Bill Text: WV HB2148 | 2022 | Regular Session | Introduced
Bill Title: Imposing a general data mining service tax on commercial data operators
Spectrum: Partisan Bill (Democrat 1-0)
Status: (Introduced - Dead) 2022-01-12 - To House Judiciary [HB2148 Detail]
Download: West_Virginia-2022-HB2148-Introduced.html
WEST virginia legislature
2021 regular session
Introduced
House Bill 2148
By Delegate Thompson
[Introduced February 10, 2021; Referred to the Committee on the Judiciary then Finance]
A BILL to amend the Code of West Virginia, 1931, as amended, by adding thereto a new article, designated §11-29-1, §11-29-2, §11-29-3, §11-29-4, §11-29-5, §11-29-6, §11-29-7 and, §11-29-8, all relating to imposing a general data mining service tax on commercial data operators; making legislative findings; definitions; establishing a valuation method; establishing the tax and the rate of tax; requiring the proceeds be deposited into the Public Employees Insurance Agency Financial Stability Fund; and requiring rule-making.
Be it enacted by the Legislature of West Virginia:
ARTICLE 29. DATA MINING SERVICE TAX.
§11-29-1. General data mining service tax imposed.
The purpose of this article is to impose a general data mining service tax.
§11-29-2. Legislative findings.
The Legislature hereby finds and declares that:
(1) The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as “knowledge discovery in databases,” the term “data mining” wasn’t coined until the 1990s. Its foundation comprises three intertwined scientific disciplines: statistics, which is the numeric study of data relationships, artificial intelligence, which is human-like intelligence displayed by software or machines, and machine learning, which refers to algorithms that can learn from data to make predictions. Data mining technology keeps evolving to keep pace with the potential of big data and computing power;
(2) Over the last decade, advances in processing power and speed have enabled data miners to move beyond manual, tedious, and time-consuming practices to quick, easy, and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers, and insurers, among others, are using data mining to discover relationships among everything from price optimization, promotions, and demographics to how the economy, risk, competition, and social media are affecting their business models, revenues, operations, and customer relationships;
(3) Data mining allows organizations to continually analyze data and automate both routine and critical decisions without the delay of human judgment. Banks can instantly detect fraudulent transactions, request verification, and even secure personal information to protect customers against identity theft. Deployed within a firm’s operational algorithms, these models can collect, analyze, and act on data independently to streamline decision-making and enhance the daily processes of an organization;
(4) Planning is a critical process within every organization. Data mining facilitates planning and provides managers with reliable forecasts based on past trends and current conditions. For example, Macy’s implements demand forecasting models to predict the demand for each clothing category at each store and route the appropriate inventory to efficiently meet the market’s needs.
(5) Data mining allows for more efficient use and allocation of resources. Organizations can plan and make automated decisions with accurate forecasts that will result in maximum cost reduction. Delta imbedded RFID chips in passengers’ checked baggage and deployed data mining models to identify holes in their process and reduce the number of bags mishandled. This process improvement increases passenger satisfaction and decreases the cost of searching for and re-routing lost baggage;
(6) Firms deploy data mining models from customer data to uncover key characteristics and differences among their customers. Data mining can be used to create personas and personalize each touchpoint to increase its revenue. In 2017, Disney invested over $1 billion to create and implement “Magic Bands.” These bands have a symbiotic relationship with consumers, working to increase revenue streams at the resort while simultaneously collecting data on their activities for Disney to analyze to further enhance its bottom line;
(7) Data mining essentially extracts information database users were not aware of and presents it in actionable format. It predicts customer behavior and, applied properly through campaign management software using advanced statistical, numerical and multivariate techniques, can directly target consumers on an individual basis;
(8) Companies are already sitting on a diamond mine of data waiting to be turned into revenue. Employing the use of professional data mining will have a noticeable effect on those companies’ bottom lines. The more data collected, deciphered, and leveraged the greater the revenue; and
(9) The data being collected and mined belongs to the public, the consumers, the citizens and taxpayers from whom it is gleaned. The data’s intrinsic value is substantially increased when it is mined and thereafter used to further the economic interests of the data miners who then either use it themselves or sell to others. Thus, the citizens of West Virginia are providing something of substantial value for which they are not being compensated.
§11-29-3. Definitions.
As used in this article:
“Commercial data operator” means a person acting in the capacity as a consumer online services provider or data broker that:
(A) Generates a material amount of revenue from the use, collection, processing, sale, or sharing of the user data; and
(B) Has more than 10,000 unique monthly visitors or users in West Virginia for a majority of months during the previous one-year period.
“User” means an individual consumer who uses an online service designed for consumer use by a commercial data operator.
“User data” means any information that identifies, relates to, describes, is capable of being associated with, or could reasonably be linked with an individual user, whether directly submitted to the commercial data operator by the user or derived from the observed activity of the user by the commercial data operator.
§11-29-4. Commercial data operators.
A commercial data operator shall, on a routine basis, and not less frequently than once every 90 days:
(a) Provide the commissioner with an assessment of the economic value that the commercial data operator places on the data of its users; and
(b) Identify to the commissioner:
(1) The types of data collected from users of the commercial data operator, whether by the commercial data operator or another person pursuant to an agreement with the commercial data operator; and
(2) The ways that the data of a user of the commercial data operator is used if the use is not directly or exclusively related to the online service that the commercial data operator provides to the user.
§11-29-5. Valuation methodology.
The commissioner, in consultation with appropriate standards settings organizations, the Broadband Enhancement Council created by §31G-1-1 et seq. of this code, and other state or private persons, shall develop a method or methods for calculating the value of user data. In developing this method the commissioner shall promote comparability in calculating the value of data across commercial data operators that utilize user data in a similar manner while taking into account the potential need to develop distinct methods for calculating the value of data for different uses, sectors, and business models.
§11-29-6. Imposition of tax on revenue generated by commercial data operators; amount of tax.
(a) For the privilege of commercial data operators generating revenue from the use, collection, processing, sale, or sharing of the user data from the citizens of West Virginia, there is hereby levied and shall be collected a general data mining service tax from commercial data operators generating such revenue on revenue so generated. The tax shall be paid to and collected by the commissioner in accordance with the provisions of this chapter.
(b) The general data mining service tax imposed in subsection (a) of this section shall be at the rate of 1 cent per the dollar of value of user data obtained in this state.
§11-29-7. Allocation of tax.
The tax imposed by this article shall be deposited into the Public Employees Insurance Agency Financial Stability Fund to stabilize and preserve the future solvency of PEIA, and the amount may not be included in the calculation of any plan year aggregate premium cost-sharing percentages between employers and employees.
§11-29-8. Rule-making.
The commissioner shall propose rules for legislative approval in accordance with §29A-3-1 et seq. of this code to implement this article.
NOTE: The purpose of this bill is to impose a general data mining service tax on commercial data operators. The bill makes legislative findings. The bill establishes a valuation method. The bill establishes the rate of tax. The bill requires the proceeds be deposited into the Public Employees Insurance Agency Financial Stability Fund. The bill requires rule-making. The bill defines terms.
Strike-throughs indicate language that would be stricken from a heading or the present law and underscoring indicates new language that would be added.
