Did you know that 2020 is expected to produce 40 zettabytes of data? A massive increase compared to the 1.2 zettabytes of data produced in 2010. Why is there such a high data volume expected? Between the internet, social media, smartphone usage and IoT devices, each person is expected to produce 1.7MB of data per second. While this is great news for organisations, it also presents an important question: How to make sense of the large volume of data generated?
Having worked with data analytics for over ten years, I believe the next challenge for organisations is not just about investing in the most sophisticated analytics platform on the market, but rather setting up the processes to make sense of data produced. Without proper data management, analytics has little value because it is impossible to generate appropriate insights unless data is properly managed. Hence, why data cataloguing is so important for organisations, both public and private.
Data cataloguing is a metadata management tool to help organisations find and manage big data. Big data comes in different formats, like databases, files and tables. The data is drawn from different resources, like human resources, finance, e-commerce systems, ERP and social media feeds. Given the vast volume of data stored in their databases, organisations need a system to keep their data in order.
A data catalogue provides this oversight by centralising metadata into a single location. Those who can access the catalogue get a full view of each piece of data, including useful information like profiles, comments, statistics and summaries. So when data analysts access the databases, they know of the different data sources, no matter their format or origin and can search through it easily.
The key feature of any data catalogue is flexibility. Unlike traditional databases, data cataloguing is constantly evolving and expanding to match the organisation’s needs. As data grows, the catalogue needs to grow with it through updates or installing new features. Besides flexibility, security is a crucial feature for each catalogue – security features include but are not limited to data encryption, log information and role-based security. It is also important to ensure that data comes from trustworthy sources via data lineage.
Conventional data catalogues contain features that make it easier to find and add data. These features include business glossaries for reference, collaboration features for commenting and sharing, auditing features for governance and metadata to make searches easier. However, more modern data catalogues even include machine learning capabilities that allow for a host of sophisticated features like automated cataloguing, pattern mapping and self-generating topic extraction.
Yes, data catalogues are absolutely necessary for organisations because they allow for more efficient in-depth analysis. More in-depth analysis can be found in the form of greater diversity in perspectives, data exploration and arranging data for the needs of more advanced analytics operations.
The first point to note is the ease of collaboration. Sharing features combined with query search capability opens up the analysis to professionals from both technical and non-technical backgrounds, allowing for different expertise and perspectives to be captured in the analytics process. This opens up data expertise to business analysts or anyone with professional expertise who don’t necessarily have a technical background.
Furthermore, data cataloguing makes data exploration easier than before. As an organisation grows, their dataset also expands, making it harder for data analysts to find data. For example, data analysts at Uber would spend on average 3 hours finding relevant information before actually analysing the data in question. However, on a data catalogue, data analysts only need to find the data in question by searching for it, like they would search for information on Google, making it easier to explore data and prepare for analysis.
Data cataloguing is a data management strategy that makes it easier to comprehend large data volumes. As each organisation grows, the volume and variety of big data they collect also grows. But by cataloguing the data, analysts have an easier time exploring data and drawing connections between variables. Organisations have the chance to democratise analytics by opening up the process to different people. Every organisation needs to consider data cataloguing because it sets the foundation for a more efficient, timely analysis of their data and maintains a consistent schedule even when data grows in size and scope.