Have you ever seen a chef bussing tables? Or a dishwasher running a kitchen? A Michelin 5-star restaurant that asks its chef to wash dishes is paying far too much for that task and it is taking that individual away from the work they were hired to do (and probably lowering the job satisfaction of both chef and bussing staff!) If you have witnessed the chef doing any of this work, it’s not a good sign for the restaurant. The same issue is taking place during the process of cleansing and structuring in today’s data world. Data scientists and data engineers are being asked to annotate unstructured data instead of exercising their expertise testing models and deploying data solutions to improve your company.
Why are these highly paid individuals working on what appear to be routine tasks? Unstructured data are the data you ignore or cannot access because the datasets consist of images, documents, audio files, and other non-tabular data. They are not the structured data types needed for passing into a business intelligence platform (i.e., Power BI, Snowflake, Tableau, etc.). The unstructured, raw, data need to be organized, annotated, and structured in a way that it can be understood by these platforms. All major data analytics platforms have one thing in common, they cannot read an image or sound bite natively. So what happens? Data Engineers and Data Scientists end up spending hours annotating their data to provide some structure for use in analytics platforms. The price of the dataset has just skyrocketed due to the cost of highly paid data experts spending more time preparing the data than using it.
Most people today are hearing of Artificial Intelligence and Machine Learning (AI/ML) for the first time, but these terms are not new in the 21st century. The mathematics around these fields of data science has existed since the 1960s. Today AI/ML have reemerged in force due to the lower cost and significantly greater capabilities of modern computing power. Yet, despite all the technology improvements and GPUs being more affordable than ever, an Achilles heel in AI/ML still remains: most applications focus only on structured data consumption.
Today we produce more data than ever but the majority of data are unstructured and not easy to access. They are locked up in a variety of file types mentioned earlier. So, how do we free the data? To be used, these data, need to be annotated (tagged, labeled, transcribed, etc.) The work involved with unlocking data can be very tedious and doing it accurately requires humans to make sure the data are labeled and annotated correctly. In too many cases, that human work force defaults to the data science teams – who should instead be setting up experiments and evaluating results. Unfortunately, too many data engineers spend a copious amount of time structuring and cleaning data, simply because they’re not aware of the alternatives. Quite often, that unexpected workload is addressed via the use of synthetic data and smaller-than-optimal training data sets – resulting in lackluster performance from the AI/ML platform. In addition, time spent on data preparation is time spent away from growing your strategic AI/ML capabilities. You should ask yourself, is it really worth your data team’s time to annotate data? Or should you leverage a more efficient and effective way to capture the value trapped in your unstructured data?
Liberty Source PBC prides itself in providing data structuring solutions that won’t jeopardize your data quality or security. We are a 100% US-based company that will enable you to realize the full potential of your AI/ML/BI investment. We can handle all your unstructured data, pre-processing as needed, and then annotate, organize, restructure, and deliver high quality structured data for your AI/ML/BI platform to ingest. Data structuring needs to be the first task for any advanced technology pipeline to be effective. If the data are not structured properly, everything down stream is at risk of contamination from the bad data and the results should not be trusted or depended on in production.
