Do data scientists need to consider metadata management, and if so, why?
Metadata is the set of data that describes and gives information about other data. It is the central nervous system of any data platform, forming a foundation for your dataset that will allow it to be organized and well-managed. Metadata can be considered almost an operating system for your data platform. Numerous functions that we take for granted, such as data description, data browsing, data transfer, tagging and online cataloguing, would not be achievable without organized metadata. While metadata is generally now associated with the digital sphere, it has always been essential; dictionaries, card catalogues, and taxonomies are all examples of metadata.
Data-driven companies require a metadata management system. Metadata management should enable businesses and IT users to make use of business analytics and data governance, thereby maximising the value of their information assets. A unified and comprehensive metadata system is invaluable to both the business and the individual user.
Metadata management involves managing the data you have about your other “content” data in order to make this “content” data easier to analyse and navigate. One of the earliest and most influential examples of a metadata management system is the Dewey Decimal Classification system, which was developed in 1876 for libraries and is still used in most countries around the world.
if the data is messy, the metadata will be, too. Metadata provides a way of categorizing and organizing to make finding and using data easier. This makes it harder to adapt to constantly changing client requirements and evolving business needs, and could end up costing money in the long run.
Harnessing the power of your data is a great way to drive future decisions for your business and its clients. If your data is organized and categorized in a productive and efficient way, it will be much easier for those in your business to make better and more informed decisions, and make them faster.
Having a metadata management system in place can also help to unify unstructured or semi-structured data that is scattered across different platforms. Data that is spread across different internal operational systems, networks and devices can become even more difficult to manage and analyse, and this is something that can be resolved with a well-managed classification system.
Metadata is something that is often ignored by data-driven businesses. This is often because it isn’t seen as a priority of data management. However, recent developments in Big Data initiatives have made managing your metadata more important than before. Certain popular platforms, such as Hadoop, have now become ‘schema-less’, which means that they do not come with descriptions of their data. If the user of these platforms does not learn how to identify the data in these platforms with accurate and descriptive metadata, managing this data will become a challenge and lead to issues in the future.
Making Sense of Metadata Management
While metadata management can be a challenge, it can be incrementally implemented by working out from smaller chunks to get to the enterprise. Alternatively, data scientists can proceed through an implementation process using a maturity model such as that offered by ASG. Ultimately, this process makes adoption easier for business team members and data scientists alike.
Developing metadata management can be a challenging and time-heavy undertaking. Every business needs to consider what challenges need to be overcome to ensure that their metadata is easily accessible and effectively used, while also being kept up to date so that new metadata can be incorporated effectively.
Data integrity needs to be as well-maintained within metadata as it is in “content” data. There still need to be enforceable rules to maintain integrity and usability, and to ensure that it is possible to detect and create relationships in the data. Most data repositories will collect the metadata, but it will only be categorised in a two-dimensional way, meaning that the user can identify the attributes but not the relationships around the data. This can cause confusion and disparity down the line if businesses fail to keep track of it, but imposing rules that are enforceable on every platform can be a challenge.
A major problem is the sheer magnitude of the information assets involved, and the number of disparate information platforms, often stretching across multiple business units, that data needs to be gathered from in order to become more unified. If data is scattered in this way, particularly if it has been left unorganized or only semi-organized for a long time, it can be a real challenge to pull it together. There should be a consistent, easy-to-use format that will work for every platform, from the most sophisticated to the most rudimentary.
Similarly, all of your data – metadata included – needs to be in compliance with data protection regulations. There are many ways in which appropriate use of your metadata can actually make it easier to manage GDPR compliance — for example, the ‘right to be forgotten’ rule is much easier to enforce when a company has metadata in place that will help them to determine where the data is located, so it can be deleted quicker. But it also presents potential difficulties — the incredible volume of data and metadata that needs to be kept track of and combed through can be overwhelming.
For example, ASG has a solution in place to help manage these challenges and get data scientists onboard with implementing a metadata management strategy. Recently named a Leader in the 2018 Gartner Magic Quadrant for Metadata Management Solutions, ASG’s solutions for metadata management have helped to take on these challenges. Gartner reported,
“metadata management initiatives deliver business benefits that include: improved compliance and corporate governance, better risk management, better shareability and reuse, and the ability to better assess the impact of change in the enterprise while both creating opportunities and guarding against threats.”
What can a good metadata management strategy do for data scientists?
To summarise, Data Scientists may want to look at more raw data, which is part of the DataOps cycle. It can depend on the user stories. Business people may well prefer summaries, but data scientists may well prefer to look at the raw data. Therefore, metadata management is something that is important, right from the start, for data scientists.
The advantages for data scientists are …
- A metadata management strategy ensures that data is interpreted properly. While titles and descriptions are a given, information such as the number of entries, number of attributes, and associated relationships are also important to have in order to understand and leverage results. It is also extremely helpful for data to be categorized effectively – metadata systems as simple as tags can help to establish relationships and give context. This makes the data easier for anyone to use and work with, but data scientists will specifically benefit from the amount of time they will save by not wading through unconsolidated data to find what they need.
- As mentioned above, the preferred format that this data comes in differs depending on the user. Businesses may prefer summaries, but data scientists are much more likely to want to see and work with the raw data. A good metadata management strategy can organize, categorize and unify the raw data, which is important if the raw data is going to be the primary type of data that you will be working on.
- Good metadata management can actually free up valuable time and resources for data scientists. Data preparation accounts for between 80% and 90% of any data scientists’ job, with 60% of that time being spent on data cleaning and organizing. Well-managed metadata could make it possible for data scientists to spend less time combing through unorganized data and more time putting their expertise to better use. Data science is one of the best-paid jobs in the sector, and requires highly-qualified, highly-skilled candidates – if metadata isn’t being managed well, and data is messy and unorganized, then data scientists are at risk of wasting their time, and their company is at risk of wasting money. A good metadata management strategy can allow data scientists to fulfil their potential in the workplace, and focus more on the jobs that only they are capable of doing.