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Mo Data Mo Problems: How to Use AI to Navigate Big Data

“The more money we come across / The more problems we see,” Notorious B.I.G. observed wistfully in 1997 in his hit song "Mo Money Mo Problems." Over 25 years later, in today's world of rampant data generation and collection, we’ve arrived at a new paradoxical crossroad: Mo Data, Mo Problems. We have every bit of data we could possibly need and more. So why is it so hard to use it?

The Promise of Big Data and AI

The rise of big data—extremely large datasets that are beyond the capability of traditional data-processing software—brought with it the promise that any organization could generate unprecedented insights, enhance decision-making, and create business value by leveraging the vast amounts of data generated in this modern, interconnected world. It was no longer about just one set of customer data, but the whole world of data available.

Those insights and advantages offered by big data are enormous, but also bring new challenges.  

  • How can meaningful insights be extracted from such vast and varied datasets that mostly have nothing to do with your business?
  • How do you ensure the privacy and security of data when handling such large volumes of information?
  • How can you balance the cost of large data storage and new data infrastructure with the potential benefits of big data analytics?
  • What are the legal and ethical implications of using big data analytics?

Arguably the best technology for capturing, parsing, and analyzing that much data—and doing so while staying compliant with regulatory requirements—is AI. However, while AI can handle big data capably, it also compounds some of the inherent issues.

“The importance of having clean and accurately labeled data to train supervised learning algorithms is well known and documented,” says Kevin McCall, Managing Director of AI at Launch. “A more recent challenge is how to deal with data quality, consistency, and bias issues in the staggeringly large amounts of self-supervised data that is used to train the current wave of large language models.”

More Scale, More Quality Issues

Big data promises us smarter cities, personalized medicine, and more effective marketing, among other benefits across sectors—but only if you can find what you need. Excising useful and relevant insights from big data is often like looking for a certain book at the Library of Congress during a tornado.  

The three Vs of big data—volume, variety, and velocity—describe the scale of data being generated. Managing this deluge is a monumental task that specialized human expertise alone can't handle. Enter AI and machine learning algorithms, which can autonomously analyze vast and varied sets of data, and make sense of complex patterns, more quickly and accurately than any human team could.  

Here’s the thing: the more data you have available, the more computing power and sophisticated algorithms you need. Investing in AI for data analysis does require additional cost over and above the cost of big data infrastructure. It also requires taking a hard look at data quality.

AI's hunger for data is insatiable—the more data fed into an algorithm, the better, in theory, it should perform. However, without proper testing and oversight, the quality of the data can slip. Bad data leads to erroneous conclusions by AI, and hence poor human decisions based on those conclusions.  

Before deploying AI algorithms to production, it’s vital to perform data quality checks and preprocess data for analysis. By putting more work in upfront, you’ll be able to get the insights and automation you really need for the long haul.

More Threats, More Security Needs

Storing and securing data is a critical concern, especially as cyber threats become more sophisticated. AI is both a solution and a part of the problem. AI algorithms can monitor systems in real-time to detect suspicious activities, offering a new and proactive layer of cybersecurity that’s often beyond the capabilities of a busy IT or security team.  

On the flip side, the same advanced features of AI can be weaponized by cybercriminals to identify and exploit system vulnerabilities, raising the stakes in the eternal game of cat and mouse between hackers and security experts.

Despite the potential vulnerabilities, organizations who want to thrive in the age of AI are making the informed decision to integrate AI in their cybersecurity strategies. When it comes to the risks of AI-assisted attacks, you have to fight fire with fire—the advanced security solutions provided by AI are indispensable in navigating new complexities of today’s cyber environment and adapting to the dynamic threat landscape.

More Regulations, More Responsibility

As we rely more on AI to make sense of our world of data, it's critical that legal frameworks adapt to ensure that this powerful technology is being used responsibly and ethically. Every country and governing body is taking a sharp look. Regulatory frameworks like the GDPR and CCPA, for example, are evolving to include stipulations regarding AI and automated decision-making.

Meanwhile, AI can also inadvertently amplify the ethical concerns associated with big data. Data privacy, informed consent, and data discrimination issues become more complex when artificial intelligence gets involved.

Many datasets are biased or unrepresentative, and if you train machine learning algorithms on such data, the outputs will be—at best—inaccurate for forecasting or extrapolation. At worst, the results could perpetuate and even exacerbate existing inequalities.

“A more nuanced and multi-faceted approach to data engineering is required to deal with these emerging challenges,” Kevin contends.  “Our project approach at Launch reflects that new reality.” That approach: An AI-first mindset that prioritizes transparency, accountability, and adherence to evolving regulations. This type of strategic and thoughtful AI approach is essential to navigating the intricate and interconnected regulatory and ethical landscape:

  1. By actively engaging with regulatory frameworks like the GDPR and CCPA, leading organizations not only ensure their AI systems are compliant with the latest legal standards, thereby mitigating the risk of legal repercussions and reinforcing their commitment to ethical AI use, but they also get ahead of upcoming legislation and governance.
  2. Throughout AI transformation projects, solutions must be designed to incorporate robust data quality checks and unbiased data processing methods. Training AI models on diverse and representative datasets enhances the accuracy and fairness of AI outputs, and thoroughly evaluating those models lets you know if there’s any alignment needed between the default model and your organizational objectives and values.

This proactive and informed AI approach underscores our clients’ dedication to leveraging AI technology responsibly, ensuring not only regulatory compliance but also the ethical and equitable use of AI in managing and interpreting big data.

In the whirlwind world of big data, where the volume, variety, and velocity of data overwhelm every human, the challenges are plentiful. The thing is, more data means more problems, but it also means more opportunities for insights, advancements, and innovations.

While we’re still fine-tuning the ways in which intelligent tech helps us leap hurdles in data quality, security, and regulatory complexities, it’s clear that harnessing the power of AI is imperative to navigating these challenges effectively. The world is only getting noisier and more connected. Without the infrastructure and tools in place to take advantage of available data at major scale, many companies will be lost.

The bold ones that lean into automation and intelligent solutions? They’ll be the ones that move from “Mo Data, Mo Problems” to “Mo AI, Mo Progress.”

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Launch is on a mission to get every large and growing organization thinking AI First. Is your org ready? Take our free AI Readiness Self-Assessment to find out.

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