If technology has made the collection and storage of data extremely easy, Artificial Intelligence (AI) has made the task of organizing such data and analyzing it easier. Machine Learning plays a very important role too, in enabling AI to continuously perfect its decisions by honing in on what is efficient and discarding whatever is inefficient. Data analysis yield superior results when data is enriched by tagging it with information relevant to the business. This could be achieved by manually tagging the data or by enriching it using AI-powered methods. Let’s discuss the significant differences between these two approaches and how AI-based automated tagging becomes essential for a higher volume of buzz.
Much of the data collected from disparate sources is pretty difficult to use in its raw form. Data enrichment is an important aspect of any analysis as it has disparate sources. It will need some cleansing, uniformity in classification and tagging before it can be of any use. Tagging a document is a data enrichment method which associates a document with a name so it is pulled up in any search, even if the document itself doesn’t contain the name and wouldn’t be pulled up when using any automatic text analysis.
These efforts can be taken up manually or using an AI-powered tool. Let’s look at the essential differences between the two processes and their performance:
|
Essential Differences between Manual Tagging and AI-Enriched Data Enrichment |
|
| Human knowledge is superior to artificial intelligence, optical character recognition (OCR) and automatic ranking algorithms. | Machines on the other hand never get tired, bored, make uncalled-for errors, insist on working fixed hours, apply for a leave of absence or enjoy labor laws which protect them. |
| Domain experts can add words and offer interpretations and qualitative values in a way that can’t be automated. | Training your machine to reach the level of a domain expert needs time, patience, a lot of data enrichment and context-based training, as well as a suitable budget. |
| Humans can find any documents they need even without tagging. | Data needs to be tagged before it can be analyzed, but the process is far faster than by any other means. |
| Documents which don’t contain the name, word, query or alias to be tagged require human editors. | Documents which contain the words to be tagged can be handled without problems, provided there are no typos or OCR errors. |
| Carries the personal bias of the editors | Standardizes the content and removes bias |
| Slow and time-consuming process. May take days for what AI accomplishes in minutes. | Saves significant amounts of time, especially when chatter is upwards of a few hundred a day. |
| Won’t be able to discover issues. | Helps you discover issues which you are unlikely to find with human tagging. |
| May flounder and lose focus when large amounts of data need to be processed. | Understands semantic relationships using other numerical methods helps an AI powered data enrichment effort. |
| Manual tagging brings in a lot more work, when compared to using an AI-powered tool. | Creates auto-tags in little or no time, saving you a lot of time and effort. |
| May provide insights but is not guaranteed to. | Turns a huge amount of data into well-structured and actionable insights. |
Enriched data is a real asset to any organization, as it improves outcomes and enables informed and insightful decision-making. Do you have large amounts of data which requires dedicated efforts over time before it can yield any reliable insights, or plan to start collecting it from now on? Do take a look at what Auris, our AI powered insights platform has to offer by way of collecting and enriching your data and offering powerful and actionable consumer and business insights.