DATA MAPPING - CHALLENGES AND OBSTACLES

Data Mapping - Challenges and Obstacles

Introduction

Data mapping is a crucial part of the data quality process. It can be used to identify gaps and inconsistencies in your data, as well as uncovering new opportunities for growth. But before you start making changes to your data models, it’s essential that you understand the nature of any underlying problems first-hand.

The best way to do this? By conducting a thorough analysis of your existing data structures and finding on out exactly how they fit together. This will help you identify any issues with data accuracy or consistency before they become an issue for users or applications later on down the line. So today we’re looking at some common issues that arise during these types of audits – from understanding what makes up “good” vs “bad” data all the way through to figuring out if there are any technical limitations stopping us from being able to perform these checks quickly enough!

Don't know the Data

As you can see, there are many different places where your company’s data may be stored. And if you’re like most companies, the data is stored in multiple systems (and even across different parts of the same system). The problem with this approach is that it makes it difficult to access all of your data at once.

If we look at our example with the two employees again, say one of them has an Excel file with information about his recent performance and another has a similar Excel file containing historical performance data dating back several years. If these employees need some sort of historical correlation between their work and results, they would have to manually link those two files together before they could perform any kind of analysis on the combined information—a process that could take hours or days depending on how much time was taken up by looking for duplicates and other inconsistencies between files.

Data elements definition challenges

Defining data elements is one of the most challenging tasks in data mapping. Data elements can be defined as “the smallest unit of information in a dataset (i.e., field) that has one or more values and represents a particular characteristic or attribute of an entity or event”. These definitions are typically not very precise, so when you get down to mapping your data elements, it may be difficult to determine what exactly constitutes a “data element” and what does not.

So how do we define our master subject? Well, for starters it should have at least one value which indicates its identity and/or property. For example: “Mary Smith” is a good candidate because it identifies someone by their name; however “123 Main Street” does not identify anyone specifically since anyone could live there! So if we had both fields on our map then who would they represent? Do we have multiple people living at 123 Main Street? Is Mary Smith still alive? Or did she move somewhere else? It’s unclear! Therefore we need something more than just name alone in order for us to make sense out of this information later when working with queries or reports related specifically back into these two fields which were once thought as being relevant enough but now seem meaningless without any context around them at all!

Overly complex business rules

Business rules are difficult to understand. When business rules are complicated and hard to understand, it can lead to confusion and frustration. People don’t know why they’re being asked to enter certain information into a certain field on a form, what data is needed for them to do their jobs well, or how that data relates back up the chain of command.

Business rules may be undocumented. Businesses often don’t have documentation explaining the rationale behind their decision-making process when creating new forms, workflows, reports, etc., which makes it hard for people working in those departments to implement updates or improvements later on down the line when they’re no longer available at all.

They’re not consistent across all departments within an organization’s structure: Each department has its own unique set of needs based on its role within an overall project plan or goal; however these needs aren’t always reflected evenly throughout an entire company because there isn’t enough time spent reviewing each department’s processes individually before going through with larger updates like this one (which could leave some areas underdeveloped). This also means users may not realize how much overlap exists between different groups’ requirements unless someone points it out explicitly beforehand so everyone knows what type

No data governance across silos and multiple versions of truth

Data governance is a process that ensures that data is managed and used consistently. Data governance helps ensure that data is accurate and complete, as well as protected from unauthorized access.

The term “data governance” has been around for more than a decade but only recently has it received attention from the media. This increased interest can be attributed to the fact that many organizations are now recognizing the importance of implementing data governance best practices. While some companies have implemented data governance in silos with siloed processes, others have taken a holistic approach by developing an enterprise-wide strategy for managing their information assets.

Expensive point solutions without reusable assets

The cost of developing a point solution is just the beginning. You’ll also need to spend on maintenance, integration and scaling.

In addition, your point-solutions will be isolated from each other in terms of data ownership and governance. These issues come with complications when it comes to migrating between products or integrating with other systems.

Lack of speed to market and collaboration

No context needed for intelligent decisions

Agile data modelling - always on, always available, always business relevant, consistent and complete

Data modelling – the process of describing how data related to each other so that they can be integrated into a single source of truth – is a key step in the data integration process. In this article, we will look at 5 challenges you may encounter when creating your data model and how to overcome them:

Too much information
Too little information
Inconsistent or incomplete data sources
Data quality problems such as duplicates and errors, or missing values (nulls)
Missing requirements

Conclusion

Data mapping is the most important step in data integration and it’s what enables the transformation of data into insights and actions. Without an accurate picture of the world around us, we can’t make informed decisions or take action towards our goals. If you need help with your data mapping, get in touch with DataLogic today!