When it comes to sorting and organizing data within a business, there are a lot of unexpected problems you might face in the process. One of those is the issue of data discrepancies.

This is one of the more common issues in businesses and their data organization. It refers to the situation of two related and comparable data sets that conflict or do not match together as expected.

This is a rather big issue for enterprise search platforms, as they are challenged with providing users with accurate searches in an environment where the documents they need are stored under different names and systems.

Understanding Data Discrepancies

You’ve probably noticed that the numbers you see in different tools don’t always add up. Take tools like Jira and Salesforce, for example.

Jira serves for things like task completions, story points, etc., which don't always match up with what Salesforce is showing for closed deals and revenue. This can be really frustrating when you're trying to get an accurate picture of how your team's work is translating into business results.

These discrepancies result from the fact that Jira and Salesforce are two different systems that collect and report data in slightly different ways. It's as if you and your coworker were both trying to count the same group of tasks, but you had a slightly different definition of what constitutes a "completed" task.

Jira relies on developers and project managers updating ticket statuses, while Salesforce tracks deals closed and revenue booked. These two data sources can be slightly out of sync. 

It doesn't necessarily mean that one number is "right" and the other is "wrong." They're just looking at things through a slightly different lense based on how each system is set up. The best approach is to pick one main data source that you'll treat as the authoritative number and use that consistently when analyzing your metrics. 

That way, even if the absolute numbers are a bit off compared to other sources, you can still get an accurate sense of trends over time within that one data set. Similarly, you might see discrepancies between data in Slack (like message counts) and other operational tools due to differences in how engagement is defined and tracked across platforms.

Types of Data Discrepancies

Data discrepancies occur in many forms and for many reasons. The usual categorization recognizes six main reasons for data mismatch to occur:

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• Data processing errors: These occur during data handling and include incorrect coding, formatting issues, accidental deletion or duplication of data, and non-representative sampling.
• Sampling errors: This kind of discrepancy is introduced due to issues in the sampling methodology itself, like survey-based biases or non-representative samples.
• Variations in data definitions: Inconsistencies caused by different definitions or measurements used for the same data element across sources.
• Data entry errors: Inaccuracies that come as a result of human mistakes during manual data entry or input.
• Changes over time: Discrepancies arising from evolving definitions of concepts being measured or changes in data collection platforms or methods over time.
• Data integration issues: Inconsistencies are introduced when combining data from multiple sources with different structures, formats, or conventions.

Enterprise Search Engines and Data Discrepancies

When it comes to enterprise search engines, the idea of such a platform is to enable easy and accurate access to relevant information across the organization. 

It's no wonder that data discrepancies pose a significant problem for enterprise search's ability to provide accurate results.

For example, if the employee were to look for “product x sales in Q1.docx” but the information in question is stored under the name “sales of product x from January to April.pdf,” the search engine might not make a necessary connection, depending on how the indexing is done, and the employee could be left without much-needed information.

How Do AI Technologies Help With Data Discrepancies

AI technologies like natural language processing (NLP), machine learning, knowledge graphs, and vector search can significantly help with data discrepancies and enable more accurate and meaningful information retrieval. 

Vector search, which powers semantic search, represents words, sentences, or documents as dense vectors (arrays of numbers) that capture their meaning. This allows enterprise search engines coupled with vector search to find similar or related concepts based on their vector representations, even if they don't contain the exact same keywords.

By searching based on semantic similarity rather than keyword matching, vector search can surface relevant information despite differences in terminology or phrasing. 

Moreover, machine learning algorithms can be trained to automatically detect anomalies, outliers, and inconsistencies in data during processing stages. This can flag potential errors like incorrect coding, accidental deletions, etc.

These models can also analyze patterns in data to determine if samples are representative of the full population and detect non-representative or biased samples.

Tackling Data Discrepancies With Akooda

The first step in tackling data discrepancies is to integrate all the different data sources, tools, and platforms across the business. When all the information from various sources is integrated, it can be viewed through a single framework and effectively eliminate data discrepancies.

Akooda achieves this by integrating a company's SaaS tools into a cohesive network to allow the free flow of information between departments.

Coupled with advanced NLP, machine learning, and statistical modeling, Akooda effectively breaks down data silos and provides a centralized view of operations.  

By incorporating the latest technologies, Akooda helps organizations overcome data inconsistencies and enables easy access to relevant and accurate information across the enterprise.