The Different Types of Data Management

data management

The Different Types of Data Management

Exciting and expanding in its scope and function, data management involves the collection, storage and usage of data. Businesses aim to execute these processes in the most secure, efficient and cost-effective manner possible. As a practice, data management comprises a range of other disciplines which relate to data as a valuable resource. As information and technology become more deeply ingrained in the way businesses operate today, an effective data management strategy is quickly becoming more important than ever. Some of the most important different types of data management include:

Database Management

Database management allows users to organise, store, retrieve and manipulate information from their IT infrastructure. A database is any collection of information which has been stored in a manner which means it can be easily accessed, managed and updated. One of the more obvious purposes that comes to mind in business is the customer database. Both B2B and B2C businesses benefit greatly from the possession and usage of a customer/client database. Database management isn’t just operated with a narrow scope of objectives in mind, but instead achieves a number of crucial functions, including the facilitation of business performance, storage optimisation, security, privacy and efficiency.

Big Data Management

Big data management involves the processes and infrastructure used to collect, store, organise and administrate large repositories of data. It’s all in the name – big data consists of huge quantities of information. An obvious example of big data is that which is generated by social media platforms. Consider the masses of data processed every minute from a global social media platform. The quantity of information, in addition to its variety, speed and relevance make big data a coveted resource in business. But all of these characteristics which make big data so valuable also make it very challenging to manage. Organisations that undertake big data management need to physically house the infrastructure required to do so, implement processes to ensure data quality and establish a culture in which information continues to be valued and is leveraged productively. Effective management of big data can have wide-spread positive impacts throughout an organisation, including increasing customer service and enhancing marketing to increasing revenue and sustaining a competitive advantage.

Data Warehouses

A data warehouse is used to centralise and consolidate large amounts of information from a variety of different sources. As a primary function, data warehouses facilitate and support business intelligence activities. As its use continues, a data warehouse will inevitably become a large historical record of information. With an effective design, a data warehouse will display four defining characteristics:

  • Subject-orientated – possesses the capacity to analyse information on a specific topic.
  • Integrated – creates consistency between various data types from different sources.
  • Non-volatile – storage of data is stable, and files aren’t susceptible to change over time.
  • Time-variant – measures and identifies trends in how data changes over time.

Data Analytics

Built upon a variety of platforms, a key function of data management is data analytics. The overarching aim of data storage is to leverage it for the benefit of the business. Data analytics is the science of processing unrefined data in order to make conclusions about that information. The conclusions made from a given data set might help a company decide how much product they need to stock, what age demographic they need to market their latest campaign to, or anything in between. Four key kinds of data analytics include:

  • Descriptive – describes a trend or what is happening over a given time span.
  • Diagnostic – explores the why behind a trend or happening.
  • Predictive – suggests what can be expected or is likely to happen in the near future.
  • Prescriptive – suggests a course of action to take advantage of predicted events.

The modern organisation inevitably needs to invest in their data management systems. And while an efficient and suitable system is vital, so is human administration and intervention. The ever-increasing role of data has firmly solidified its place in helping organisations to actively achieve better results for themselves and their customers.