Tackling The Constraints That Are Keeping Data Innovation In Check
25 June, 2018 / Articles
In the past, enterprise data usage was like a string quartet: Data was largely relegated to a smaller, cohesive team of operators, developers and analysts. This is no longer the case. The promise of data to drive innovation and streamline business operations has spread to every part of the organization. From engineering to marketing and human resources — everyone wants a piece of it. Your string quartet is now an orchestra, big band and marching band all thrown into one.
Taming this discord requires everyone to play their part. It’s not just data scientists and data engineers, but the database administrators (DBAs), developers, system administrators and security team that need to come together so everyone has the data they need to bring the music to life. When data is flowing everywhere it needs to be in your enterprise, it’s a beautiful thing.
Flowing Within Constraints
Every organization has constraints, even those elusive unicorns leading the charge. Whether it’s security, information technology (IT) infrastructure or organizational silos, there are countless hurdles to overcome to enable data access in a meaningful, efficient way. Instead of letting these challenges get in the way of development, start with the business outcomes and prioritize addressing the most critical impediments on your data journey.
Data privacy looms large amongst these challenges. Companies want to leverage the cloud for elastic development or machine learning training environments. They want to empower outsourced partners with fresh, clean data to drive higher quality projects. They want teams to have self-service access to the data they need to get their jobs done. But when they look at the data involved, they cringe in fear at the specter of data privacy. Nearly five million records are lost or stolen every day, according to the Breach Level Index, and no one wants to be the next Equifax or Cambridge Analytica.
Instead, teams turn to synthetic data — decreasing quality and velocity or shadow IT — increasing risk. Tackling this problem requires understanding how data flows across privacy domains: from user to production, production to non-production, from non-production to outsourced partners. Companies can then employ techniques such as data masking and de-identification to reduce risk in their data as it flows across organizational, technological and physical boundaries. This is just one example of how understanding the constraints and outcomes can drive real change in your data infrastructure.
So, chase your dreams while being ambitious and realistic at the same time. This is not an all-out sprint to keep up with the Joneses in the WeWork down the hall or to catch up with tech giants with limitless resources. Understand where your data is today, how it ties into your business initiatives, what its constraints are and what its true potential is. Only then can you think creatively about tackling the tangible barriers in your data infrastructure to enable data to flow where it needs to. It’s a matter of understanding the reality of your own unique data (both opportunities and challenges), then using that reality check to establish a more sustainable and stronger data infrastructure that will set you up for success for years to come.
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