Explaining some important data management concepts and terms

By Mark Benson, Chief Technology Officer, Logicalis UK&I

Recent Logicalis UK&I eBooks and articles have focused on the critical role of data in digital business, from unstructured data and the role of Information Lifecycle Management, to the importance of robust, business-driven data storage strategy.

In this article, we explain some key data management concepts and terms, highlighting their importance in delivering an effective data management strategy and meeting the challenges and opportunities of the increasingly data-led digital business landscape.      

Data management

Data management encompasses all the activities required to establish and maintain control over data assets. This control is an essential foundation for maximising the value of data, optimising data storage costs, and minimising risks.

It has become a critical function as data has evolved into a key business asset with significant value, risk and cost to the busines. Analytics and AI are helping businesses to realise the inherent value of data; growing regulation and cybercrime threats are increasing the business risk from data, and there is a constant drive to optimise the cost of storing the rapidly growing data ocean. This is explored in more detail in another Logicalis UK&I blog.

Data management starts with setting data strategy in line with business strategy, and moves through data governance – establishing structures, policies and responsibilities for data – to implementation and operation of the processes, metadata, tools and technologies required to manage data effectively as an asset.

Metadata  

Metadata is simply data about data. Any data item has associated metadata such as a creation date, owner, retention period, definition, status (active, complete, archived, etc).

Comprehensive and accurate metadata is the essential currency of data management, enabling policies and rules to be enacted effectively and consistently.

Information Lifecycle Management (ILM)

Information Lifecycle Management (ILM) is a key component of data management. It is the activity of managing an information item throughout its lifecycle from creation or discovery, storage, retrieval, use, retirement from active use and removal.   

ILM covers classification, ownership, mastering, securing, storage and access control, archiving and deletion. 

As with a physical asset, if information is in the wrong place, wrongly classified, incorrectly stored or used, it generates cost and risk and reduces its value.

Structured and Unstructured Data

Data can exist in a structured form, for example in a database, formatted file or spreadsheet, where it is codified and formatted, or as unstructured data such as an image, audio or video file. 

The amount of unstructured data in existence has grown exponentially ever since it became possible to store voice, video and images digitally. This growth has been accelerated further by social media and the Internet.

Today, the vast majority of digital data is unstructured, representing both challenges and opportunities for data management – challenges for storage and management, and opportunities for extracting ever more value from data.   

Dark Data

Gartner defines dark data as “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).”

While estimates vary, it is reckoned that on average at least 50% of the data held by organisations is dark data.

Dark data represents a significant security and regulatory risk for organisations, and bringing it under control is a key priority for data management. 

What next?

Logicalis UK&I have helped many clients deliver success in managing and maximising value from data.

Download our eBook to find out more about the critical role of data management in digital business, or visit www.ie.logicalis.com to find out more.