{"id":10924,"date":"2022-05-18T07:20:51","date_gmt":"2022-05-18T12:20:51","guid":{"rendered":"https:\/\/flevy.com\/blog\/?p=10924"},"modified":"2022-05-18T07:20:53","modified_gmt":"2022-05-18T12:20:53","slug":"data-valuation","status":"publish","type":"post","link":"https:\/\/flevy.com\/blog\/data-valuation\/","title":{"rendered":"Data Valuation"},"content":{"rendered":"\n
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There is abundance of data being generated and stored by organizations across the globe.  Data storage is growing at the pace of around 40% per year.  Digital assets are of immense value for organizations.  However, not too many organizations know the real worth of their data. <\/p>\n\n\n\n

Technological disruption and adoption of Big Data<\/a> and Analytics <\/a>as the major engine to creating priceless insights and a Learning Culture has pushed the organizations to know how much their data assets actually value.  Data Valuation, moreover, plays a key role in evaluating M&A deals<\/a>, bankruptcy filings, and sales of data assets.<\/p>\n\n\n\n

Enterprises are increasingly gathering and analyzing vast pools of data generated on a daily basis to know more about their customers\u2019 requirements and preferences.  For instance, Microsoft purchased LinkedIn for $26.2 billion in 2016.  The major drive behind this acquisition of LinkedIn was the 433 million registered users (100 million active users) and the vast pools of valuable data that they generated each day. <\/p>\n\n\n\n

Instead of valuing their data assets, organizations are generally more concerned about the issues associated with data storage, security, access, and analysis.  These issues are the core responsibility of the IT function however, the IT unit alone is incapable of making the call to transform data into business value. <\/p>\n\n\n\n

At most companies there aren\u2019t any Data Valuation practices in place.  In the name of Data Valuation practices, the most that majority of the organizations embark on include time and effort-intensive Data Classification<\/a> efforts.   <\/p>\n\n\n\n

These Data Classification initiatives typically require categorization of terabytes of data into \u201ccritical,\u201d \u201cimportant,\u201d and \u201cother\u201d groups.  The \u201cother\u201d category is often challenging to categorize due to its context-specific nature.  The valuation of this category is complicated as it is based on a number of attributes, e.g. content, usage, age, author, frequency, history, revenue potential, security needed etc.  <\/p>\n\n\n\n

To work around these challenges, executives need to formalize their data valuation practices into a structured Data Valuation Framework.  The Data Valuation Framework is a combination of 3 sources of value:<\/p>\n\n\n\n