Data-driven companies are 23 times more likely to beat their rivals at gaining new customers, seven times more likely to keep existing ones, and about 19 times more likely to stay profitable.
It’s clear that data can pay dividends. But before this can happen, your data needs to be capable of being put to work - and this demands a cohesive framework. Data mapping is essential precisely because it provides this framework.
Discover more about what data mapping means, how to get it right, and how it supports your data-driven initiatives.
What Is Data Mapping? Real-Life Data Mapping Definition…
What data do you hold? Where does it reside? What is its purpose? And how do these various data sources fit together? Data mapping gives you answers on all of this.
Data mapping involves tracking the flow of data to, through and from your organization. It seeks to align and connect data from all the various sources, systems, and formats you have in play. Get it right, and data mapping helps ensure your data is rendered fit-for-purpose for whatever you need to do with it.
What Is the Purpose of Data Mapping?
Supporting digital transformation
What’s the next big project in store for your IT team? Are you looking at enhanced operational planning through Business Intelligence (BI)? A more joined up approach to customer relationship management (CRM)? Enhanced strategic capabilities through enterprise resource planning (ERP) or corporate performance management (CPM)?
These - and most other transformation initiatives - all have one thing in common: they demand the ability to draw together different types of data from different parts of the organisation.
No matter how sophisticated the technologies you are seeking to implement, the principle, “ in, out,” always applies. Data mapping helps ensure that the data feeding into your tools is accurate, consistent, and usable.
Enabling integration
Over time - especially as digital transformation gathers pace - the sheer volume of data sources in play can increase significantly. In fact, one survey suggested that the average number of data sources per organisation is 400.
For all of these sources and systems to work together, it’s essential that data flows smoothly between them. Data mapping aligns these sources (e.g. by reconciling the ways in which fields are named and organised). This ensures that data can be shared and understood across your technical ecosystem, reducing the likelihood of mismatch and error.
A single source of truth
Where did these conclusions come from? Why is the finance department’s figure for revenue different to the sales team’s numbers?
For business insiders to embrace data-driven decision making, they have to be able to trust the numbers. Data silos can be a major barrier to achieving this; i.e. different departments processing data in isolation, using their own sources, formats and structures.
Data mapping performs a key role in breaking down these silos, ensuring that data flows smoothly between systems, and that all stakeholders can access and rely upon the same information.
Achieving compliance
Data mapping enables you to track what data you control, and why you hold it. This is vital in the context of personal data; particularly in the light of data protection regulations.
Under GDPR for instance, most organisations are required to keep an up-to-date record of all data processing activities. Data mapping makes it easier to do this in a systematic way by allowing you to identify the flow of data through the business - making it much less likely that any data processing activities are overlooked.
Most data protection frameworks - e.g. GDPR, HIPAA, CCPA - focus strongly on transparency; the duty on organisations to be as upfront as possible on what happens to the personal information they control. Data mapping can make it easier to give this information to customers and other data subjects in the clearest, most accurate way.
What Are the Stages of Data Mapping?
Broadly, effective data mapping involves the following:
Data mapping scope
The point of data mapping is to render your data and data pipelines fit-for-purpose for whatever you need to achieve with the data. So it’s important to be clear your objectives from the outset; for instance to render it usable with a specific data analysis tool you are planning to implement.
Discovery
This involves mapping the ‘as is’ data state of your organisation. There are two main parts to this. First off, processes; identifying every situation where data is being processed. Secondly, storage; establishing what data resides across all servers, platforms, and endpoints.
Identifying data relationships
Not all data fields are equally important. You need to ensure that your mapping enables you to use precisely the fields you need to meet your end goal. Analysing data relationships helps you understand how different data points connect, making it easier to isolate the ones you need, and eliminate those you don’t.
Create a data mapping specification
This is your mapping blueprint, setting out key rules for data flow and integration. Key elements include field mappings, relationships between source and target data, transformation rules, validation criteria, and any other procedures needed to make data fit-for-purpose.
Deployment
Finally, you deploy your data map. Post-deployment, be mindful that data mapping is not a once-and-for-all exercise. The way in which data moves across your organisation tends to change. The introduction of new tools, new storage methods and new business initiatives can all render an existing data map out-of-date. Continuous monitoring is required to address any problems, or changes in data requirements.
What Is the Role of Data Mapping Software?
There are multiple pitfalls associated with data mapping - especially if you attempt to do everything manually.
As just a couple of examples, different IT team members can easily apply varying interpretations to mapping tasks, jeopardising that all-important consistency you are looking for. Data relationships can be overlooked or misinterpreted. And of course, the larger your IT estate, the more time-consuming it can be; potentially jeopardising your timelines for interdependent projects.
- Dedicated data mapping software directly addresses these issues. Key capabilities to look for in data mapping tools include the following:
- Automated discovery, including scanning and classification of assets across your data estate.
- Data transformation: automatically converting rules set by you to convert data into your required format or structure.
- Validation and cleansing; detecting and correcting errors, and removal of extraneous or invalid data.
- Support for integration; facilitating the flow of data between different systems, including, for instance, real-time synchronisation.
