Have you ever considered the value of data? Let me ask you a question: Where does data typically start? I believe the answer would be at collection. Data usually begins somewhere in a hard drive, warehouse, NAS (network-attached storage), server or some other system that can store data. When data is collected and stored, it can be processed and analyzed, which would identify patterns and trends.
There are a few different data disciplines to consider in helping grow your data science and big data knowledge. These disciplines can be helpful for both professionals and amateurs that are out there trying to learn as much as they can about data science and big data.
Here are Data Disciplines for Data Scientists and Big Data Management:
Data Operations (DevOps)
Data operations, or DevOps, is an emerging field in tech that focuses on the management and development of data science projects. It’s a combination of software engineering and data science, with an emphasis on automation, security and quality control.
As an industry matures, it’s important to have a system in place to ensure that new technologies are being developed as safely and efficiently as possible. Data operations specialists use their knowledge of data science to create policies and procedures for software engineers who work with data scientists. They also help automate processes so developers can focus on what they do best: building great products.
Data Inventory Management
Data Inventory Management is a must-have for any data scientist. This is the process of collecting and maintaining a record of all data within an organization. This includes both structured and unstructured data, as well as metadata associated with these datasets.
Data Inventory Management is crucial for companies that want to accurately track their information assets, so they can make strategic decisions about the most effective use of their data. It also helps ensure that all important data is stored in one place where it can be easily accessed and analyzed.
Tracking Data Lineage
When dealing with data, it is important to track the lineage of your datasets. This ensures that you know where the data came from and can verify its accuracy. If a dataset is not tracked well, it could be corrupt or outdated.
Data lineage refers to a system for tracking how data is used and modified over time. It helps ensure that the data you are working with is accurate and reliable by showing you who created it, when they created it, and how they modified it.
Example is Metadata— which refers to any information about a dataset that isn’t part of the actual content of the dataset itself (like author name or date created). Metadata helps identify who created a dataset and when it was created or modified. You can use metadata to keep track of who changed what in a dataset over time as well as when changes were made so that you know if there’s been an error introduced into your dataset at some point in time during its development process.
Here is an actual example of data lineage: if you’re analyzing data generated by a survey, you can look up the survey report to see what questions were asked and how respondents were selected. The more detail you have about where your data came from, the more confidence you can have in your results.
Data Security
Data security is a critical part of data management, especially for data scientists and big data management. In the wrong hands, data can be used to manipulate public opinion and sway elections. Data security is also important because it keeps personal information safe from hackers.
Data security can mean different things depending on what type of data you’re working with. For example, if you’re working with sensitive information that could be used against someone if it gets into the wrong hands (like credit card numbers), then you want to make sure that your database is encrypted so that only authorized people can access it.
If you’re working with large amounts of unstructured or machine-readable data, then you need to make sure that this information is stored securely so it doesn’t fall into the wrong hands when an employee leaves your company or if something happens to their computer or phone while they’re at work.
You also need to make sure that unstructured data isn’t accessible by anyone outside your company— even if they have access rights within your organization’s network— because this could lead to breaches in privacy laws due to negligence on behalf of management/employees who aren’t aware of how important it is protect confidential information.
Data Governance
Data governance is a set of policies and processes that guide how data is managed across an organization. These policies and processes ensure that the data is accessible, reliable, and correct. The goal of data governance is to create a repeatable process for managing data across all departments and systems so that it can be easily accessed by anyone who needs it.
Data governance usually involves five different stages: planning, design, implementation, maintenance, and monitoring. The first stage involves creating a plan for how the data will be governed; this plan should include details about who will be involved in governing the data as well as what types of policies need to be enforced or developed. Once this stage is complete, you move on to designing your system so that it can support your plans for governing your data. Then comes implementation— this involves putting your plan into action by setting up processes that allow people within your organization access to the information they need while keeping them from accessing any other information they shouldn’t have access to yet (or ever). Maintenance involves making sure everything stays up-to-date with new laws or regulations regarding privacy laws so nothing gets overlooked due to lack of knowledge about these changes affecting how businesses manage their data. The final step is to evaluate whether your system is working as it should, and if not, making changes to fix any issues that arise so you can continue with the business of governing your data.
Data Reporting
Data reporting is a broad term that encompasses several different types of analysis. Data reporting can be used to answer questions like, “What happened?” or “What should we expect to happen next?”
Companies that have implemented data reporting systems often use them to generate reports for internal use, but they can also be used to provide data-driven insights to customers and other stakeholders. When it comes to Big Data, these insights are usually generated by analyzing large amounts of data and then using statistical methods to predict events that may occur in the future.
Data reporting typically involves creating visualizations of data so that its meaning can be easily understood by non-technical users. A good video editor and some animations can help. This allows companies to share information with their stakeholders without having to rely on technical jargon or complicated statistical models in order to explain what’s happening with their business processes.
Performance Monitoring and Optimization
Performance monitoring and optimization is a discipline that focuses on measuring and improving the performance of a system. It involves detecting, diagnosing, and resolving bottlenecks in the system or process. Data scientists who specialize in this area will be able to develop statistical models to find out how well a system is performing and what factors are affecting its performance. They will also be able to use those models to identify bottlenecks so that they can be removed or reduced.
Performance monitoring can be done in two ways: manual and automated. Manual performance monitoring is when someone goes through the data manually and makes adjustments as necessary. Automated performance monitoring uses an algorithm to make decisions about how well the system is working, based on its own analysis. Monitoring can also be done at higher levels by measuring application-level metrics such as response time or throughput.
Performance monitoring can be used in many different ways. A common approach is to monitor system resources like CPU and memory usage or I/O bandwidth utilization. Actual example is monitoring the size of your disk space which gives your system good performance. The optimization process is by compressing files and videos with ZIP archive and with a video compressor.
Conclusion
With data being such a vital and important component of every industry, from retail to banking, it has never been more important to institute some kind of system that facilitates a better understanding of all that data. The ever-increasing amount of information collected and analyzed makes it tricky for those in charge of storing and handling the data to be able to understand how it is being consumed. The disciplines outlined above are some ways in which the big data management process can be enhanced, both at this point in time, and as technology becomes more advanced.
