All you need to know about Big data management
Big Data management is the organization, management, and governance of a large volume of both structured and unstructured data. Big data management's role is to ensure a high level of data quality and accessibility to business intelligence and big data analysis applications. Companies, administrations, and all other organizations are implementing Big Data management strategies to help them cope with the growing amount of data. Data that is counted in terabytes, even petabytes, and is stored in a multitude of formats. Efficient big data management allows companies to identify and locate key information in a mass of unstructured and semi-structured data from different sources, such as call recording systems, system logs, or still social networks. Most big data environments extend beyond relational databases and traditional data warehouses. They integrate technologies capable of processing and storing non-transactional data formats.
This trend around big data collection and analysis has given birth to new platforms that combine classic data warehouse technology with big data systems in a logical architecture. For example, the system decides what data should be retained for compliance reasons, what other data should be deleted, and what should be retained and analyzed to improve business processes and provide a competitive advantage to the business. These processes require careful data classification so that ultimately small data sets can be analyzed quickly and productively. Read on!
What is Big Data?
Big Data is an expression used to designate a large volume of structured or unstructured data. This volume is indeed so large that it is difficult, if not impossible, to process them using traditional tools such as databases or software. In most business scenarios, big data represents such a large volume of data that it exceeds operational processing capacity. Big Data is, therefore, a revolution in the field of digital information processing. The use of data analytics by businesses is increasing every year. The latter are concentrating on the moment, mainly on their customer data. Big Data is, therefore, flourishing in B2C (Business to Consumer) applications. Depending on the environments, it is possible to divide the analysis of Big Data into three areas:
- Prescriptive analysis: this is the field of business analysis (Business Analytics) devoted to finding the best action plan for a given situation.
- The predictive analysis: encompasses various techniques derived from statistics of knowledge extraction from data and from a game theory that analyzes present and past facts to make predictive hypotheses about future events.
- Descriptive analysis: it is used to describe the basic characteristics of the data of a study. This is to provide simple summaries on samples and measurements. Along with the simple graphical analysis, descriptive analysis forms the basis of almost all quantitative data analysis.
Big Data offers an immense potential, and it has become indispensable today. Data volumes are exploding day by day. Whether it's traditional point-of-sale systems or e-commerce websites, the amount of data being collected is exponential. It is the same on the internet where social networks increase their database in real-time and yet has only limited analytical capacities.
Big Data research involves analyzing a large amount of varied data, which allows more informed, predictive, and holistic decisions to be made. Big Data analysis, therefore, allows companies in the field of business intelligence to offer better experiences to their customers.
How to do Big Data management
Plan for the long term by thinking in the short term
You are not alone in worrying about the evolution of technologies related to Big Data. Everything is changing so quickly that it is impossible to know the tools, platforms, and methodologies that will be at work next year. Relax. You can adapt perfectly to this rapid evolution. Each year, suppliers are increasingly mastering Big Data. Online relational and transactional processing (OLTP) systems are becoming more efficient and smarter, whether used on-premises or in the cloud. The latest technical advances facilitate the interfacing between Hadoop and data warehouses. And new products are constantly appearing on the market, meeting your needs more and more precisely.
No need to worry, therefore. Stay tuned for these new products' possibilities if they are beneficial enough to justify their integration into your current environment. Maintain a business intelligence platform to connect to many different formats. You will then be prepared for whatever the market can produce.
Detect false questions
Does your business need Hadoop or a data warehouse? This is a trick question. Not only do Hadoop and data warehouses work seamlessly in parallel, businesses even take advantage of the collaborative capabilities of these systems. The data warehouse is best suited for managing your important, structured data and storing it where your BI tools and dashboards can easily find it. However, this solution will be less efficient and slower for analytical processing and other types of transformation. Instead, use Hadoop. Additionally, although Hadoop is poorly suited to interactive queries and data management, it is good at integrating raw, unstructured, and complex data.
Together, these systems work in symbiosis. Take, for example, the data your leaders rely on when they list their needs for the year ahead. This dataset is probably gigantic, and you don't have enough time to model, restructure, or prepare it somehow before you integrate it into your data warehouse. When they're done, sometimes after just a week, leaders will get rid of them. At this precise moment, Hadoop intervenes to store and refine this data before sending a sample of it to the data warehouse.
According to experts, Big Data is no substitute for storage in a data warehouse. “It's also not something to be managed separately. These data are part of the new IT environment. "
Don't fall for the trap; you don't have to choose between Hadoop and the data warehouse. You can and should use these two resources.
Integrate Big Data into traditional systems
To be truly productive and efficient, it is advisable not to use Big Data in isolation, but to integrate it into existing systems. Indeed, by combining big data within your organization with traditional sources and operational systems, you can obtain a detailed overview of all the important data about your customers, products, suppliers, and partners. Based on this, you can, for example, segment customers to use them to tailor marketing efforts. It also gives you a better view of supplier reliability and accuracy.
Collection of data
Whether produced or collected by your company, data enters your information system at a specific time. Sales figures, customer database, consumer reviews on e-commerce sites, social media, marketing lists, and electronic mail archives are massive and heterogeneous. And often, you need to buy or retrieve external data to enrich your internal analyzes. If you need to source new data at large, your need for new infrastructure could increase dramatically. But all of this naturally depends on what kind of data you need.
The data can come from sensors positioned in devices, machines, buildings, vehicles, product packaging. Globally, the sensors can be positioned anywhere you need to collect information. Data also comes from applications. It may, for example, be an application for ordering products intended for customers. Finally, data can come from other sources, such as surveillance camera circuits.
With a little technical knowledge, you can set up these systems yourself, but you can also use the service providers to set up these systems and collect the data for you. Collecting external data sources, such as social media, doesn't require major changes to your infrastructure, since you have access to data that someone else is collecting and managing. If you've got a computer and an Internet connection, you're pretty much equipped to get started.
As the volume of data generated and stored by businesses has exploded, sophisticated yet accessible systems and tools have been developed to help you in this task. The main storage options are a traditional data warehouse, a data lake, a distributed or cloud storage system, and of course, your corporate server or your computer's hard drive. Conventional (mechanical) hard disks are now available with very high capacities at low cost. If you work in a small business, this may be all you need. But when you start to process a large amount of data for storage and analysis, or if that data is destined to become a key part of your strategy, you need something else. A Hadoop-like, or cloud-based, the distributed system might be more suitable. In fact, cloud storage is an attractive option for most businesses. It's flexible, you don't need physical systems, and it reduces your need for security tools to protect data yourself. It's also much cheaper than going through the investment in dedicated systems and data warehouses.
Author: Vicki Lezama