Data is processed to generate information, which can be later used for creating better business strategies and increasing the company’s competitive edge. Data mining and knowledge go hand in hand, providing insightful information to create applications that can make predictions, identify patterns, and, last but not least, facilitate decision-making. Working with massive structured and unstructured data sets can turn out to be complicated. Nonetheless, it’s important to treat the entire process as valuable work rather than treating it as a nightmare.
It’s obvious that you’ll want to use big data, but it’s not so obvious how you’re going to work with it. Knowing some techniques in advance can lighten the road. So, let’s have a close look at some of the best strategies to work with large data sets.
Preserve information: Keep your raw data raw
Raw data is better than cooked data because it’s accessible for further processing and analysis. There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. If it’s not done right away, then later. The raw data can be fed into a database or data warehouse. An analyst can examine the data using business intelligence tools to derive useful information.
To arrange your data and keep it raw, you need to:
Make sure the data pipeline is simple so you can easily move data from point A to point B.
Save a copy before editing to prevent changes to the original data.
Summarize and sample your data at query time.
Attention needs to be paid to the fact that it’s not always possible to archive or analyze all the data that’s being produced. Nonetheless, you must invest time and effort into extracting the best possible value from the data sets. Everyone has to manage raw data at one point or another; yet, not everybody stores it in a way that’s useful for further analysis or comparison to other data sets.
It’s much easier to work with graphs
As data sets become bigger, it becomes harder to visualize information. It’s recommended to use lots and lots of graphs. Draw a chart highlighting each endpoint in your data. If you’re working with thousands or tens of thousands of nodes, this can be very useful. You can finally understand what you’re looking at and what the data is saying. The graphs can either be single, grouped, or stacked. The format can be classified by size, but you can choose to organize data horizontally or vertically/by column.
Data visualization enables you to:
Make sense of the distributional characteristics of variables
Easily identify data entry issues
Choose suitable variables for data analysis
Assess the outcome of predictive models
Communicate the results to those interested
It doesn’t matter if you use graphs or charts, you need to get better at data visualization. Data visualization, empowered by the computer, is one of the most practical tools you have at your disposal. You’re familiar with the saying “A picture is worth a thousand words”. Just so you know, a picture isn’t a substitute for a thousand words.
Store and organize the data in a scalable way
Data storage is a key component of any successful organization. The way in which you store data impacts ease of access, use, not to mention security. Choosing the right data storage model for your requirements is paramount. There are countless implementations to choose from, including SQL and NoSQL databases. Speaking of which. A NoSQl database can use documents for the storage and retrieval of data. The central concept is the idea of a document. Documents encompass and encode data (or information) in a standard format. A document is susceptible to change.
The documents can be in PDF format. You won’t have any problems storing document files. You don’t necessarily need to download Abode Acrobat to manipulate PDF files. There are reliable alternatives such as PDFChef that make it possible to edit and protect PDF documents. getting back on topic, documents can encode data in various formats, such as Word, XML, JSON, and BSON. Data type description and the value for the concerned description can be found in the document. The structure of the documents that make up the database can be similar or present certain differences. It’s not necessary to alter the schema to add to the database.
Manage workflow data and remove unnecessarily complex processes
The workflow is basically a sequence of tasks that processes a set of data. It’s necessary to have a structured workflow to explore new opportunities. The good news is that you don’t have to do things manually. These days, you have software to help you handle the process. To put it simply, you can manage both documents and processes. You can identify redundant tasks, map out the workflow, automate the process, and discover areas for improvement. Even leading organizations can end up with unorganized documents, disconnected tasks, and so on.
The most important features any workflow management system should have are:
Integration with other cloud apps
WYSIWYG form designer
SLA status indicators
Notifications when and where you need them
The best part about data workflow management is that you can take a task and develop a custom solution to bring clarity to the entire team on what needs to be done and, most importantly, how.
We have one last thing that we’d like to add. It’s a good idea to record metadata. Standardizing metadata helps ensure that information assets continue to meet the desired needs for the long term. The metadata describes exactly how observations were collected, formatted, and organized. The specialized set of information preserves and provides access to electronic records. No matter what your strategy is, try to think about the future. It might be necessary one day to integrate your data with that of other departments. Metadata makes the task a lot easier. It improves the data quality and system effectiveness.