Big Data Governance: Information Collection, Storage And Policies
Task: Write a research report of about 3000 words focusing on one of the following topics
- Big Data privacy
- Big data governance
- Strategic issues stemming from BI&A and big data
- Change management issues stemming from BI&A and big data
Big Data governance is a framework adopted by the organisation in order to safely and securely store all the information collected from the various products and services made available by said organisation. This framework allows effective management, storage, validation and reliability of the data provided and collected by it.
This report will explicate the different aspects related to Big data and Big Data Governance. These will include explaining what big data is and what does big data governance imply. The report will also discuss the need for big data governance, its benefits and the framework of big data governance along with the challenges faced by organisations and the different tools that provide Big Data Governance.
What is Big Data?
Information is collected by the organisations through different means like the products and services provided by them, social media interactions, emails, surveys, website registrations and so on. All the data received from these various sources tend to be in abundance. Such a kind of data is known as Big Data. Big data can be defined as data that is so high in volume that it is difficult to manage it using the traditional means like Data Warehousing. Different big organisations like Google, Facebook, Youtube and LinkedIn have adapted different methods that will help in the management of Big Data. (Constantiou & Kalliniko, 2015).
What is Data Governance?
Data governance is the base for efficient and integral data management. Data governance consists of processes, people and tools to proficiently manage data available in the organisation. Data governance guarantees that the data made available is accurate, reliable, comprehensible and secure. (Forthergill et al.2019)
Data governance includes:
- Data Quality- Maintains the quality of data
- Data Architecture- Creates an architecture to store the data securely
- Data design- Creating a database model with logical and conceptual levels
- Data storage- Selecting a storage medium to store the data digitally
- Data Security- Securing the data from an external or internal mishap
- Metadata Management- Managing the metadata of the actual content data
- Data integration- Making sure that the data and its source is reliable and usable
- Data warehousing- Creating the data warehouse for data analysis and reporting (McAfee et al.2012)
What is Big data Governance
The process used to manage, availability, integrity security and usability of data is called as Big data Governance. It is a small part of an extensive information governance program that formulates strategies to secure information and keep them safe. (Scheepers & Deschamps, 2016).
Need for Big Data Governance
All big organisations have ample amount of data that is helpful for the organisation to progress further. This data is known as big data as the amount of data is more than any other traditional means are not capable of handling it. In order to store this data comprehensively, data governance becomes important. Data governance helps the organisation to store data effectively and securely. Through this, data management becomes easier as there is no repetition of data. Since the data is store efficiently, analysis and reports related to the information stored can be drawn out eventually. Data governance not only stores the data but also the ‘data of the data’ called the metadata and allows management of that data.
The following questions can only be answered through big data governance
- Where does the data come from?
- What processes are used for transporting data through the organisation?
- How can the data be transformed?
- Can the source of the data and the data be trusted?
- Can an accurate analysis be derived using the data?
- To whom is the data available?
- How is the data used?
- Who owns the data?
- Who is responsible for its security?
Data governance allows the company to be run more efficiently and smoothly.
Benefits of Big Data Governance
Big data governance has the following benefits to the organisations so that they can prosper and safeguard the data at the same time. It allows data to be available, usable and manageable.
Maintain availability: It is important that the data governance process makes data available to the users at any given point of time. Along with availability, the process should also have options to recover that in case of technical issues.
Anyone from the organisation and also the people residing in different geographical locations should be able to access the data without any problems.
Securing Sensitive Data: Data security is one of the important tools that a data governance process should provide. It should integrate a secure environment for data storage. The data governance process should include different authentication modules like the Linux Pluggable Authentication Module in Mapr. All the data stored should be encrypted at all times when the data is still, in use or being transferred. (Yamada & Peran, 2017)
Access the Data at High Speed: Data governance allows the data to be accessed readily and quickly. This benefit is in synonym with the availability as it makes data available at all times. However, it is important that the data is available instantly and without any delay at any given point of time. Data governance makes it easy for the information to be searched and accessed.
Know the data: Data governance allows the company to know all the data that is available to them. It allows companies to draw out predictive analysis and garner unique insights regarding the data.
Some other benefits include:
- Improves the quality of data
- Makes data comprehensible
- Explicates Data Lineage
- Helps using data and improve the ability of decision making
- Helps in adopting the correct Regulatory compliance
Big Data Governance Framework
Creating a data governance framework means developing a model to store data from the organisation. This framework has rules and regulations that need to be followed and is created according to business regulations. This framework is able to manage and manipulate the data stored in the system.
When a data governance framework is adopted, the staff and employees of the team responsible for managing the framework is created. This team is accountable for not just the management of the data but also produce a governance system that is able to adopt business strategies and create rules required for safe guarding the data of the organisation. The system adopted needs to be able to answer different data related questions and be able to keep up with all the big data collected by the organisation.
Adopting Data Governance
Adoption of a process has a different meaning than the actual meaning of adoption. In the business terms, it means creating a completely different team with people that are mainly from different parts of the organisation to come together and work. While selecting the right process of data governance, it is important for the organisation to locate the part of the system that requires improvement. Once identified, these areas are then converted into datasets.
Each dataset has a separate team that is responsible for the data. The roles, responsibilities and problems are distributed among different teams that will be responsible for that specific problem only. It is the liability of these teams to understand, segregate, clean, transform and enrich the data as required for the company. This allows the team to improve the quality of data. The team responsible for the data set becomes the owner of the data included in that dataset. It is the duty of the owner to check the accuracy of the source of the data and the data itself and create processes to maintain the high integrity of that data. This changes a company's structure from a transaction based on a master data based company. Finally, a feedback mechanism is needed in order to understand the shortcomings of the process so that there is a timely improvement of the process. (Chen et al. 2012)
Problems to adopting big data governance
While adopting data governance is important, there have been different problems faced by companies while selecting a data governance process.
- Lack of participation: Lack of participation occurs when the members of the organisations are not clear about the objectives of applying data or may not know that data governance has been incorporated.
- Lack of education and training: Providing training implicates that the staff is able to understand the data governance process. In order to encourage a data driven culture, it is important that the staff associated with the organisation is made aware of the process through educational pieces of training and activities. These will in turn increase participation in the process by the different staff and employees associated with it.
- Lack of business strategies: There need to be objectives defined in order to undertake a data governance process. As it is important to give high priority to business challenges first, safeguarding data is essential.
Best practices for Data Governance
There are five best practices defined by experts for effective Data Governance.
- Keep it realistic: It is important to get different departments to be on board with the data governance initiative. At first, it is difficult to effectively discuss the set of business rules and the way to plan the development of the framework as there are bound to be disagreements. According to experts, it is important to keep moving forward with the discussion instead of halting it at disagreements. It is better for the discussion to continue rather than stop as there are opportunities of reaching an agreement at some point of time in the discussion. It is advisable for the team members to be more flexible during the discussion so that all business rules and regulations are discussed thoroughly along with how it affects the governance framework selected by the company. If there are any changes or better and effective solutions, they can be incorporated then and there in the planning itself. (Hannah-Moffat, 2018)
- Top-down and Bottom-up Approaches: Selecting only one of the two approaches may hinder the development of the process. The top down approach means going down from the executive level, while bottom ups include incorporating the grass roots policy. The approach selected should depend on the of the part of the organisation the framework affects. If the frame work affects the entire organisation, the top down approach is advisable. However, if it is required only for a single department of the organisation instead of the whole, a bottom up approach can be selected. (Attard & Brennan, 2018)
- Partnerships between business and Information Technology: It is important to strike the right balance between information technology and business when developing and evaluating business rules and policies. The employees cannot be expected to adhere to the change in policies without any notice. There is a need to enforce rules slowly and steadily and provide incentives to those who are willing to comply to the new set of rules and regulations set up by the company to develop a data governance framework. (Manyika et al.2011)
- Business Glossary Updatation: A business glossary contains all the new definitions that have been decided by the team responsible for creating the framework. This business glossary is to be shared effectively with all the employees associated with the company. Therefore it is recommended to begin the development of the business glossary as the process of planning begins. This will ensure that there have been no meanings left out or missed will collating the final documents related to the data governance framework that has been adopted by the company and the different changes brought about in the business rules and regulations of the organisation as a whole. (Gleeson & Walden, 2016)
- Using common tools: Using tools that have been commonly used across the different companies adopting a data governance framework is necessary as they ensure that not much time is wasted for training the new staff. Having a proprietary software is good but training the new batch of employees to use the software leads to a waste of time and energy. Instead of hiring staff that is able to use already software used by the organisation makes it easier for the new staff to settle in and makes time management easier for the company and also lets it save finances that are otherwise used on the training and new software development and updates. (Ketter et al.2015)
The proper approach to Big Data Governance
It is important to approach data governance carefully and accurately. For developing a successful big data governance framework, the model should be business oriented that will give importance to the understanding of company-wide data. Proper tools and methods will facilitate in developing a successful big data governance framework. This will help all the people using the data to do so willingly, easily and safely. In an organisation, data is stored and transported across different platforms offered by the company through integrations, applications and interfaces. Using an approach that is well-known between the people of the organisation is effective in bring both the important aspects of the framework together, people and data. If the approach is well known, people will be able to access the data easily without any kinds of issues or problems. The organisations will not have to conduct a different training session for the employees to understand how the big data governance framework will function, which will, in turn, lead to time waste and unnecessary fund usage. (Drost-Hansen et al.2017)
Building a Big Data Governance Strategy
While creating a Big Data Governance Strategy, it is important to keep the following points in mind:
- Big Data Governance should be consistent with company objectives:
There are times when some changes have to be made in the company policies while adopting a big data framework. However, it is important to understand that data governance will only be managing the data aspect of the business and not the whole organisation. Therefore, it is important that the big data governance framework that is adopted remains inconsistent with all the existing business policies adopted by the company. Completely changing all the policies to match a specific framework will lead to bigger changes in the organisations than what is required. The frameworks for data governance are flexible and can be changed according to the' organisations' requirements. (Hall, 2016).
The I.T. team of the company is specifically responsible for the development and maintenance of the big data governance framework that has been decided to be adopted.
- Setting up the Data Governance Team: The Big data governance team is responsible for the planning, development, testing and implementation of the big data governance framework. The data governance team will have team members similar to the I.T. team of the company. The roles and responsibilities will include both the members of the I.T. team and the business team. This team has the responsibility of discussing the different business related regulations and how that will affect the development of the framework. The team also decides on the business glossary that needs to be created and the changes in the business policies that may be required in order to incorporate the big data governance framework. This team follows a top down approach as is common within the organisations since this framework will affect the entire organisation and not just a single department. Therefore, a top down approach seems to be more plausible than a bottom up approach. Big data governance frameworks usually affect the organisation as a whole rather than a single department. (Koltay, 2016)
- Communication with all the employees of the company: Only the Big data governance team will be the point of communication with the different issues and questions regarding the framework. It is also the only point of contact that will provide all the framework related information to the entire organisation. (Korhonen et al.2018)
- Big data governance is an ongoing process:
Unlike any other products or services of the organisation, a big data governance framework is an ongoing process. There has to be a well defined team that will look over the completion and evolution of the framework. In order to understand if the framework is correct, it can be compared with the data governance maturity model. (Kallinikos & Constantiou, 2015).
A Maturity model is a multilevel graph measured against risks and revenue generation by the framework. These levels help the team to track the progress of the organisation framework and optimise the process as it progresses. (McAfee et al. 2012)
Big Data Governance Tools
- Apache Atlas: It is a Hadoop based framework. Apache Atlas is supported by the different components of Hadoop in order for managing metadata and organisational data. All the metadata is stored in a central repository from which the data can be accessible for the organisation. This can be done using tags. (Sharma et al.2014)
- SAP Master Data Governance: SAP Master Data Governance, too, is a repository based that is useful for maintaining the data quality and data policy management. It makes the data accessibility easy by providing glossary terms that have been used to develop and evolve the business policies and regulations. Data ownership too becomes easier.
- Alation Data Catalog: Alation Data Catalog allows the users to have a single point of references for the different data sources just like a normal magazine catalogue. It helps in automation of governance tasks like updating data dictionaries and educating the staff about the different governance practices. It also provides a collaboration feature for sharing information with a different organisation. (Alhassan et al.2016)
It could be concluded from the above discussion that big data plays a significant role in the current global business scenario which needs appropriate policy and regulations to control and monitor the handling and usage of it. Therefore, the big data governance and associated policies and guidelines are essential in case prevention of data protection policies and data leakage. This report has discussed the different modules related to big data governance. Big data governance is a process to guard the information stored in the big data governance framework, allow easy access to the data and ease in management of the data. It also allows the generation of reports and analysis as required by the organisation without having to waste time in search of the information as it is readily available for the people to use. Different strategies and best practices have also been discussed in the report. Big data governance assignments are being prepared by our IT Management assignment help experts from top universities which let us to provide you a reliable assignment help online service.
Alhassan, I., Sammon, D., & Daly, M. (2016). Data governance activities: an analysis of the literature. Journal of Decision Systems, 25(sup1), 64-75.
Attard, J., & Brennan, R. (2018, October). Challenges in Value-Driven Data Governance. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 546-554). Springer, Cham.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.
Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: big data and the changing context of strategy. Journal of Information Technology, 30(1), 44-57.
Drost-Hansen, C.E., Error, C.R. and Grieshaber, P.J., Adobe Systems Inc, 2017. Controlling data usage using structured data governance metadata. U.S. Patent Application 15/139,059.
Fothergill, D. B., Knight, W., Stahl, B. C., & Ulnicane, I. (2019). Responsible Data Governance of Neuroscience Big Data. Frontiers in Neuroinformatics, 13, 28.
Gleeson, N. and Walden, I., 2016. Placing the state in the cloud: Issues of data governance and public procurement. Computer Law & Security Review, 32(5), pp.683-695.
Hall, J. (2016). Roadway Safety Data Governance in Illinois: Roadway Safety Data and Analysis Case Study (No. FHWA-SA-16-108).
Hannah-Moffat, K., 2018. Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates. Theoretical Criminology, p.1362480618763582.
Kallinikos, J., & Constantiou, I. D. (2015). Big data revisited: a rejoinder. Journal of Information Technology, 30(1), 70-74.
Ketter, W., Peters, M., Collins, J., & Gupta, A. (2015). Competitive Benchmarking: An I.S. Research Approach to Address Wicked Problems with Big Data and Analytics. MIS Quarterly. (forthcoming)
Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA journal, 42(4), 303-312.
Korhonen, J. J., Melleri, I., Hiekkanen, K., & Helenius, M. (2018). Designing data governance structure: an organisational perspective. GSTF Journal on Computing (JoC), 2(4).
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. 2011. “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute. http://www.mckinsey.com/business-functions/business-technology/ourinsights/big-data-the-next-frontier-for-innovation; access on 4 March 2016.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data. The management revolution. Harvard Business Review, 90(10), 61-67.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data. The management revolution. Harvard Business Review, 90(10), 61-67.
Scheepers, F. E., & Deschamps, P. (2016). 32.5 ARE YOU READY FOR BIG DATA? GOVERNANCE IN BIG-DATA RESEARCH. Journal of the American Academy of Child & Adolescent Psychiatry, 55(10), S309.
Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433-441.
Yamada, A., & Peran, M. (2017, December). Governance framework for enterprise analytics and data. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 3623-3631). IEEE.
Get Top Quality Assignment Help and Score high grades. Download the Total Assignment help App from Google play store or Subscribe to totalassignmenthelp and receive the latest updates from the Academic fraternity in real time.