In a digital workspace, team members use different online tools to enhance productivity and output quality. For instance, the sales and marketing teams work in CRMs like HubSpot, the project managers use tools like Asana or Trello, and most teams store and manage documents in Google Drive.
As teams use these online tools, massive amounts of data points are generated. Data points that AI can use effectively to further enhance productivity and output quality.
Question is: How do you ensure that digital workplace mistakes do not poison the process of collecting, cleaning, storing, and feeding the data to AI models? Let’s find out!
Digital WorkPlace Mistakes that Poison AI Data Pipelines (Including Fixes)
Yes, there are several digital workspace mistakes that occur in various departments. For this piece, we’ll focus on those that specifically affect AI data pipelines.
AI data pipelines define how data is collected, cleaned, transformed, stored, and fed into a specific AI model. If you let these mistakes get in the way, you’ll end up with an AI model that yields biased results, memorizes data, or one that does not improve over time.
Here are the digital workplace mistakes that poison AI data pipelines and how to fix them fast:
- Skipping metadata and context
Before an AI model becomes useful or “intelligent,” it needs access to well-structured, consistent, clean, and meaningful data. And, metadata and context is what adds meaning to data.
Metadata is simply a description of the data within a specific dataset. Such descriptions include the date the data was collected, who entered the data, and what system generated it.
Context, on the other hand, clarifies the meaning of the information or data. For instance, if you are working with numbers, what do they present? You should be specific with the meaning of the numbers. Do they present sales revenue, customer satisfaction scores, product quantity, or what?
Without metadata and context, an AI model generates faulty insights, makes poor decisions, and makes misleading predictions. So, ensure your AI data pipelines have a metadata and context layer, especially when you are using web scraping solutions like a Browser API to scale web data collection.
- Over-reliance on manual entry
Even though digital workspace data comes from separate online tools, manually entering it into an AI data pipeline slows down operations. This is because people tend to get distracted or tired. Some interpret instructions incorrectly or differently.
When you are working with a small dataset, manual entry is manageable. But as more data comes in, you’ll need to hire more data entry labor. The more you hire, the more the common mistakes, including missing fields, misspelled names, wrong numbers, and more.
If you do decide to rely on manual entry, ensure you have someone tracking input quality. Also, start working on automating data entry so that when more data starts flowing in, you don’t worry much about data quality.
AI does not thrive on inconsistent or messy data. Plus, the more you automate data entry or integrate tools with AI models, the more teams get to focus on high-value tasks.
- Week access controls and security systems
While processing digital workspace data and feeding it into AI models for insights is beneficial, not securing the data flow from the workspace apps or databases to an AI model is disastrous.
If anyone can access the data while it is flowing, there is a chance that someone may poison the data without a trace. In short, when there are no access control structures in place, you can’t tell who did what to the data while in storage or transit.
Apart from access control, not securing the data increases the risk of a data breach. You expose your business to reputation damage, financial losses, and compliance issues.
So, ensure all AI data pipelines are linked to reliable access control and security systems. Use attribute-based access controls (ABAC) or role-based access controls (RBAC) to give employees access to the tools and data they truly need.
Add effective authentication methods and encryption systems, too. Whether in storage or transit, data should be encrypted. Also, log user activity and regularly review and update user permissions as roles change. This way, you can tell who made certain changes to data at a specific period.
- No clear data ownership and feedback loop
AI data pipelines feed from multiple departments including operations, finance, HR, and sales. If no one oversees the quality of the data coming from these departments, errors slip through unnoticed. Over time, the errors accumulate, leading to poor AI output.
Sometimes, an employee may notice an issue or error in the data pipeline. But if there is no clear process on how to report the findings, the employee may decide to ignore the error. The issue or error just keeps circulating in the pipeline.
Once you have an AI data pipeline in place, have practical data ownership systems and feedback processes in place.
Each department must have someone overseeing data flow to the AI pipeline. They should be responsible for the data’s accuracy, upkeep, and quality.
The data ownership system should also include clearly defined steps on how to address errors within the data pipeline.
For the feedback processes system, ensure there are clearly defined steps on how an employee can report errors without friction. The system should also log the incoming reports in time and trigger notifications to have errors fixed.
- Not updating or properly managing legacy systems
While you may be okay with keeping legacy systems in place, note that they are prone to compatibility issues. Most legacy systems were not built with integration in mind. So, they lack APIs for easy data sharing.
Not forgetting, some legacy systems store information in outdated formats or use inconsistent structures. This makes it risky to link them to AI data pipelines because the inconsistency and unsupported data formats lead to clashes between the two systems.
If your business is using legacy systems, only integrate the parts that are compatible with certain AI data pipelines. For the incompatible parts, start updating the legacy code. You can also build APIs for the incompatible parts.
The aim is to reduce friction in your AI data pipelines. You want to ensure that despite your brand running on legacy systems, it still enjoys the benefits of AI systems.
Final Words
Whether you are running a legacy or modern system, integrating either with AI data pipelines requires that you avoid these five mistakes. If you ignore these mistakes, the AI model feeding from the pipeline will generate misleading predictions or insights, negatively impacting business growth or reputation.
