12:20 | 13:00
Keywords defining the session:
Takeaway points of the session:
- Data Governance is essential to drive business value in Big Data environments.
- Best practices include using specific methodologies that embed Data Governance processes, and using specific products to help automating as much tasks as possible.
The title of this proposal, “Big Data Governance Challenges” can be read in two ways: Data Governance Challenges for Big Data (the obvious way), but the reader can also understand that Data Governance Challenges nowadays are big, really big! As data volume and variety keeps growing and growing, the complexity that needs to be managed to generate business value raises accordingly. We will call this complexity “Data Entropy”.
Data governance can be defined as the formal orchestration of people, processes and technology that enables an organization to leverage data as an enterprise asset by properly managing Data Entropy.
Why Data Governance?
Raw data in the Data Lake has little value, it needs to be refined in an efficient an agile way to be understood and to allow organizations to generate business value in different ways, such as increasing revenues, reducing risks or driving competitive advantages. Refining huge amounts of heterogeneous data which is rapidly generated, both in streams and in batch processes, is a cumbersome task and during the process questions arise like: What does this data really mean? Where does it come from? Can I trust it? Am I allowed to use it in any way? Who may I ask help about this data? Data Governance is needed to answer all this questions.
How do we put in place Data Governance in a Big Data environment?
We should follow three rules:
– The first rule is to keep it as simple as possible (assuming that as simple as possible may become quite complicated to be effective)
– The second rule is to automate as much repetitive tasks as you can
– The third rule is to always keep focus on business value generation
In our view, the key to a successful Data Governance strategy is to follow a comprehensive methodology that applies best practices, and proven experience to guide a project team from project launch through completion. A solid methodology provides a standardized systematic framework for approaching projects consistently, while maintaining control and equilibrium through a successful delivery.
DXC Global Method (DXC GM) for BI & Analytics Implementation is an example of a methodology which offers highly successful, documented approaches for performing vital planning and development activities in a coherent, repeatable, and accountable manner. Drawing on DXC’s world-class experience helping Global 2000 companies solve complex data problems and manage “big data” volumes and issues, these methodologies apply best practices and repeatable agile processes to maximize the opportunity for a successful and sustainable delivery.
Data Governance is considered a core component of any successful Analytics or Big Data scenario. In order for a Big Data solution to be successful, the quality of the data must be managed, the security and compliance procedures must be considered, and business ownership of the data must be established. In order to manage all this aspects, governance mechanisms are required. Data Governance elements are therefore embedded throughout the content of this implementation methodology, so Data Governance definition is not seen as a stand-alone process.
For automating as mucho tasks as possible, technology and specific tools are a great help. For example, AnalytixDS Epicenter Data Governance, Security & Compliance Center (https://analytixds.com/epicenter/) helps bridge the missing gap between business and IT and provides a platform consisting of People, Processes, Policies and technology to create a robust medium to handle vital business information through a set of proven methodologies and deliver trusted & secure data that helps organizations to prioritize business goals and connect to the most important critical business objectives.