The data description: what, exactly, is data management?

Organisations often make important decisions based on data they don’t fully comprehend. This can have a harmful impact on business intelligence, which is vital to getting ahead of the competition in a data-centric industry.

So, how can an organisation mitigate this issue? Through intelligent data management and protection. Organisations require a thorough data management plan to succeed in modern business, so is it time your organisation took its functionality to the next level by enlisting the services of a reputable data management organisation?

So, if you’ve been considering data management in Canberra, Sydney, Melbourne or beyond, here are the details on data management and what it means for your organisation.

What is data management?

Data management is the practice of creating and maintaining a framework for ingesting, storing, archiving and mining vital organisational data. Data management is imperative to allow connectivity between all aspects of the information lifecycle.

Data management works in conjunction with process management, ensuring that an organisation's moves and decisions are based upon the most updated, relevant data available. In today’s business world, organisations have to track changes and trends on-the-move, and data management helps teams make decisions that enhance business intelligence.

The 7 types of data management

There are a range of data management specialties that experts focus on, including:

1. Master data management: Master data management is the process of ensuring an organisation always has, and is making proper decisions on, a single version of current information. Ingesting data from all of an organisation’s sources and providing it as one reliable, constant source requires the proper tools, as does recultivating data for use in different systems.
   

2. Data warehousing: Information is key to building a successful business. But all this information requires efficient cloud or physical storage infrastructure so it can be easily accessed and analysed to provide proper business insights.
   

3. Data quality management: The data quality manager is responsible for analysing collected data for unseen issues like inconsistent versions, duplicate records and others.
   

4. Data security: One of the most vital elements of data management is its overall security. There are new practises that utilise security concerns at each stage of data exchange and application development, and security specialists are required to implement encryption management, protection against accidental movement or deletion as well as preventing unauthorised access.
   

5. Data stewardship: A data steward does not produce information management policies, instead they deploy and enforce them across an organisation. A data steward ensures that data collection and movement policies are working effectively, as well as ensuring that the correct practises are understood and enforced.
   

6. Data governance: Data governance produces the law for an organisation’s state of information. A data governance framework is essentially a constitution that clearly defines policies for an organisation’s intake, protection and flow of information. Data governors oversee their quality management professionals, security teams, network of stewards and other data management processes and people to create a governance policy that requires a master data management approach.
   

7. Big data management: Big data is the term used to describe the analysing, gathering and utilisation of huge amounts of digital information to improve organisational operations. In a broader sense, big data management involves storage, intake and integrity of the surge of raw data that other teams utilise to improve organisation operations and security or enhance business intelligence.

All organisations are unique and face different challenges. This means that a business might have to incorporate a number of these processes to manage their data and use it to effectively inform business intelligence. By using one or a number of the above data management processes, organisations can have greater access to their data as well as enhanced comprehension, something that benefits any organisation in the long run.

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