Data Heroes face many challenges today because data is everywhere! The data ecosystem continues to expand, is more distributed – on-premises, in the cloud, in-stream, on-edge – and more varied than ever. Business needs are driving new demands from analytics and AI, and these needs all require data. Data scientists want to use this information to drive and derive insights. Data engineers need to build data pipelines to support the needs of the data scientist. Data officers need to understand the creation and use of the data while ensuring it is being managed responsibly. With these many challenges around the data and expanding business requirements, how can our data hero use big data in effective, timely, proactive, and responsible ways that can easily scale to drive insights, decisions, and value?
The Data Villains and how to overcome them
Data villains fall into two buckets. Those that prevent quickly identifying and getting to the right data for business use, and those that impact the timeliness of the data. The overarching goals of these nemeses are to destroy trust in the analytical outcomes while preventing timely and accurate decisions based on the data. However, our data heroes have their own secrete weapons to combat their villains.
Data Governance and Data Catalogs
The data catalogs quickly help the data consumers find the right data to solve business problems. With the data assets cataloged in a central location, the consumers can quickly search and explore the data available across the organization, can create their own data collections, and can rank the data sets for better transparency into the quality or usability of the data. Data catalogs build data literacy because it helps the business understand their data assets and what’s available to them. Of course, the data catalog needs another hero to be successful- data governance. This hero understands that the health of the data is important to securing trust in data and that protecting sensitive data is imperative to any responsible organization. This hero focuses on ensuring the data is high quality, provides centralized data ownership, can identify, protect, and secure sensitive data, and monitors the health of the data to promote decisioning wellbeing, and maintains responsible data practices at the organization.
DataOps and Data Pipelines
It is no longer a business best practice to wait hours or days for data. Data needs to be available to the data community when they need it and how they want it. DataOps and data pipelines provide the agility needed by organizations today. Using this approach, data transformations are executed where it makes the most sense with a focus on automation and data accessibility. Data pipelines allow data engineers and data scientists to dynamically access and prepare data for analytical needs. Weaving together DataOps and data pipelines ensures the organization remains nimbly and can proactively adjust to changing market conditions.
To learn more about real-life data heroes and the tools they use, please check out this on demand webinar: