Whether it’s big or little, data has the power to drive innovation and transform the enterprise. Today, retailers are leveraging behavioral data to deliver personalized shopping experiences to consumers, banks are analyzing purchase activities to detect and prevent fraud, and healthcare companies are combining patient and social data to advance precision medicine.
Still, many enterprises seeking to unlock the full potential of data still encounter challenges. Putting data to work has been harder than it should be.
Today, data is coming from everywhere—mobile applications, the cloud, IoT, social media. A lot of it is unstructured. Business data internal to the organization is more complex and varied than ever before. And data streams from outside the organization–as valuable as it is–pose new privacy and compliance dilemmas.
In other words, your data today is far more challenging than it was even five years ago. Even expert data users have trouble finding and trusting the right data for their needs. Business users—the people who are arguably making the key decisions to move your enterprise forward—are hard pressed to access, understand, and trust data. For a while now, businesses have been introducing self-service business intelligence tools to try to close that gap. But if users don’t trust the data they find, can’t understand what it means, and have no insight into its sources, that data will likely remain where it was: sitting stagnant in a very expensive data lake.
Data governance drives better analytics
Putting data to work requires an enterprise-wide ability to find, understand, and trust data. And that takes good data governance. Why? Data governance gives you a systematic way to manage your data and its availability, usability, integrity and security. Data governance helps organizations improve data usability, quality and value. For users, good data governance makes data transparent and usable, building confidence and trust.
Define critical sets of data
Starting with the critical data is crucial to effective governance. Critical data often includes some structured information from the enterprise, but in today’s world things such as chat logs, Facebook data, Twitter feeds and sensor data are often crucial to new, value-added processes and activities.
Manual processes can’t keep pace with the variety and breadth of today’s data. Automating data governance can help you maintain policies across the enterprise as your data continues to expand. With automation you can track data history, lineage, and ownership easily and track the impact of any changes you make to data standards and policies in the future.
Don’t forget to govern your analytics
The data governance approach, and the system that automates it, should have the flexibility to capture information about all aspects of your analytics, from Map/Reduce jobs to visualizations.
Build a data governance team
A strong data governance council should include the chief data officer and executive sponsors from around the organization. Data stewards and subject matter experts must also be empowered to constantly augment, improve, and enhance the data and its information.
Without data governance, new data initiatives can unleash a big mess of trouble: misleading data, unexpected costs and risk of regulatory violations. Data governance is a framework for setting data usage policies and implementing controls designed to ensure that information remains accurate, consistent and accessible. It enables organizations to provide a set of information to their users, making it possible for them to leverage the power of their data.