structured data needed to maximize efficiency in utilizing AI tools is very much the exception inside almost every industry.

Many companies are plagued by a host of data management issues, including multiple sources of truth, a lack of robust automation and validation, and even manual data entry errors. Such widespread deficiencies make it much less surprising that nearly 80% of organizations who embark on AI integration will run into problems related to unstructured data. It’s a costly roadblock, and it’s a roadblock that almost every organization needs to consider when thinking about the future of their data and data-based decision making.

Why AI-enhanced “cleaning” is often the answer. 

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.

Recent research shows that AI-enhanced data cleaning is more effective than traditional methods. In a systematic review of data cleansing mechanisms, machine learning-based approaches were found to be the most effective singular solution with both high scalability and high efficiency. In fact, the only approaches that performed better than machine learning alone were those that employed a combination of approaches and/or algorithms.

But the benefits don’t stop there. AI tools are far more adaptive in their approach, making them better equipped for dealing with dynamic data landscapes. With data usage and data regulations constantly in flux, such adaptability can be invaluable. From error prioritization to advanced entity matching — from transformer-based approaches to autoencoder-based approaches — AI can give you myriad tools for effectively cleaning your data. Once clean, that data is ready to experience the full benefits of an AI-fueled framework.

AI demands a centralized, data-driven culture. 

Of course, even if your data is clean, unstructured, and interconnected, that doesn’t mean it’s going to stay that way. No organization will be truly ready for integrating AI at scale until there is a demand for high-quality data from throughout the business. That level of demand is built by a data-driven environment — a day-to-day working culture that consistently uses data as a truth-seeking tool.

Creating this culture isn’t easy. Most data initiatives are rooted in technology, which can make them feel inaccessible to those less technologically inclined. What’s more, not all analytics have immediate and tangible ROI, which is to say nothing of the fact that people simply don’t like change. It’s no wonder 6 out of 10 leaders haven’t been able to establish a data-driven culture throughout their organization, and this failure can cripple the rollout of almost any new procedure or policy targeting data and data usage.

This data-driven culture must also understand the critical importance of centralization. Think about all the different groups and subgroups accessing and influencing data within your organization. Sales and marketing teams operate through the customer relationship management (CRM) system. Meanwhile, analysts rely on the data warehouse, and customer support specialists log information into a completely separate database. Multiple sources of truth isn’t just a problem for data cleaning. It’s also an ongoing challenge for advanced data management and data use.

How does AI integration affect the bottom line? 

Most immediately, AI integration improves data accuracy by up to 80%. That said, the more impressive part of the story is the many different ripple effects and ancillary benefits of incorporating AI into your business. AI tools can also reduce costs through automation, generate new revenue through data-driven insights, and enhance customer experiences. How such integration affects the bottom line of any given business will depend on many factors, but the end result is almost always increased sales and profitability.

Things to know: 

• The average organization is using AI for approximately two different business functions.
• The two most popular functions for AI are marketing/sales and product/service development.
• Nearly three-fourths of all top performers report improved profitability due to AI integration.
• 64% of those same top performers have already used AI to develop products or services.

On your mark. Get set…  

The race is underway. Everyone is pushing towards the front. Where do you stand? How can you keep up or catch up?

AI integration is something every business will eventually need to stay competitive. That integration, however, can be either buttery smooth or destructively disruptive. Organizations need clean, structured, and fully connected data just to get off the starting line. To keep pace or to stay ahead, they need a data-driven culture that demands centralization at all times from in their methods of data management.

Written by Dave Curtis.
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