What is Big Data Management and its Disruption in Analytics

Big Data Management is the efficient handling, organization or use of large volumes of structured and unstructured data which belongs to the organization. It offers the company to know its customers better, develop new products and make significant financial decisions based on the investigation of large amounts of corporate data.

Talking about management it is quite a wide process which comprises of the policies, actions, and technology which are used for the compilation, storage, governance, organization, administration, and release of large repositories of data. Even it consists of data cleansing, migration, integration, and preparation for use in reporting and analytics.

It is commonly linked to the plan of Data Lifecycle Management (DLM). For finding out which information should be stored, where a policy-based approach is applied within an organization's IT environment, as well as at what moment the data can delete without any risk.

For management purpose people with many diverse job titles may be involved in a typical enterprise. They may consist of Chief Data Officer (CDO), Chief Information Officer (CIO), Data Managers, Database Administrators, Data Architects, Data Modelers, Data Scientists, Data Warehouse Managers, Data Warehouse Analysts, Business analysts, Developers and others.

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Big Data Management Processes

There are various processes that follow and are explained below for the further clarification.

• Through a central interface/dashboard, monitoring and it makes sure the ease of use of all big data resources.
• Performing Database maintenance for big results.
• Executing and monitoring big data analytics, big data reporting, and other similar solutions.
• Making sure that the capable design and functioning of Data Life-Cycle processes that deal out the highest quality results.
• To make sure the security of Big Data repositories and run access.
• Techniques such as data virtualization are used to diminish the volume of data and advance big data operations with sooner admission and fewer issues.
• Data virtualization techniques to be applied so that a single data set can be used by manifold applications/users at the same time.
• To make sure that all the data captured and stored from all possessions as desired.

Big Data Management Benefits:

Having data management for organizations is crucial as there are certain benefits to it. They are:-

1. Increase in revenue:

In Experian study, 61 percent of those surveyed said their data management efforts were a great aid for the organization in increasing there revenue. In addition, 82 percent said particularly that data quality solutions actually help in increasing revenues.

2. Customer service is improved:

Improved customer service was the second-most-common benefit is in the same study which is cited by 56 percent of respondents. And in the second kind of survey that is IDG Survey, the number one business objective of big data initiatives was customer service with 55 percent of respondents selecting it as a top goal.

3. Enhanced marketing:

Though it is not a usual benefit as sales and customer service, marketing also sees a rise from data management. Experian found that 39 percent of respondents said data management enhanced their marketing efforts, and an enormous 85 percent said they saw "improvements in sensible and modified customer communications" as a result of data quality schemes.

4. Efficiency is increased:

According to the Experian survey, 57 percent of those surveyed said keeping high-quality contact data assisted them to amplify efficiency. And the 2017 MIT Sloan Management Review report "Analytics as a Source of Business Innovation", which was sponsored by SAS, found that some consumers expected millions of dollars per year incompetence gains as a consequence of big data projects.

5. Cost savings:

A total of 38 percent of those who took part in the Experian study experienced cost saving. Correspondingly, in the NewVantage survey, 49.2 percent of those reviewed said their big data efforts had assisted them to decrease expenses.

6. New applications are enabled:

Anecdotally, organizations have stated when the firms have more confidence in their data it rises their innovation and motivates them to make new applications. In the NewVantage survey, 64.5 percent of executives surveyed said they were looking too big data to create new avenues for innovation, and 44.3 percent had achieved some success in this area.

7. Improved accuracy of Analytics:

The most important advantage of data management practices is that it raises the correctness and reliability of big data analytics. Good data coming into the analytics solution makes the organization appropriate for quality business insights coming out of the explanation.

8. Competitive Advantage:

According to the report of MIT, 57 percent of organizations using analytics reported a reasonable advantage. Quality big data management exercise allows that analytics, which in turn permit the companies to break their peers.

Best Practices

How can the Data management challenges be overcome by the organization and maximize the advantage from their efforts?  Several best practices are recommended by the experts. They are:-

• From all the departments in data management efforts all team members are arranged- Writing strategy is involved in data management which helps in making policies and transforming the organizational culture which is not just empowering in technology. In order to achieve success in these efforts, it helps to have as many of the stakeholders concerned in the process as probable. Members of the IT team, as well as participants from the business side and, of course, an executive sponsor, are excluded in it.
• For Data Lifecycle Management written strategy and policies are created. Having a written policy makes it much more probable that the policy will be applied all through the organization. In addition, many organizations should have their Data Lifecycle Management practices in writing for observance purposes.
• Identify and protect sensitive data. In the everyday news cyber attacks and data breaches organizations are more aware than ever of the need to defend business and client information. Data management teams need to ensure that all the sensitive data in their systems is sufficiently secured and that data security teams are keeping up with the latest suspicious strategies and techniques.

• An audit trail is deployed to strong identity and access management controls that.A major part of any data security plan is to ensure that only authorized personnel can see or interrelate with sensitive data, as well as maintaining track of who has seen or used the data and when. Again, these controls can also be significant for observance purposes.
• Investment is done in training for employees. Big data experts are costly and in short supply, it makes sense to maintain big data talent from within.serving current staff obtain big data skills can be a win-win for the company and the employee.
• Enable data sharing across your organization.  "Companies that share data internally get more value from their analytics. And the companies that are the most pioneering with analytics are more likely to split data further than their company boundaries." according to the MIT report.
• Consider appointing a chief data officer (CDO). This exclusive role is becoming all the time more common in large enterprises. The NewVantage survey found that 55.9 percent of executives surveyed said that their organization had a CDO. When asked about what the CDO should do, 48.3 said he or she should create innovation and a data culture, while 41.4 percent said the CDO should handle data as an enterprise asset. Less than 4 percent said the role was needless.

Big Data Management Services

When we talk about technology, organizations have a number of diverse types of data management solutions to choose from. Vendors offer a range of standalone or multi-featured data management tools, and many organizations use multiple tools. Some of the most frequent types of big data management capabilities consist of the following:

• Data cleansing:  Finding and fixing mistakes in data sets is covered in Data cleaning.
• Data integration: Merging data from two or more sources
• Data migration: Affecting data from one environment to another, such as moving data from in-house data centers to the cloud
• Data preparation: Making the data ready to be used in analytics or other applications
• Data enrichment: Getting better the quality of data by adding new data sets, correcting small errors or extrapolating new information from raw data
• Data analytics: Analyzing data with a diversity of algorithms in order to increase insights
• Data quality: Making sure data is precise and dependable
• Master data management (MDM): Linking significant enterprise data to one master set that serves as the single source of reality for the organization
• Data governance: Making certain the accessibility, usability, integrity, and accuracy of data comes under Data governance.
• Extract transform load (ETL): Moving data from an alive repository into a database or Data warehouse

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