What is the Cost of In-House Data Collection?The expense of collecting and preparing internal data can have multiple meanings in this case. Here we are only referring to the tangible investment and the amount of time and effort you put into collecting and annotating data. As far as monetary transactions are concerned, you have two major expenses:Salaries for your in-house AI specialists, data scientists, annotators, and QA associates.The costs involved in using and maintaining a dedicated data annotation platform.At any given point of time, the total cost incurred to work with in-house data is: Cost Incurred = Number of Annotaters*Cost per annotator + Platform costThere are also multiple hidden costs involved. Let’s look at them individually. Hidden Costs Associated with In-House Data Collection
Management ExpensesThere are crucial expenses associated with managing the entire operation and processes in data collection and annotation. This is an integral wing of AI adoption that needs to be funded and constantly monitored. To successfully collect and prepare internal data, there must be a hierarchy involving associates, quality executives, and managers who report to senior management. Data Accuracy Optimization ExpensesData directly from a CRM or any other source is still raw and requires data cleaning and annotation. Your in-house team must manually identify and attribute every single element in a text, video, image, or audio and make it ready for training purposes. The datasets require validation through results. When the results are not accurate, they have to be manually adjusted for optimization. Based on the scale of your ambitions and data availability, multiple rounds of optimization workflows can not only be expensive but tedious and time-consuming as well.Employee Turnover ExpensesEmployees are bound to leave organizations no matter how enjoyable the work culture. At the end of the day, personal ambitions and satisfaction become a priority for employees. While this is philosophically correct, monetarily, it’s a significant loss for business owners and operators. When employees frequently join and leave your organization, you end up spending money on their onboarding, training, and even exit. The worst part is you have to teach a new resource about your data collection and annotation techniques from scratch. If they learn slowly, they will end up skewing results and trigger additional data accuracy optimization expenses.Wrapping UpThe expenses related to in-house data collection include direct and hidden costs. Remember that amidst the complex process, you also have to develop your product, promote the company, and prepare go-to-market strategies.To avoid all the hassles, we recommend getting in touch with data collection and annotation experts. At Shaip, we have the most extensive data network in hand, making it easier for us to source datasets from niche market segments & demographics. We also deliver annotated data so you could directly use it for training purposes. Get in touch with us today.