Why ‘Clean Datasets’ Are Key To Driving Meaningful ROI For Businesses Using AI And ML

Reliable data sets have never been more crucial with an ever-increasing number of companies utilizing AI and ML to create and maintain a competitive edge.
5 min read
data collection tools for immaculate data sets to calibrate your ML and AI outputs

Sourcing immaculate datasets is crucial in powering algorithms that provide accurate and impactful outputs that businesses can leverage.

Artificial Intelligence [AI] performs human-like tasks while Machine Learning [ML] performs tasks and independently learns from errors. Both are powered and underpinned by datasets in a similar way.

High-quality data, is the ‘ingredient’, without which AI and ML are meaningless.

In this post you will learn:

The fundamental difference between AI and ML and why data is crucial to receiving optimal results in both

Put simply, AI capabilities are machines that are capable of performing tasks that would otherwise require human-level intelligence.

ML is one of the many applications of AI. Machine Learning algorithms process data in order to identify specific and applicable patterns. When performing an analysis of a specific data environment, ML searches for already existing trends. In the absence of this, ML will analyze the data and make ‘educated guesses’ as to potential outcomes and responses.

Typically, data scientists will feed their AI and ML ‘training data’ from their data lakes which enables their ML and AI. In other words, algorithms are educated, enriched, and powered by the datasets they are fed in the initial stage. This means that data integrated into your businesses’ programs should be up-to-date and properly sourced from the get-go leading into the operational stages of your systems.

What data characteristics are crucial to powering algorithms that generate better results

AI, ML, and data can be thought of in the context of an Olympic athlete who needs to be fed healthy and nourishing food as well as being trained properly in order to have a positive influence on his or her performance. Similar to training sessions, as well as eating and sleeping regimens, data sets must be:

  • Clean
  • Consistent
  • Reliable
  • Accurate
  • Traceable
  • Explainable

In order for the rest of the system to attain next-level success. This is especially true long after the ‘training stage’ when algorithms enter their ‘data maturity’ phase. Meaning when ML and AI are expected to deliver full capacity results and optimize activity based on real-world interactions and happenings.

Let’s look at this through the prism of real-world use cases. Industries that currently stand to gain from clean data being fed to their AI and ML can range from market and banking intelligence to real estate investing and trend prediction.

Zooming in on Real estate investing, a firm may currently use AI in order to provide an augmented competitive advantage in commercial property vehicles in the top 100 metropolitan areas in the U.S., for example. If said company were to plug their systems into a tool capable of providing them a steady influx of data pertaining to:

  • Price fluctuations
  • Zoning changes and infrastructure planning committee decisions
  • Related legislation (ownership, subdivision, taxation etc..)

Their AI could make better-informed decisions as to where their users should invest their money in order to receive optimal results. If however this data had a significant time lag or was corrupted in any way from a geolocation perspective, this would severely damage algorithmic insights and output.

With this example in mind, you must always remember that your AI and ML are only as valuable as the datasets you feed them. All roads lead to the quality and accuracy of the datasets that you collect and compromised data means derived trends, insights and conclusions need to be thrown out with the bathwater.

In fact, research conducted by Cognilytica (via Internet Archive) indicates that:

Corporations spend over 80% of their time on cleaning data in preparation for AI usage.

Conclusion: If businesses are investing in AI and ML in order to improve efficiency and results they must first invest in pristine datasets, only then will AI and ML present true value to businesses. This means that your technology needs to be aligned and work in tandem with your data feed.

Would you like to gain access to clean data for better results? Try our data collection tools now and gain access to reliable and accurate data that will allow you to optimize AI and ML output while helping you achieve your business goals

Why maintaining consistent data quality is key to deriving meaningful value from algorithms over time

Many companies spend a great deal of resources in the data training phase of their AI and ML. They invest in data of the highest quality in order to build algorithms that can deliver value to customers and explosive ROI. Issues start to arise over the course of time because companies start to become negligent regarding maintaining the quality of the operational data they feed their algorithms.

Instead of getting higher than expected ROI, companies are actually seeing really linear graphs.

The magic really happens when companies make a commitment to maintaining the quality of their data over time.

AI and ML will only begin generating meaningful ROI for businesses when ‘clean data-sourcing’ is made a top priority.

If you would like to begin practically implementing these insights I recommend putting an action plan into play by:

Defining the goals you wish to achieve using your AI, ML, and clean data. For example, providing real-time investing tips for users.

Prioritizing the data that is most important to achieving the goals you defined. For example, same-day stock price movement over $5 on technology securities.

Finding a reliable data collection tool that can guarantee supplying you with a steady flow of reliable data. For example a global network with big data capacity that utilizes consumer devices to support your collection efforts.

Integrating the new data into your algorithms and allowing your AI and ML to adapt their ongoing workflow and analysis in accordance. For example, when managing governance risk and compliance issues, your systems will be able to recommend appropriate controls based on rapidly changing regulations.

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