big data management

Big data management is a crucial area that is essential when enterprise data stores are growing exponentially. This is a challenging area because managing big data is increasingly a demanding area for organizations. Big data management essentially means putting the right technologies, policies, and people in place to ensure accuracy, quality, and security for those massive sets of data from the data point of view.

Does that mean only management is the key concern for big data management? Not to mention, accurate data is important for organizations; otherwise, they can compromise opportunities, which can decrease the brand value and customer satisfaction. Besides, lack of data quality enhances the chances of non-compliance. Here comes the importance of big data management. With proper data policies and the help of various tools storing such huge data sets, rather big data management takes place.

Big Data Management – the term

Big data generally refers to data characterized by the 3V’s – volume, velocity, and variety. When a data set meets certain criteria on these three parameters, it is called big data. Big data management encompasses procedures, policies, and technology for the storage, collection, organization, governance, administration of huge repositories of big data. Big data management involves several stages like data cleaning, migration, integration, and big data preparation in repositories for reporting and analytics.

Data lifecycle management is a concept that works closely with Big data management. This policy-based approach determines what information must be considered for storing and which data should be deleted safely.

Big Data Management Cycle

The Data Lifecycle. Source: DataONE

Areas needs to be focused for Big data management

Leadership

Only good quality of Big data can’t assure a company’s success; instead, proper leadership is also a crucial part of Big data management. It needs clear goals and a proper vision. Here the business leaders must understand the market, think creatively, and propose novel offerings. At the same time, they need to work hard to realize it. Besides, dealing effectively with customers, stockholders, employees, and other stakeholders.

Talent management

Proper talent for Big data management is important as big data management consists of many activities from data cleansing to data visualization. It is no doubt important to understand the statistics while you are dealing with big data analytics, however, business knowledge is equally important to help leaders to reformulate the challenges associated with Big data management.

Technology

Modern Big data management deals with many tools to handle the 3V’s, i.e., volume, velocity, and variety of big data. These tools are not very expensive, and many of them are open source. For example, Hadoop is the most commonly used framework that combines open-source software with commodity hardware. Besides, these frameworks provide tools to analyze data.

However, these technologies require a skill set that is new to most IT departments. Hence, technology knowledge must be a part of a big data strategy.

Company culture

Before strategizing Big data management, a company must understand that it has to be truly data-driven to propose better decisions. Leaders must embrace this fact. It is domain expertise with data science knowledge that can pull away from the company from its rivals.

Challenges associated with Big Data Management

Big data management associates many challenges with it. Some of the common issues include:

1. Data silos: An organization may consist of many departments. When a repository of fixed data remains under one department’s control, whereas it remains isolated from other departments, it is called data silos. However, it could lead to issues like duplicate information sharing. At the same time, storing the same information in multiple places eats up storage spaces.

2. Growing data storage: Big data keeps growing. It is one of the challenges apart from the size of big data when it comes to the question of managing it. For example, a small company storing and managing customer records is easier than a large retailer. It becomes a performance issue while crawling through those petabytes of data or even moving it from the data store for analytics purposes.

3. Data complexity: Data silos or growing data stores are not the only problems here. The format of today’s data is complex. It could be structured or unstructured and can be generated from simple text documents to complex sensor data. These disparate data can be of any format. So, in an enterprise where many applications are running, it needs to read and write to many databases. In this scenario, the cataloging of the data becomes a tedious job.

4. Maintaining data quality:  The above-mentioned challenges make it difficult for organizations to ensuring data quality. Here one problem is the synchronization issue due to data silos. This makes it difficult to define which part of the data is correct and which one is duplicate. This overall hamper the data quality. There is a big chance of human error here. This can raise the typo issue.

5. Inadequate staffing: Big data management also needs expert staffing. Hence, this is another complicated area due to the lack of experienced staff in big data management. Even if appropriate staffing is possible, that demands high salaries, which are a costly affair for enterprises.

7. Establishing proper data-friendly culture: In a data-driven organization, moving from a manual decision culture prioritizes a data-driven culture, which is a huge transition. However, most organizations fail to achieve this goal as changing the workforce mindset is a challenging task.

Related post – Why Data Strategy is more important than a Cloud Strategy

Benefits of Big Data Management

Although there are many challenges associated with big data management, its successful implementation reports several benefits. Some of them are as follows:

Increased revenue: Proper management of data helps organizations to increase revenue. Besides, it enhances the data quality solution, which also contributes to gain revenue.

Improved customer service: The immediate most common benefit of big data management is better customer service. Another survey shows customer service is one of the primary objectives of big data initiatives.

Enhanced marketing: Since proper big data management enhances data quality, marketing also sees a boost from big data management. These improvements happen due to timely and personalized customer communications.

Cost savings: As efficiency increases, it helps in cost savings. Big data management efforts help to decrease expenses.

More accurate analytics: Big data management practice can increase the reliability and accuracy of big data analytics. Well-formed data coming into the analytics solution set the organization ready for quality business insights due to the solution.

Competitive advantage: Big data management brings a competitive advantage as proper big data management practices enable analytics, allowing the companies to edge over their peers.

Big Data Management Best Practices

Like all other practices, Big data management follows some best practices to overcome the challenges and enhances the benefit from the efforts. Several best practices are in place. Some of them are as follows:

Encourage team members from all the departments to involve in big data management efforts. It involves several activities like writing strategy, transforming the organizational culture, and creating policies. This is merely investing in technology. This is a huge effort that needs as many of the stakeholders to be involved in the process.

Need a well-planned policy and strategy for data lifecycle management. As mentioned in the beginning, big data management is often associated with data life cycle management, so having a written policy is essential. This policy must be enforced throughout the organization. Besides, in many organizations, it acts as a part of a compliance strategy.

Identifying and protecting sensitive data. When dealing with corporate and customer data, it is necessary to protect those data from cyberattacks and data breaches. So, all the systems’ sensitive data must be properly secured with proper defensive techniques and strategies. The data security teams must ensure this.

Identity and access management controls in place. Only authorized personnel have access to sensitive data. Every time the data has accessed the person and the time must be marked as a part of the compliance strategy.

Invest in training for employees. A big data talent pool must be created as there is a shortage of big data experts in the market. Organizations can benefit if they train their existing members, who will undoubtedly be a win-win situation.

Source: Experian Data Quality 2017 Global Data Management Benchmark Report

Big Data Management Services

From the technology point of view, big data solutions come in many flavors. There are a variety of standalone and multi-featured tools are available in the market by the vendors. Some of the most common types of big data management solutions include the following:

Data cleansing: To find and fix errors in data sets

Data migration: To move data from one environment to another

Data integration: To combine data from multiple sources

Data preparation: To preparedata for analytics

Data enrichment: Toimprove the data quality

Data analytics: To analyze data with algorithms to gain insights

Data quality: To make sure data is accurate and reliable

Master data management (MDM)

Data governance

Extract transform load (ETL)

Big data management tools

Here are some of the best tools that are used in different stages of Big data management –

IBM Infosphere Information Server

SAS Data Management

PowerCenter Informatica

Pentaho Business Analytics

Tableau

D3.js

Highcharts

Microsoft Power BI

QlikView

Final verdict

Big data emergence has brought lots of changes in how big organizations opt for multiple software for Big data management. Not all organizations need the same software for Big data management. It should match their requirement to access data in real-time and quick analysis to get valuable data insights. Most of the organizations are continuously scaling up their tool suite for better handling big data.

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