From self-driving vehicles and automatic language translators to digital assistants and disease mapping, Artificial Intelligence (AI) is impacting our lives in many ways. While AI is a broad area of computer science that is predicted to achieve a global market growth of over 50 per cent by 2025, a key area of advancement for the energy industry is Machine Learning (ML), in which algorithms learn to imitate human decision-making by analysing data.
The benefits of Machine Learning are extensive and promise to transform the way that businesses operate, with significant cost savings, enhanced productivity, and better efficiency likely once an AI project has been implemented. However, the effectiveness of ML depends heavily on the quality of the data, so why is data cleaning fundamental to successful AI data processing?
Enabling AI To Correlate Information.
Most large energy companies have a huge volume of data, but it is easy for this to be poorly organised in multiple siloes of information. For AI to be effective, data needs to be able to flow into data stores, so that it is readily available to all parts of the business in real-time. Artificial Intelligence applications are more efficient and learn more quickly when they can correlate data from multiple sources; siloed data, in contrast, is difficult to access. Data cleansing will help to eradicate this problem, improving the software’s ability to find value in the business’s data.
Eradicating Errors and Duplications.
When data is stored in multiple siloes, there is likely to be a high degree of duplication. Additionally, some information pertaining to the same dataset may display discreet differences, which will cause conflicts and errors during AI training. Data cleaning is highly effective in improving the quality of data by eliminating duplicated information; this, in turn, will enable AI to learn and perform more accurately, without time-consuming and frustrating conflicts that will have to be individually resolved.
Making Information Easier To Find.
For AI to work effectively, data needs to be easy to search because, if information cannot be located, it will prevent the software from learning intuitively. Prior to commencing an AI project, it’s important that your business’s data stores are well-organised and logical. This can be achieved by implementing:
- Metadata tagging, to categorise data appropriately, thereby improving AI search capabilities.
- Sirus’ industry-specific taxonomies, which create a sophisticated knowledge map and streamline the business’s information systems.
- Data cleansing, to eradicate duplications and enhance AI performance.
By improving the organisation of your business’s data and removing unwanted information, the process of training AI will be improved, yielding more accurate and swifter outcomes.
Contact Flare Solutions To Clean Up Your Business’s Data
If you’re planning on implementing AI in your energy organisation but want to maximise the chances of success, please contact Flare Solutions today to find out how our AI data cleaning service can give your project the head start it needs.