Overview of Enterprise Metadata Management
Today, data is a precious commodity. While it doesn’t have an absolute value like gold, certain data can be much more expensive than the precious metal. When a firm identifies a catalog of information as crucial to their operations and processes, they find themselves willing to pay hefty sums to get a hold of said data.
A major chunk of the internet populace has already seen ample material on the merits of collecting and using data, therefore it would be rather pointless to ramble on about how data can be used to your organization’s benefit.
Enterprise metadata management is the term given to the practices and methods of using data to its fullest potential, although you may already be aware of this, and the importance of data governance in a data-dominated sector. All this talk about corporate data governance can get old quickly, but the truth is, we haven’t even scratched the surface of metadata management and its potential.
According to recent research, the metadata management sector is predicted to grow at a CAGR of 19% from 2021 to 2026. What is now a $6.3 billion industry is expected to be worth $15.1 billion in another five years. Moreover, the lion’s share of this wealth is set to be centered within North America.
Now, what does this mean for players in the market?
While the general metadata management sector is expected to grow at a fair pace over the next five years, thorough research has been conducted to help pinpoint the verticals that should expect the highest levels of growth. Separated into instruments and services, the sectors’ growth can be forecasted based on the current levels of application in global markets.
To be more specific, sectors like BFSI, IT, retail, and manufacturing, have shown increased levels of acceptance towards metadata management tools in recent years. Using the current levels of involvement of metadata management in industry sectors allows us to create a predictive model for the growth, at the top of which sits the risk and compliance management sector.
The Importance and Applicability Of Metadata Management
Industries are beginning to realize the importance and applicability of metadata management in various fields, as ideas like structural health monitoring and financial risk prediction are at the forefront of the risk and compliance management field of metadata management.
The banking, insurance sector and financial services are the favorite to retain the most market size for the forecasted period until 2026, which bodes well for financial risk management and other similar firms.
Whether you practice data governance on an organizational level or not, research suggests that there is no time more opportune to be stepping into or upping the quality of your practices when it comes to metadata management. In order to latch on to rapid growth expected in the sector, Global IDs has prepared a list of practices to follow in accordance with their actionable governance methodology:
- First off, every organization differs from another, not only in regard to personnel and ideologies but often in terms of long- and short-term objectives. Even if the objectives of two different firms are to clash, the similarities would be vague. Therefore, it is imperative to understand the importance of outlining a metadata strategy. By doing so, you not only start by proceeding directly towards achieving your organizational objectives, but the digital transformation your firm will undergo is also aligned with these objectives, pointing everyone from top management to the bottom in the right direction.
- Once you know exactly what you want to do concerning all things’ data, you can start getting into the optimal ways for your organization to achieve this digital transformation. Expressing a digital hierarchy allows you to define what levels or parts of your organization act as creators, consumers, and managers of metadata. It also ensures that your enterprise will strive towards data governance in a more controllable manner, irrespective of whether the digital transformation is accepted by all levels of your firm.
- The next most important step is crucial to realizing the goal of optimal metadata management. Selecting the right metadata management tool or tools can make or break the digital transformation of an organization, or majorly affect the potential levels of data governance in the future. According to Gartner, tools that make the use of machine learning are extremely beneficial towards data governance, especially in the long run. At Global IDs, we practice data classification through the help of ML, allowing you to make the most of your data through intelligent optimization.
- Achieving standardization of your metadata set according to more widely accepted standards can enable ease of information transfer and assimilation to and from your customers or clients. Uniformity of data standards can make it easier for your organization to take on more business across a wider range of collaborations without any digital hindrances, which is a large step towards achieving data governance across your whole enterprise.
By carrying out these processes, you can witness a steady movement of your organization towards data governance, with the final destination being the attainment of data intelligence. Meticulously followed, these practices can enable organization-wide support for the practice of data enablement, ushering in a much more communicative era of data dominance.
Role of Global IDs in Enterprise Metadata Management
At Global IDs, we aspire towards heightening levels of data literacy across the various industries that run our economy by enabling companies to understand and organize data quickly. The quickest way to do so is to achieve data intelligence that matches the human intelligence that exists as the backbone behind any successful organization.
We identify metadata as the key towards achieving a balance between regulatory compliance and data value, which is becoming increasingly hard to achieve with increasing regulatory pressures. Our platform helps you create a map of your enterprise data landscape, factoring in data lineage and quality to provide the best possible analysis of your data landscape and identify data relevancy and its potential.