Navigating Data Security Within Data Sharing In Today’s Evolving Landscape
While every organization of a certain age is saddled with legacy technology, very few can afford to upgrade to meet modern needs. Instead, successful organizations must build cross-enterprise data strategies that incent data sharing while allowing the organizations to continue to evolve their systems. This can be achieved using business-driven microservices architectures for enterprise-wide transformation – with security as the backdrop.
Building a Plug-and-Play, Microservices, Data-Sharing Architecture
In a microservices architecture, the emphasis is on building a set of business capabilities that are self-contained, autonomous, and loosely coupled, which enables independent and manageable lifecycles.
Successful cross-enterprise data strategies bring a unified approach to data integration, quality, governance, and data sharing. Innovation is not through a set of siloed products. It is a single platform that moves and manages different types of data under one roof. To create a successful data management strategy and avoid any data security mishaps, chief data officers (CDOs) and their teams should start by setting up governance and establishing business rules and system controls for access.
CDOs report the most success when their data sharing architecture is built on microservices that answer business questions. That is, what data is needed to provide insights into the most difficult business problems? For example, the CDO of a large Internet-based home furnishing company recently shared that when they treat data integration as a business transformation project, they receive better requirements about business needs, data security and data trust, more focus from stakeholders, and broader adoption across the organization and within roles.
Another best practice approach that both encourages sharing while also only labeling trusted, vetted data sources is the concept of certified versus uncertified data sets. A large, US-based fitness chain allows datasets to be published within its data sharing architecture early in the process, but only data sets that have been vetted for data quality, data security and the authoritative source for the data are eventually certified. Access to uncertified data sets is restricted through requests to the data steward, whereas certified data sets are made more generally available.
With a business requirement-driven, microservices architecture-based, data-sharing platform in place, the organization is posed to modernize its systems while maintaining data sharing and data security requirements.
Upgrading Legacy Systems with Uninterrupted Data Sharing
The majority of experts agree that IT systems need to be refreshed every three to five years for larger companies while smaller companies may be able to get by with replacing on an “as-needed” basis. However, there are many reasons for companies to continue using legacy systems.
Many CDOs are concerned about the cost required to upgrade the ecosystems that house their mission critical data. Although maintaining a legacy system is expensive over time, upgrading requires an up-front investment, both in dollars and human resources, and for some, this can be very daunting. There is also typically some internal resistance to change that CDOs and their teams must overcome. This process is even more challenging when legacy systems have obsolete programming languages. These complex homegrown legacy systems often have poorly documented, complex business logic coded into the applications, and the individuals who wrote and maintained the systems have long since retired. While there are significant benefits to the business in upgrading to a more modern system that can properly leverage the data and provide additional operational and strategic insights, the perceived risk to the business is high.
The key to overcoming this challenge is to adopt an iterative approach that does not assume a direct like-for-like replacement for existing systems. A new system may consolidate a number of old systems or, conversely, a large homegrown legacy system may end up being segmented differently across newer systems. An example of this is a large tools manufacturer consolidating 70-plus enterprise resource planning (ERP) systems. This replacing and merging of ERPs over time is being done in parallel to the creation of authoritative data sets that are published across systems leveraging their microservices architecture. The end result is a single authoritative source of data available before the modernization effort completes.
Organizations must establish which systems are most in need of an upgrade and begin the process of modernizing systems. Within an established microservices architecture, legacy data sources can be easily swapped out with the modernized systems without larger architectural refactoring.
Keeping Data Security Top of Mind
Throughout this process, CDOs need to keep data security top of mind. A lot of this has to do with the context of the data. For example, government agencies that handle sensitive information must keep track of who has access to what data because the need to know may shift as the context of the mission shifts. In modernizing infrastructure, it is vital to create a system that will automatically track and update privacy and accessibility for confidential data to ensure the utmost security. Organizations that do not prioritize data security throughout their data sharing strategy put their data at risk of exposure and expose themselves to legal and reputational risks.
Data security protection best practices combine technical access controls with human-centric processes that put responsibility for vetting the need for access on the data steward of the data set. One aerospace company has followed this model and shifted to being a data-driven company by allowing data sets to be discoverable with context and making the data steward for each data set responsible for determining if access is appropriate from a security perspective. An oil & gas company implemented this model after struggling with the breadth of the data and the variety of the security processes associated with the data sets made it unrealistic for centralized control. Moving to a model where data stewards administered access facilitated data sharing across silos where appropriate.
Navigating data security in today’s evolving landscape continues to challenge businesses, especially as the COVID-19 pandemic highlights the difficulties many organizations face as they deal with outdated infrastructure. By taking these steps to become a data-driven company, CDOs help their teams understand the organization’s business goals and available resources and the overall direction of the data management market. Data is a company’s most valuable asset and will ultimately help create a future-proof, secure strategy to enable business success.
About the author: Julie Furt leads global delivery at Talend, where she is responsible for ensuring customers realize business value from Talend products quickly and efficiently. She is a globally recognized, award winning, regularly published industry leader in enterprise software adoption. She has extensive experience managing high growth, high performing global customer success teams and has worked with clients across a variety of industries in 20+ countries.