What is data mapping?

Data collected by enterprises is vital to help them make better decisions, understand consumer behavior, improve process efficiency, and understand performance—be it of the company or its product.

For an enterprise or a business of any size. To obtain the most value from data, an enterprise must excel at identifying and storing the data, have the technical capability to analyze and visualize the data, and implement the relevant insights obtained using the above processes.

But before all this can happen—before the company can get down to making decisions based on the analyzed data—it has to be standardized in a way that’s accessible to multiple users. This is where data mapping comes in because it enables different systems to read and interpret data in the same way.

Why is there a need to standardize data?

Today, data comes from many sources, and each source may define the same or similar data points differently. Data mapping helps create a reliable, consistent single source of truth for the organization.

For example, in Kenya, the country may be KE in one source field, KEN in another, or Kenya in a third source field. Data mapping bridges the gaps and differences between these varied data systems/models, so the end output is standard, accurate, and valuable.

Data compliance and consumer privacy

The other need for data standardization is to help with data privacy and compliance. There are many regulations around data security, and it is in the organization’s best interests to comply and champion data privacy. In particular, general data protection regulation (GDPR) and the California consumer privacy act (CCPA) significantly impacted data storage and use rules.

Organizations must be able to:

• Provide data to those who need it, stripped of identifying information
• Enforce privacy legislation and see where improvements are required
• Provide individuals their data if they request it

These functions are only possible if all pieces of information are linked and disparate data about a person is consolidated. This will give the organization the control to enforce rules, manage data, and consistently apply regulations.

Data mapping techniques

There are three main data mapping techniques:

Manual data mapping

Manual data mapping involves connecting data sources and documenting the process using code. Analysts will usually make the map using coding languages such as SQL, C++, or Java. Data mappers could also utilize extract, transform and load (ETL) functions to move data between datasets, or data virtualization could be used. The benefits of manual data mapping include flexibility, control over the process, and the ability to customize to exact needs. However, being manual, the biggest drawback is the time consumption. It is also resource-intensive, dependent on code, and tool-agnostic.

Because data mapping needs to be done by someone skilled, such as a data scientist, this task not only uses up financial resources but diverts attention from more value-adding and creative processes.

Semi-automated data mapping

Semi-automated data mapping is also known as schema mapping. This requires the user to have coding knowledge and move between manual and automated data mapping processes. Semi-automated data mapping uses graphical representations of data links. This could be drawing lines or using a drag and drop function to create a visual interface through data-mapping software. A data analyst then reviews these connections and makes manual adjustments as needed.

The advantage of this method is the balance between greater flexibility and effectiveness and reduced time consumption. However, coding requires a certain level of expertise and knowledge related to navigating between manual and automated processes. The other drawback of this method is that it is still quite resource intensive.

Automated data mapping

Modern data mapping is in the process of becoming fully automated, using artificial intelligence (AI). Anyone can initiate and carry out the operation, from the expert to the amateur. Coding is not required; data can be sorted, regularly refreshed, and scheduled for analysis. Some mapping platforms can also use machine learning (ML) tools such as natural language processing to match data fields, which helps better understand the data. This translates into better analysis and reduces incorrect assumptions. The advantages of going the automated route are numerous: less technical know-how required, quicker and easier to scale, and scheduling and deployment are more efficient. However, even automated efficiency comes with its drawbacks—mainly that the costs and any training involved will be limited to the specific software/platform purchased.

But other cases can be made for the automated mapping of data, explicitly using cloud-based artificial intelligence tools. As discussed earlier, paper-based or manual systems cannot keep pace with the complexity of data today. As businesses rely significantly on data quality, data architects need a clear, precise, and real-time view of data at its source and destination. Automated data mapping tools fulfill these needs by providing a standard idea of the mapped structures. This allows a birds-eye view of the entire data structure, its fluidity, flow, and transformations in real-time.

A good data tool additionally allows for accurately streamlining data movements, thus reducing the possibility of human error. Data maps are not a one-time-done thing, either. Changes in data standards, data privacy laws, and reporting mechanisms mean that maps require maintenance. A good mapping tool will ensure the documentation of these changes in a standardized and timely manner.

Organizations should consider using artificial intelligence and machine learning to help map their data. These offer better performance than traditional analytics techniques and can help automatically recognize personal data, enabling organizations to align with GDPR.

Mapping for data management

While data integration may be considered the central role of data mapping, it plays an equally important role in data management processes such as migration, transformation, and integration. The logic used during mapping will determine the quality of the methods, and any inaccuracies in the mapping will also affect these processes.

Data integration

Data integration integrates data from different sources to form a uniform view, and mapping is required when integrating data into a workflow or a data warehouse. Data mapping tools help bridge the differences between source and destination systems by enabling data transformation and conversion. Businesses are thus allowed to collate information from different data points efficiently. The importance of data mapping is reflected in the fact that many data integration tools available today include a code-free mapping feature.

Data transformation

Data is taken in a specific format and then converted into a different structure or format. This step is crucial in data integration, and mapping will be used to define the connections between the data sets.

Data migration

Data migration is shifting data from one dataset to another, and data mapping is used to make this a smooth transition. Data migration involves several complex steps, and creating mappings between source and destination is one of the most crucial. Inaccurate mappings at this stage can negatively affect the usability of the data.

Electronic data interchange (EDI) exchange

Data mapping facilitates EDI by converting files into various sources such as Excel, JSON, and XML. A good mapping tool has built-in transformative tools to map data to EDI formats without coding, thus allowing seamless business-to-business (B2B) data exchange.

The data mapping process

Like any project, data mapping also requires the collation of information from all involved stakeholders. There are six steps to efficient data mapping.

Step One: Define

The data being moved, including formats at both source and destination, need to be clearly defined, as does the transfer frequency.

Step Two: Map the data

This step involves matching the source and destination fields.

Step Three: Transformation

This is the coding of a transformation formula or rule if any specific field requires transformation.

Step Four: Test

Do a test run transfer to see how it works and make necessary adjustments.

Step Five: Deploy

If all works well, go live.

Step Six: Maintain and update

Data integration is a dynamic process, and mapping, therefore, becomes active, taking on regular updates and changes. It is not a one-and-done process, but ongoing maintenance and checking are required for continued optimal data.

Why is data mapping crucial?

Today, all businesses are data-driven. Data is used to make informed decisions rather than those based on intuition or assumptions. However, even companies with data at their fingertips sometimes make the wrong decisions because the data was either not collected or analyzed accurately. Imagine going to a new place, a new city, and the global positioning system (GPS) is not working. Suddenly, no one can plan their travel efficiently; no one can go where they need to go. Now, apply the same logic to a company without data mapping.

Mapping is the first vital step in uncovering key insights and business intelligence of the organization’s target markets and customers. If data is keyed into the system uncharted, analysts won’t know which data sources are redundant, potentially leading to misinterpreted data analytics. Data mapping allows the combining of sources into a single trustworthy dataset, which leads to more transparent processes and analyses.

Additionally, when an organization has business-critical data about its customers, data mapping is crucial to establish its validity and provide documentation on how it was obtained. This is a necessity for data regulations. Given the explosive volumes of data today, data privacy regulations have become more stringent and require companies to responsibly inventory, store, and manage all data. Mapping logically links seemingly unrelated data and allows the company to enforce privacy policies and correctly use the data.

Apart from these, data mapping allows for the following key benefits:

• Better analysis leads to the identification of patterns and trends and a deeper understanding of consumer behavior
• Easier and quicker access to data
• Better privacy for individual data
• More stringent data compliance protocols
• Improved data security
• Improved processes

Is data mapping really that simple?

While the data mapping process can be broken down into simple tasks, there is no doubt that the process is fraught with its own set of challenges. Many organizations find it challenging to bring their data mapping process to a fruitful conclusion. Because prevention is better than cure, organizations need to be aware of some common roadblocks in the mapping journey.

Data mapping is time intensive

Data mapping is a time-intensive process, encompassing numerous datasets across the enterprise. Encapsulating that into business processes is difficult. Artificial intelligence (AI) and machine learning (ML) can come into play here because they can work on a massive amount of data quickly, efficiently, and with minimal effort.

Attention is required constantly

Data mapping requires constant and faithful attention if an organization obtains optimum results. Data patterns must be evaluated, verified, and updated, or else the analysis provided may prove to be of no real value as a business tool.

Information must be in place before mapping

Information is key to data mapping. All information must be taken into account while building data maps. Essentially, before data mapping begins, companies must secure all the information required from any stakeholders. Examples could be litigation risk profile, accessibility constraints, and retention. Data protection also comes into play here.

Expertise and skilled employees are required

Mapping requires expertise, precision, and comprehensive knowledge of data systems. While technologically advanced solutions have made low-code solutions readily available for the layman, a certain level of accuracy is required from the very initiation of any mapping project. This is why AI-powered mapping solutions with user-friendly dashboards and shared templates are gaining more traction. These solutions allow for precise mapping with less complexity and time consumption.

Organizations need data mapping to make sense of their data

Data was recognized as a business asset almost two decades ago, yet there remains a gap between the actual recognition and its implantation as an asset. It is not that organizations fail to understand the importance of data; the absolute failure comes in its utility and the underestimation of data as the most valuable business asset of all. Today, businesses collect data from websites, mobile phones, customer interaction, and vendor interaction points. This data collection is essentially the key to succeeding in a highly dynamic and competitive business environment. But that’s the case only if the data enables a holistic 360-degree view of an entire business mechanism.

Data mapping allows an organization to make optimum use of its data—to market better, sell better, and make every business process more efficient and successful through empowered business intelligence.