Can Data Be A Liability For The Business?

Today, almost every organization is trying to leverage data for improving its business performance using artificial intelligence (AI) and machine learning (ML) techniques. Fundamentally, data has the potential to improve the company’s revenue, reduce expenses and mitigate risk. While data can be a valuable business asset by offering tangible business results, it has some serious limitations and can become a huge liability if not managed well. How can an intangible asset like data become a liability for business? There are four common scenarios:

  • Collecting data without a defined business purpose will result in huge data volumes, ultimately resulting in increased data management complexity and cost. In 2018, according to Deloitte, the average IT spending in a company was 3.3% of the top line and trending upwards at an average of 49% every year. One important reason attributed to these increased IT expenses is the processing of huge data volumes. In addition, if the data is captured without a defined purpose, it will remain unused. Forrester found that between 60% and 73% of data in a company is never used strategically, and research by Carnegie Mellon University (via Forbes) has found that 90% of the data in an organization is “dark data.”
  • Data takes up vast amounts of energy to store, secure and process, resulting in an increase in the carbon footprint for the business. This makes it less attractive for investors considering their growing interest in ESG commitments these days. In 2018, data centers consumed roughly 1% of total global electricity. By 2025, according to Swedish researcher Anders Andrae (via The Guardian), the energy consumption of data centers is set to account for 3.2% of the total worldwide carbon emissions and consume 20% of global electricity.
  • Cybercriminals are drawn to organizations that have large volumes of data. Many cyber crimes and data breaches in the last few years are associated with organizations that have large databases. These cybercriminals do not care whether or not the data is dark data and acquire all the data they can get their hands on. Following its 2017 data breach, Equifax spent $1.4 billion on modifying its technology infrastructure.
  • Managing data also entails privacy compliance. As Fortune noted, Facebook lost $35 billion in market value following the Cambridge Analytica data scandal. In addition, the scandal resulted in the permanent closure of Cambridge Analytica. While it was data that was responsible for the success and growth of Cambridge Analytica, it was the same data that resulted in the collapse and ultimate closure of Cambridge Analytica.

What can organizations do to transform data into a business asset? What can businesses do to prevent data from becoming a liability? Below are three key recommended strategies for business enterprises to transform a valuable resource like data into a business asset.

  1. Data management should be purpose-driven. Fundamentally, data is used for three main purposes in business: (a) operations to serve its stakeholders, (b) compliance with industry standards, security policies and government laws and regulations, and (c) derive insights for decision-making. If the data captured does not clearly associate itself with one or more of the above three purposes, there is a good chance that the data will eventually become dark data or unused data, consuming valuable business resources and providing little or no value.
  2. Data should be structured. According to an article published by CIO, over 80% of business data is documents, audio, video, images and more. These data elements are unstructured (i.e., they do not have a predefined data model and data type). When data has the right structure, it enables efficient data access and processing. From the insight derivation or analytics perspective, the structure provides the right data type (i.e., nominal, ordinal or numeric). The data type is important because it holds the key in analytics in facilitating the selection of the right statistical technique for insight derivation. In other words, structuring the data enhances its utility. For example, in predictive analytics, if the response data type is numeric in nature, linear regression is the preferred technique. However, if the response data type is nominal or categorical in nature, the recommended predictive analytics technique to be applied will be logistics regression.
  3. Data should be nonsubstitutable. Businesses always look for resources that are cheaper to procure, faster to deploy and reliable to consume. From the analytics point of view, insights can be derived from intuition or data. If the data and analytics literacy in the company is low, intuition precedes data as the main option for deriving insights. While intuition has some advantages, what is needed in today’s VUCA (volatility, uncertainty, complexity and ambiguity) business environment is deriving insights holistically by combining both intuition and data.

Overall, data is a valuable resource and has the potential to become a valuable asset for business enterprises. However, just capturing and storing data does not make data a valuable enterprise asset nor does it make a company data-driven. Data is a business asset only when it is consciously captured and deliberately managed; if not, data can become a huge liability that threatens the very existence of the firm.

This article was originally published by Dr.Southekal in Forbes.com


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