In a previous article, we listed the main developments in data architectures and data management. This ranged from the current iData Factory and Edge Data Management implementations to the search for practical implementations for Data Mesh architectures.
In this second article we complete the list of trends outside data architecture.
GADMP Framework (Generally Accepted Data Management Principles)
Part of implementing a data strategy in our company is to define a list of principles and commit to them. With these principles it will be possible to define policies and guidelines and achieve best practices in data management. There are standard frameworks of "generally accepted" principles that are used as a starting point in the definition of these principles, such as GAAP, GARP, GASSP or Gartner's GAIP.
Susan Earley proposed the framework GADMP to facilitate the phases of defining principles within data governance. Starting from these principles greatly facilitates the definition of organisational policies and guidelines. The eight principles are as follows:
- Data as an asset (Worth as an asset).
- Ethical use of data (Ethical Use).
- Data Management as everyone's job (Cultural Incorporation).
- Focus on the data lifecycle (Data Lifecycle Ownership).
- Incorporation into operational processes (Operational Incorporation).
- Transformation of operational processes (Operational Transparency).
- Prevention is better than cure (Prevention > Remediation).
- Reuse increments the value (Reuse improves value).
Data Exchange / Data Market in data management
As the number of available sources of information increases, it becomes more complicated to carry out analytical phases given the usual diversity of the data. This problem is compounded by the possible scarcity of information associated with the data, its meaning, its expiry date, etc.
This trend seeks to create a one-stop shop for information exchange. Each block of information is treated as a product and its exchange (Data Exchange) or even monetisation (Data Market) is facilitated. Each product must contain minimum information including a description of its usefulness, modes of use, expiry date, lineage, etc. This system saves analysts from spending long days browsing through databases looking for information and investigating whether it is the latest version of the data. Some tools such as Snowflake include a Data Exchange system, and there are also platforms such as Nokia Data Marketplace where you can easily buy data.
New regulatory frameworks and legal regimes (Compliance)
The personal data we store in our organisation may be subject to Control Bodies. For example, in Spain we had the LOPD and its ARCO rights and after its modification by the LOPDGDD it was replaced by the current European RGDP. The regulations require changes at the level of data processing and non-compliance can lead to sanctions.
At the time, it was necessary to generate new operational procedures to guarantee ARCO rights. After the advent of the GDPR in 2018, it was also necessary to cover the new rights it brought with it. During 2023 it will be necessary to look at the regulatory frameworks of California (USA), Virginia (USA) and Switzerland, if we operate with personal data of citizens of those locations. In addition, and although it is a draft pending approval, it is interesting to keep an eye on the Canadian CPPA regulations (currently Bill C-11) in case these regulations apply to our data.
Augmented Data Management
Daily operations in data management are generally a manual process and, in most areas, we act according to the needs of our company. Why not take advantage of artificial intelligence and machine learning to optimise these processes?
From the term 'Augmented' a number of disciplines have emerged (Augmented Data Quality, Augmented Data Cataloging & MMIS, Augmented Master Data Management, etc.), which use these techniques to improve operational processes. The adoption of new architectures will facilitate the integration of these services to enrich our processes and information systems.
Over the next few years (2023-2025) we will be watching for the maturity of these trends and the possible emergence of new solutions within the Data Management field.
