With the incorporation of artificial intelligence and machine learning into business operations, it has become even more necessary to get clean data sets. These data sets are processed and analyzed to provide accurate and impactful results that businesses can benefit from especially when it comes to decision making.
It can be said that in the business world of today, clean data sets are driving great ROI as many businesses are data-dependent.
By incorporating artificial intelligence (AI) into your business, human-like tasks would be handled by the algorithms, while machine learning (ML) also performs human-like tasks, and learns from mistakes. Both are dependent on the data sets you feed them to yield good results.
Without high quality and clean data sets, AI, and ML in your business would be useless.
Data Characteristics That Provide The Best Results
To understand the importance of clean data with AI and ML, think of it as a student who needs to study, feed, and stay healthy to perform well in school. Just as studying and feeding are for a student, data sets must be:
This is essential for the system to give you the best performance and help with your success. This can be seen especially when algorithms enter the data maturation phase. At this point, AI and ML are expected to give a full performance and optimize actions based on real-world happenings and interactions.
In the real world, industries that can gain a lot from clean data sets being fed to their AI and ML are market and banking intelligence, down to real estate investment and prediction of trends.
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Taking real estate as a case study, a firm may be using AI to have an augmented competitive advantage in commercial property vehicles in popular metropolitan areas in the US. If the said company could connect their systems to a tool that would provide them with steady data as regards:
- Zoning changes and decisions from the infrastructure committee
- Fluctuations in price across the country
- Related legislation
It would assist their AI in making better and well-informed decisions about where users should invest their money for the best results. If the data were however corrupted or had a time lag, it would reduce the insights and output from the algorithm.
With that in mind, remember that your AI and ML are useless without clean data, and can only perform as well as the quality of data you feed them. So it’s important to have clean data sets as it drives ROI.
According to research by cognilytica, corporations spend a huge part of their time (up to 80%) cleaning data to be used with AI. It doesn’t end with investing in AI and ML, but businesses also have to invest in clean data sets because only then will the algorithms be useful.
The Importance of Maintaining Consistently High Data Quality For Meaningful ROI
Companies must prioritize the data training phase of their AI and ML use. this is done in most companies by investing a lot in getting clean data sets of premium quality to build their algorithms to the finest standard so that customers can get great value and blowing ROI.
After a while, it's normal to want to relax on these efforts and that’s when the problems start to arise. Companies that become negligent about the quality of operational data they feed their algorithms begin to see linear graphs of ROI instead of an increase.
The best results are only gotten when clean data sets are continually fed to the algorithms, as clean data sets drive ROI. AI and ML will only get used to you in generating high ROI when clean data sourcing and use are made the top priority. We would recommend you put the following into action if you want to follow up with these insights:
- Make the data your priority as it’s very important in achieving your goals.
- Define what you wish to achieve using your clean data sets, AI, and ML. it could be anything like providing real-time investment tips for your customers.
- Integrate the clean data sets into your algorithms and allow your AI and ML to analyze them accordingly.
- Find and use a reliable data collection tool that will provide you with reliable data as often as is necessary.
The Difference Between AI and ML
In simple terms, AI is the capability of machines to perform tasks that would otherwise require human intelligence to perform. ML is one of the applications of AI, and its algorithms process data to identify patterns that are specific and applicable.
In analyzing data of a specific context, ML searches for trends that already exist, and in the absence of said trends, make logical guesses regarding the potential outcomes and results.
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In practice, data scientists would enable both AI and ML by feeding them data training from their data lakes. The algorithms are taught, powered, and run by the initial data sets they were fed in the initial stage.
So data that is integrated into business programs should always be up to date and accurate as clean data sets drive ROI.