Data collection has always been a plaguing concern for growing companies. Unfortunately, small to medium-sized businesses struggle with data collection strategies and techniques. Larger companies and start-ups with access to funding have the advantage of acquiring datasets from vendors or outsource the process for optimum quality and output. For entrepreneurs still solidifying their position in the market, the struggle is real.
Before your AI system can process and deliver impeccable results, it has to process thousands of datasets for training purposes. A system only becomes better with repeated training over contextual and relevant datasets. Businesses that fail to procure the right datasets in huge volumes often pave the way for ineffective systems that deliver skewed or biased results.
However, data collection isn’t that simple. In one of our previous posts, we explored the advantages and disadvantages of using free resources. We outlined when it is appropriate to use these sources but highly recommend reviewing your internal data before utilizing free datasets. In this post, we will further explain the costs of using in-house data.
What is In-House Data?
In-house data refers to the analytics you generate internally through your business. Internal or in-house data could be the information from your CRM, heatmap data of your website, Google analytics, ad campaigns, or another essential source obtained from within your company and its operations.
What are the Pros and Cons of In-House Data Sources?
The most significant benefit of in-house data is that it is free. The data generated internally is also relevant to the specific product or service you provide. Other advantages of obtaining in-house data include:
- You already have the pipelines and workflows for data generation, and this happens in real-time autonomously. There are no manual interventions or efforts involved in the data generation phase.
- In-house data is the most pertinent source of information if your business is unique, first to market in a geographical area, or is super-niche, and there are no previously available datasets available.
- Your internal sources offer you the most contextual, reliable, and up-to-date data, which you can customize based on your needs and preferences.
While internal sources seem ideal, applying them to your AI models is complicated. The process of data collection is simple but preparing is much more complex and time-consuming. Raw data requires you and your team to put in countless hours of manual work annotating, tagging, and turning it into AI training data.
You will have to collaborate with multiple teams – wherever data sources are scattered – and bring them together for a streamlined data collection process. Once collected and compiled, manual work kicks in again. This adds to complexity further, if you have limited time to market.
Let’s discuss your AI Training Data requirement today.
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 cost
There are also multiple hidden costs involved. Let’s look at them individually.
Hidden Costs Associated with In-House Data Collection
There 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 Expenses
Data 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 Expenses
Employees 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.
The 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.