Data Valuation is an emerging discipline aimed at estimating the information’s value as a business asset.
Information is an intangible asset that is becoming more and more important within organizations. However, the current accounting policies do not require assessing their actual or potential value. Business information and its physical representation in systems are two sides of the same coin. In business systems, an information entity needs to be:
- created,
- controlled and validated,
- updated and aggregated,
- reconciled and overseen,
- persisted and published,
- moved and archived.
In other words, it needs to be managed. The information’s meaning drives the business processes and decisions. However, not all information has the same value in the business operation.
What are the aims of Data Valuation?
- quantify the value and the costs of data assets,
- understand how to improve data management,
- identify innovation opportunities
- promote more data-oriented business culture.
We can attain these aims, possibly with the help of the Chief Financial Officer, by defining a standard methodology. It will help us measure the financial value of data as if they were assets and liabilities on the balance sheet.
What do we need to implement Data Valuation?
What is not measured cannot be managed. Data Governance is essential for assessing the value of data assets. The three fundamental steps are:
To catalog
A Data Catalog registers what exists and directs the information governance activities. In this process, we can enrich the data with metadata accessible within the company. This will expand the scope of the analysis. The registered entities are represented in the model along with the relations between them. This creates an understanding that extends the possibilities of data governance. It provides a tool for assessing the value of activities and data assets. And this is essential in the world of business and IT that goes round data.
To manage
We need to register the activities that produce relevant output. It allows to evaluate the data value both in qualitative:
- how important the data is for the company,
- how many relevant outputs benefit from it, etc.
and in quantitative terms:
- how much the data production and management costs,
- what its commercial value is, etc.
We need to understand who (systems, organizational units) does what (automatically or manually) and using which data assets. Then we can build a data value chain to identify the areas that need improvement and innovation. Once we understand which contexts use a Data Asset, we can understand its strategic value.
To measure
To typical system entities of data governance, add new ones that represent new quantities: data valuation models.
The benefits that follow are numerous. The Chief Data Officer can derive from Data Valuation the base for valuing the Data Management. This value, in the form of the report shared with the CIO, the CFO, the CEO, can highlight the economic benefits (both quantitative and qualitative) of Data Governance activities. Furthermore, based on intrinsic and economic value of data one can create a data relevance scale. The latter can help the decision-makers assess and choose the procedures and investments in data management, determining priorities and budget. The data value may also be important to those in charge of the risk management, such as the CRO and the CISO. For example, it is relevant to know the costs of a potential loss, clients’ data theft or use of erroneous data. A complete view of the information, rather than regarding it by silos, gives a chance to improve the efficiency. It lays the groundwork for new interactions and uses of data (marketing, products, services…), and offers further opportunities to deliver innovative services.
Irion EDM offers Data Management solutions with a particular focus on Data Governance practices. They will help realize a flexible Data Catalog and Business Glossary. These make it easy to map the business terms with the physical entities in computer systems, define and navigate the corporate processes via Data Lineage, and perform impact analysis. The solution is in perfect compliance with GDPR. It aims at managing complex structures and large volumes of data.
We created this software with a collaborative approach in mind. Several teams can simultaneously work on prototyping and developing a solution. And their work will be automatically coordinated. The solution also has native mechanisms of versioning, granular permissioning, historization, approval workflow. We offer a dynamic and flexible tool designed for rapid prototyping. The metamodel structure can be changed on the fly by adding, modifying or deleting entities, attributes or links without losing the data already entered. To all this, add the ability to realize even complex Data Valuation models in order to sustain the enterprise value.
Want to learn more?
Contact us! We will provide you with illustrative examples of how other organizations have already started their transformation.
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