Data transformation is changing the way enterprises do business. The adoption of advanced technologies like robotics, AI-driven software, and chatbot-enabled services are accelerating because enterprises benefit from positive outcomes. Here’s a quick guide.
What are examples of enterprise data transformation?
Examples of data transformation are everywhere. At FedEx, robots handling packages in the distribution and routing facilities help increase efficiency and reduce error rates culminating in improved customer satisfaction and repeat clients. In the past year with increased online demand, American Eagle deployed robotics to sort clothing. This has improved order fulfilment cycle times and provided their clients with a better buying experience. Similarly, IBM is now using AI-driven chatbots to improve customer service responsiveness.
These are all pragmatic uses of intelligent data to drive a positive impact on the business. The one common and critical element for effective use of such advanced technology is having reliable data. Without a reliable set of data, the automation process that is instantaneously making critical decisions is at risk. Let’s explore what it means to get reliable data?
What are some challenges facing enterprise data transformation?
These are all pragmatic The challenge is managing the process to rationalize data from disparate sources, assimilate that data, achieve an acceptable level of data quality, and then maintain it as a single source of truth. Most enterprises today have numerous sources of data both within and outside their organizations. This data, if used in the right context, is pivotal to making better decisions or taking actions to drive data monetization. Gartner defines data quality solutions as “the processes and technologies for identifying, understanding and correcting flaws in data that support effective data and analytics governance across operational business processes and decision making.”
Data transformation involves all of this and more. To begin, the enterprise needs to understand the data source options, and put the context of relevancy into that data-based with context on the business problem to be solved. Typically, the functional data analyst, in conjunction with the data steward and the data engineers, should be focused on comprehending the compatibility of data across applications, the sources, the types of data involved and the implications of data sources on the end goal.
What is a data transformation lifecycle?
Embarking on the data transformation lifecycle, starting with data access and sources, enterprises need to establish business domains for data along with appropriate data policies. Depending on the type of data, enterprises may want to isolate restricted data from sensitive data, apply different privacy rules and potentially access controls. As an example, the enterprise could decide that ‘Cust Name’ is a better way to define all future uses of Client Name and hence establish the data domain for Customer Name. Since data used for different purposes can be defined in diverse ways, the degree of data transformation can vary by organization and by a data element. Once the data policies are established, the next step would be to validate the extracted data using the data policies and where appropriate curate the data to align with the acceptable use of that data. This is a critical step in the data quality routine and puts the enterprise on the right track to a reliable data repository. With today’s cloud infrastructure technologies and the fact that storage is no longer an expensive investment, enterprises have adopted the ELT (Extract, Load and Transform) methodology. This means it is more efficient for enterprises to consolidate disparate data into the end state data repository and then apply transformation logic to assimilate, optimize and validate prior to initiating automation routines.
Data is useful only when monetized. To get there, data transformation is a critical success factor. It requires enterprises to invest in the entire lifecycle from data access through validation. With data going through constant change, data transformation is an iterative process. Think about a business process in your organization that would help impact the business today.