Big Data governance plays a vital role in firms for gaining success. Big Data technology is gradually growing day-by-day with ever challenging trends and technologies. The term “Big Data” we usually refer as bytes of data generated everyday globally in size of Exabyte or Petabyte. Big data is an evolving term that illustrates the voluminous amount of unstructured, semi-structured and structured datasets that have the potential to be mined for the information.
Importance of Data Governance in Big Data
Giant firms employing Big Data businesses are: Terabyte, IBM, HP, SAP, Amazon.com, and many more. Big Data supports search, development, and governance and analytics services for all Big Data services. With the emerging new trends and technologies, it’s harder to retain a firm in the competitive Big Data market. These days Big Data governance experts have been striving for the successful formulation of roadmap for powerful implementation of Big Data strategies. Emerging trends and technological challenges confronted by Big Data enterprises are:
- Employing Data Governance
- Discover the new job roles
- Who owns it?
- Flex up to big results
- Involve unstructured Content Management
- Business Value-View from the middle
- Set the dial on precision
- To be purely practical
- Formalize the Hand-off
1. Employing Data Governance
Data governance is a control, that ensures, the quality of data coming into the organization is good. It is the set of process that ensures in which datasets are managed formally by the enterprise. Data governance often primarily focused only on inbound data quality improvement. Without any proactive steps and stewardship, it’s hard to gain data governance on datasets. For example:
- Cataloging incoming data feeds
- Defining data standards
- Ensuring transparency of business rules to sustain lineage
With this, incoming results can downstream analytic environment by receiving inconsistent data. To refrain from such obstacles, you can use a good on boarding process and follow up, thus ensuring that the data is properly cataloged and easily discoverable.
2. Discover the new job roles
One must have a clear sense regarding the specific job roles of stakeholders/Key decision makers. The job role of key decision maker changes according to the movement of data in your eco-system and during its life-cycle event. The roles specified for key decision makers must be well defined when it refers to Big Data job roles. The enterprise must identify these stakeholders / decision makers before embarking its Big Data initiatives, and should prepare to refine and iterate the job roles in Big Data.
3. Who owns it?
Good governance is established by good business-driven data ownership. Though in practical reality, it is often difficult to identify a single valid data ownership. Data is being generated by individuals, but, are owned by the firm.
4. Flex up to big results
Firms must adapt data governance and data management processes to support the needs of Big Data technology and Big Data users. Data governance and data management is flexible that takes into consideration of context and discovery along with rigid operational and transactional needs.
For supporting Big Data concepts such as unstructured data streams, you must make sure whether your reference information architecture is updated or not. In order to correlate with all the basic meta-data elements, meta-data management features should be enhanced as well as, they should also support in rich data types as “tagging”.
5. Involve unstructured Content Management
Unstructured data procured from various sources is again an issue for firms to manage very large data sets. Big Data is about analyzing, exploring, and managing the unstructured data for gaining new insights that can be used to improve business processes.
Hadoop: Apache Hadoop is an open-source software (framework written in Java language). It is economical. The software is reliable and scalable, and helpful in managing very large datasets. It also supports database and analytics infrastructure. Apache Hadoop tool is supported by large and active eco-systems.
6. Business Value-View from the middle management
For any enterprise, it is hard to define governance activities. Middle management is very dubious about the governance as, they have real deliverables with the deadlines to match with limited available of resources.
Good data governance will identify key integration elements with the strategic initiatives and existing processes. Good governance adopted by an organization will help an organization understand and to leverage data into well format.
7. Set the dial on precision
What level of quality and precisions you desire to have? Be very specific on what you intend to do with your data. You might have exploratory data that supports production grades and early decision, thus supporting financial reporting.
For achieving higher quality and precision of the data, it requires a robust data management and oversight. Once your organization matures with Big Data, consider a precision classification approach and quality establishing that will allow data users to understand the components used and how effectively it works matching up their expectations.
8. To be purely practical
Firms are lacking in skills and understanding the demands for buy-in business for supporting governance. It’s very important for businesses to implement and adapt data governance in a pure and practical manner for improving very large datasets.
To implement a pure practical approach of governance in an organization follow the below steps:
- Make use of the onboarding process, the technique makes use of simple skill to perform a good job.
- To make learning business/IT professional about, how making the governance works as a natural extension in your existing work (pure way)?
- Prioritize your data according to the work and place.
- It can ease the workload and timing of business/IT professionals.
- Approach for change management for calibrating your program to pace your organization.
9. Formalize the Hand-off
As the data evolves gradually, it is hard to sustain leverage for highly valuable data. You can make use of the agile method for promoting your data development efforts.
Agile methods allow an organization to achieve big results by encompassing Big Data technologies, ongoing technologies and advanced analytics. The agile method is also useful for gaining useable decision-support.
The benefits gained by using agile method are:
- Measures the value you gain from each step
- Save money by avoiding waste
- Faster realization value from your data
Thus, small businesses, those that are just start-up in the Big Data market, find it hard to deal with such huge datasets. The resolution for handling and managing such datasets, firms have to overcome the technical challenges for gaining success over Big Data firms.
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