AI and Data Management

Put simply, a data management strategy is an organization’s roadmap for using data to achieve its goals. This roadmap ensures that all the activities surrounding data management—from collection to collaboration—work together effectively and efficiently to be as useful as possible and easy to govern. With a data management strategy in place, your company can avoid some of these common data challenges:

• Incompatible, duplicate, or missing data from undocumented or inconsistently documented sources
• Siloed projects that use the same data, yet duplicate the efforts and costs associated with that data
• Data activities that consume time and resources but do not contribute to overall business objectives

A data management strategy will be the strong foundation needed for consistent project approaches, successful integration, and business growth.

• Make life easier
  • Well-organised data management increases your efficiency, and saves time and
    effort in the long run;
• Protect yourself and others
  • Reduce the risk of costly/embarrassing/damaging accidents – losing data, disclosing
    confidential data;
• Preserve the integrity of your research
  • Well-documented data demonstrate the authenticity of your research and the
    reliability of your findings;
• Plan ahead for sharin
  • Public sharing of data supporting research findings is integral to the practice of
    scholarly communication, and must be anticipated and planned for from the outse
  • Data that are preserved and accessible in the long-term can be re-used to your
    benefit and others’.

The term “big data” began appearing in dictionaries during the past decade. But the concept itself has been around since at least WWII. More recently, wireless connectivity, internet 2.0, and other technologies have made the management and analysis of massive data sets a reality for all of us.

Big data refers to data sets that are too large and complex for traditional data processing and data management applications. Big data became more popular with the advent of mobile technology and the Internet of Things, because people were producing more and more data with their devices. Consider the data generated by geolocation services, web browser histories, social media activity, or even fitness apps.

Among the granular details mentioned above, a solid data storage approach is central to good data management. It begins by determining if your storage needs best suit a data warehouse or a data lake (or both), and whether the company’s data belongs on-premises or in the cloud.

Then outline a consistent, and consistently enforced, agreement for naming files, folders, directories, users, and more. This is a foundational piece of data management, as these parameters will determine how to store all future data, and inconsistencies will result in errors and incomplete intelligence.

Data visualisation is the graphical representation of information and data. By using visual elements like charts, graphs and maps, data visualisation tools provide an accessible way to see and understand trends, outliers and patterns in data.

In the world of big data, data visualisation tools and technologies are essential for analysing massive amounts of information and making data-driven decisions.