Data Mapping for Aussie companies

How to Identify and understand data structures

If you want to learn how to identify and understand data structures, you already recognise the importance of data mapping and the need to have access and insight to complex company data. To identify and understand data structures, you need to assess, collate and analyse data from across your enterprise, which will inform your data decisions and use. 

 

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Harnessing the power of data

Does the concept of data structures do your head in? If so, you’re not alone. We work with many Australian clients who recognise that understanding their data is the key to the future growth of their business. Yet, they have trouble coming to terms with the concepts and are worried they are falling behind in the race to use data to gain competitive advantage. 

Activities such as data migration, integration, synchronisation and automation require data mapping to be done before they can begin. This article will cover the basics of data mapping for Aussie companies. 

What is the process of data mapping?

Data mapping is a process that can be carried out when you are seeking to bring information from different systems together to create a master data set or single source of truth within your information and data networks. Data mapping is the first step in the process of data migration and integration.

To establish this single source of truth, software such as Enterprise Resources Planning (ERP) systems or Master Data Management (MDM) tools can be implemented to gather and present data from across separate systems, programs and data sets. To be able to implement an ERP or MDM, you need to have a baseline set of data across all systems that can be picked up and utilized. 

Data mapping details how you will match and connect data from one source (such as a database or application) to another. Data mapping is a critical process for data analysts and architects, who need to see and understand how data flows and transforms to be able to improve processes and achieve high levels of data integrity.

Data mapping steps

Carrying out data mapping enables you to get a clear picture of the state and condition of the data you hold within your information networks. The process of data mapping enables you to assess your data, understand where it resides and which components are used in particular processes.

To begin with, all you need is an interest in understanding your current position and some ideas about how you might need to use data in the future. Data mapping steps include:

1. Investigate data sources

The first step is to list out and consider all of your data sources- the systems in which you have corporate information and the format it takes. This involves identifying systems and sources and the components you will need to include in the data map.

2. Review data resources

In most cases, you will have collected resources related to your software solutions, gathering existing knowledge and records pertaining to your customisations. At this point you can also gather feedback from staff about their experiences with data interactions and existing system capabilities. 

3. Analyse the data

The objective here is to see if you can clarify the relationships between existing data components and how they relate to and interact with each other. At this point, you can also test data sets for accuracy, validity, missing components and errors, so that you have a better understanding of the baseline level of data quality and integrity.

4. Map the data

This step involves creating representations of the data and how it interacts, so that you are more clearly have to see and visualise data movement. You can use graphics programs, flowcharts or other mapping tools to plot out the processes. The map to be created should include all the details of the source and the target systems, as well as details relating to how each system will access and alter data that it interacts with during the process. 

5. Data testing

The final step involves carrying out testing to ensure your map is accurate and valid. Testing can be done to ensure that data output through the process is behaving as expected. Test results can be shared through wider groups of stakeholders to ensure the data map will meet expectations and requirements. The final product is a validated and adopted data map resource.

Processes that use data mapping

You may want to improve data process, use and handling for your enterprise business. Several data activities benefit from the process of data mapping.

• Data integration - the average enterprise business runs around 50 separate software tool and programs, each often with its own data and handling processes. Data mapping helps data integration by giving you a clear picture of how these systems interact and understanding the ideal way in which they should share and use data. 
• Data migration - a data map is useful during the process of data migration because it ensures accuracy and consistency when you import data from one system to another.
• Data transformation - the process of converting data from one type to another is enhanced through detailed data maps.
• Data warehousing - a component of business intelligence and establishing a single source of truth, data warehousing involves establishing rules for where data is kept. A data map benefits this activity by giving you clarity about the handling and storage of data.

How should data be transformed and mapped? 

There are several steps you can take to prepare for data mapping to help ensure the activity is beneficial for your system transformation requirements. It is considered best practice to consider the following as you carry out your data mapping.

• Conduct data profiling- this step can help identify inconsistencies before you get to the point of data mapping.
• Define clear mapping rules - this means creating consistent criteria for the data to be transformed. 
• Format the data - meaning doing as much work as possible during the mapping stage to achieve standard field and file types across systems. This will ensure a degree of consistency within the target system. 
• Regular review - data transformation processes should be regularly reviewed and checked for performance.

Examples of data mapping issues 

There is the potential for huge variation in individual records and files across different data sets, files, programs, and spreadsheets. With so much data being drawn from diverse sources, it can be difficult to efficiently compare and combine data. Different software solutions store information and records in different ways, even if they have common categories of information, such as customer name or email address. Although common categories exist, they may be labeled differently. 
 
For example, you may have one system that uses a field customer name, another that uses customer ID and a third that uses client ID. In another example, you may have one system that lists customer addresses in a single address category. In another, each record may have a field for street number, street name, suburb and postcode. In this example, it can prove challenging trying to aggregate and create a master record for each customer when they are labelled so differently. 

And not only are there differences in the fields, labels and categories of records, but there may also be differences in what each field contains. Some systems might record all previous transactions in a single field or cell, where others will generate a new record for each.

How to ensure data consistency and accuracy 

Ensuring your data is as consistent and accurate as possible is an essential part of data mapping and transformation and needs to be done at the beginning of your implementation project. Taking the time to review data consistency and accuracy before you try and implement a system to span your entire network and gather data from complex sources will save you time in the long run and lead to a better result.

Providing clear and regular information about data upgrade and transformation projects is critical. So too is developing resources, guidelines, policies and procedures related to data management and expectations. 

Because just about everyone uses data and information for their work, it is also important to involve your staff and stakeholders in the processes. To help ensure data consistency and accuracy, you can: 

• document data standards - involving all staff and stakeholders in taking steps to establish and implement a set of data standards will mean everyone is familiar with expectations and is working to improve overall data consistency. 
• implement data validation rules - behind the scenes, you can also run data checks to check for data validity, consistency and accuracy
• use data profiling tools - to analyse the data and identify any data quality issues. Data profiling tools can help identify inconsistencies, duplicates, and other data quality issues that need to be resolved.
• Implement data governance ¬- the delivery of policies and procedures to ensure data is managed effectively and consistently. This involves defining roles and responsibilities for data management and implementing processes for data acquisition, storage, transformation, and use.
• Use data quality metrics - to monitor data consistency and accuracy over time. Data quality metrics can help identify areas where data quality is improving or declining and enable you to take corrective action as needed.
• Provide data training – to stakeholders to ensure that everyone understands the importance of data consistency and accuracy and how to achieve it. This can include training on data standards, data validation rules, and data governance policies and procedures.

Can AI Help with the process of data mapping?

The amount of data held in the average corporate data network is expanding exponentially and at a rapid rate. The process of data mapping is becoming increasingly complex, and in many cases, automated tools are required to make extensive data mapping possible.

Artificial Intelligence (AI) can help with data mapping by carrying out a number of processes for you, through the automation of activities such as data review, assessment and reporting. Options for the use of AI in data mapping include:

• automated mapping - through which the AI tool observes and learns relevant algorithms to assess data and identify relationships and usage between different data sets
• data validation - through which the AI tool detects errors or gaps in data
• data enrichment - through which AI uses predictive tools to fill in any gaps in data sets, adding elements or attributes 
• ongoing maintenance - through the AI tool assesses data quality and use and offers suggestions for improvement in handling or process

However, we are not quite at the point where we can rely on AI to completely handle data mapping activity. Despite advancements in AI technology, data mapping still requires human set up, direction and moderation.

Using a data mapping tool

Data mapping tools do the work showing you how data can be mapped right through from source to destination. Using a data mapping tool also helps eliminate or reduce the risk of human error, by ensuring accuracy. There are technological solutions available that can carry out data mapping for you.

Today, cloud-based data mapping tools are fast and flexible, and can save you a lot of time and labour. They can be used to automate repetitive and time-consuming data tasks, to achieve a more accurate single source of truth. Data mapping tools can handle all types of file types and formats, and use data from enterprise solutions. Data mapping tools can also work to a set schedule, following instructions about when and how to assess data.

Related Questions 

What are the best free data mapping tools?

According to software review website G2, there are more than thirty data mapping software tools on the market today. Although these data mapping tools can be costly to run, there are several free options available for enterprise businesses. There are 11 options for free data mapping tools profiled on the site. These are

1. Boomi
2. Peregrine Connect
3. Transcend
4. Pimcore
5. Astera Centreprise
6. Altova MapForce
7. CloverDX
8. Ethyca
9. Maltego
10. HVR
11. Etlworks Intergrator