Data Monetization

Introduction

Data Monetization is the concept of gaining quantifiable economic benefit by using data. The first thing to consider here is how exactly companies use data to increase economic value. Typically data monetization covers both internal and external business performance measures that indicates areas of opportunity allowing executive personnel to make informed decisions to lower cost or increase business profits. The opportunities that data monetization create, spans a broad spectrum of industries and business sectors fostering a new and competitive atmosphere by leveraging key concepts from data analytics.

Data Analytics has increased in direct proportionality to software development and Internet of Things (IoT). Many companies find it crucial to understand what their data means and how does it translate to economic benefits. Traditionally, data was stored in silos and were ultimately ignored due to the sheer volume, as a result business decisions were primarily influenced by expert opinions or outdated business models. Today data analytics has completely changed the modus operandi for any company seeking dominance in their respective industry. This has been proven by the astronomical economical yield that data analytics has awarded successful companies who currently use data to drive strategic business decisions.

Data monetization is ultimately derived from business intelligence which resides under data analytics or Big Data Analytics. It consists of strategies, applications, methodologies and technology which are all used to maximize the use of data to accomplish effective resource management, cost reduction in processes, improvements in flows of companies, improvements in the product or services, stock analysis etc. As the data continues to rise companies all over the globe are continually investing in procedures that adapt, transform and generate large volumes of data contributing to today’s information age. The information age is the essence and embodiment of a shift from traditional industrial revolution to an economy that relies on information technology and the effective use of data. Reviewing the relevant business models and steps taken to effectively achieve effective data monetization practices would be useful for any company that seek to remain or become more competitive.

Data Driven Business models

Business models driven by data is an essential approach to increasing revenue while remaining flexible to implement new technologies, strategies and procedures that facilitates capitalization of new opportunities as data grows. This approach enables discovery for new business opportunities and customers and fosters a data centric atmosphere that may grant benefits to customers that is specific to their individual preferences which increases customer satisfaction and loyalty. In large corporations there are deliberate efforts placed on staff data analytics to promote staff loyalty reducing employee turnover rate, in addition data business models also account for embedded analytics that seek to resolve systemic or quantitative problems by improving products or services.

The processes of improving business performance or goals using methods and strategies derived from data are referred to as indirect data monetization practices because it depends on the result of decisions based on analytics, in most cases these business outcomes usually results in profits or enhancement. On the other hand the most direct form of data monetization is where companies sell data to third party firms who wish to use that data to create business opportunity for themselves. The data may be distributed in its raw form or transformed in data analytics before dissemination. It is important to note that government mandates help protect consumer personally identifiable information and allows consumers to deny dissemination of their information using the proper channels.

Steps to Data Monetization

Data Source Validation:It is important to consider all data silos, archives, databases and files as viable data sources that can provide economic benefit. This includes data related to clients, staff, supply chain, inventory etc. The understanding that analytics can provide insights to current, unknown and future problems can help companies to plan and prepare sufficiently for a desired outcome.

Data Aggregation / Warehousing:Make a deliberate decision to consolidate data for reporting and analytic purposes. Ensure all internal data is readily accessible or archived, as they present potential avenues for improving business performance or creating further business opportunities. It is essential to consolidate the data via a data warehouse bearing in mind architecture that will support performance of data retrieval. Data retrieval will be as a result of reporting needs which facilitates business intelligence. In addition external data can be coupled with internal data which can also be stored within a consolidated database for reporting. External data is any data that is not originated within the company, however, creates greater insights when combined with internal data, for example using google maps distance matrix API with internal delivery logistics data or weather forecasts APIs to coordinate important business events. Physical file storage is also another data source often overlooked, however, in certain instances may prove to be an invaluable source of data. The information from physical files can be extracted using appropriate tools that and then stored within appropriate databases or data warehouse. Essentially all data silos whether electronic or physical must be considered as a possible opportunity to increase revenue and should never be ignored. An effective data warehouse architecture will store all relevant data in a structured format and may be hosted in cloud or on a company machine depending on security policies in place for data storage.

Data Processing and Tools:Data processing is defined as the change and management of data attributes. This entails data transformation by using necessary tools that converts unstructured data into structured relational data prior to data aggregation and also utilizing relevant tools to access this data to perform analytics and reporting. Data transformation also includes changing the format, structure, or values within the data which is a relevant step in preparation for use or storage. Data processing tools provide the ability to manage data in small or large volumes, creating value from seemingly ambiguous data using analytics. Data processing tools are also regarded as ETL Tools because they are used to extract, transform and load data. Utilizing the right tools is pivotal in managing cost and effective preparation when considering the amount of processing power / storage required. Small volumes of data require less compute and storage while large data requires significant amount of processing power to sufficiently complete ETL in a timely manner.

Some examples of popular data processing tools are:
• - Microsoft Power BI
• - SAP
• - Jupyter Notebooks
• - SAS Business Intelligence
• - Tableau