Data Management in Big Technologies and Main Techniques

Data management is essential to the operations and functioning of businesses. But the data itself does not say anything, which is why a good management model is necessary. 

Therefore, organizations must establish a strategy to ensure that information is accurate, available and accessible. This way can make better business decisions, reduce costs, and increase revenues. Otherwise, if they are not managed effectively, the company may be limited in its analysis or get erroneous results.

Data management: main techniques

Data management is obtaining, storing, organizing, using and maintaining the data that a company collects. The objective is that the information obtained is available, safe, shareable and reliable in the long term. Thus, it seeks to promote decision-making and strategic planning in the company and ultimately become a Data Driven Company.

Database management systems

The most common type is the relational database management system. This system organizes data in tables that can be linked internally based on shared data. This allows users to retrieve tables with a single query quickly. It is a rigid data model that is best suited, in this case, for structured transaction data. 

The programming language most used in this type of database is the structured query language (Structured Query Language or SQL). SQL is the most common language in most companies and allows you to manipulate and download data while also doing advanced calculations.

But other options have also appeared, such as NoSQL databases. They do not have as rigid requirements on data models and can store unstructured or semi-structured data. There are four main types of NoSQL systems: document databases, key-value databases, vast column stores, and graph databases.

Big Data Management

NoSQL databases allow you to store and manage very different types of data volumes. That is why they are frequently used in Big Data implementations. Big data environments are typically built on open-source technologies (such as Hadoop) and are increasingly being deployed in the cloud. This is so because the cloud allows for reduced costs, greater agility and scalability for data storage and processing. Its versatility is also ideal for responding to the specific needs of companies.

Data warehouses and data lakes

Data warehouses include data from an organization’s business systems. Storage is the most traditional method, collecting structured data from different operating systems ready to be analyzed. Two of the most common uses for data warehouses are business reports and BI queries. Both allow you to analyze sales, manage inventory or extract key performance indicators.

On the other hand, there are data lakes, which store big data pools for predictive modelling, machine learning, and other forms of advanced analytics. A data lake can be implemented on NoSQL databases and combined with other platforms.

Data integration

Data integration is a process that combines data from different sources to get a unified and more valuable view of it. In this way, companies can make better decisions and do so more agilely.

ETL is the most widely used data integration technique: extract, transform and load. It is a technique that extracts the data, converts it to a new format and loads it already integrated into a data warehouse or other target system. In the case of big data systems and data lakes, the usual method is a variation of ETL, which is an extract, load and transform (ELT).

Data governance, data quality and MDM

Data governance refers to the ability of a company to ensure the quality of data throughout its life cycle. It encompasses the policies and procedures that are put in place to ensure that data is accurate and that it is handled correctly. This includes infrastructure, technology, data processes and policies, and the detection of the people who are responsible for managing them. The ultimate goal of data governance is to improve data quality. Data quality techniques include data profiling, cleaning and sanitizing, or validation.

Related to governance and data quality is also master data management or MDM. Because these programs are much more complex, they have yet to be adopted as widely and have been limited to large companies. MDM creates a central master data record for selected data domains, enabling business reporting and analytics.

Data modelling

Data modelling creates a visual representation or schema that defines data collection and management systems. This will allow data analysts to create a unified view of an organization’s data. The model shows, in this way, the data that a company collects, the relationship between the different data sets and the methods with which they will be stored and analyzed. The most common techniques for modelling data are entity relationship diagrams and data and schema mappings.

Good data management or data management can be essential for a company to achieve competitive advantages. Thanks to it, you can improve the efficiency of operations and, above all, it will help you make better decisions. 

Companies that manage their data well are more agile, know how to detect market trends and can take advantage of new opportunities. But not only this, but they will also avoid data privacy issues, security breaches or regulatory compliance issues that could damage your reputation.

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