Usage of AI in monetization of Data

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The economic value of data for companies is challenging to conceptualize and measure directly. Companies can monetize by improving customer experiences, reducing costs, finding new customers and so much more from the data that is produced directly or indirectly using big data analytics and AI. Many B2B  businesses understand that data monetization using AI and data analytics can create higher returns on investment and streamlined operations.   

The potential for data to deliver value for many parts of the business is enormous. For Data monetization, following five rules are necessary to follow:

1. Understand the role and value of Data in business - Good data management is about making sure you have the proper data to support your business and improve performance. Often companies fail to accurately value their data because it is not strictly accounted for as an asset even though it has real worth in external markets.

2. Get Data house in order - Before thinking about monetizing data, companies need to discover what kind of Data they hold about their partners, customers, products, assets or  transactions and what publicly available data can be called on to increase the value of their proprietary data. 

3. Embed data monetization into business strategy and get the right structures in place - Once you understand the quality of Data and have tied it to business strategy then you can put the right structures in place to monetize it. Together they can determine ownership structures for different data sets while making sure that sense of ownership doesn't result in data bottlenecks. 

4. Be open to new opportunities - Companies should be open to learning from other businesses and partnering in ways that make sense from data point of view. 

5. Communicate data's value internally and externally to foster growth -  Monetizing data is a relatively new experience for many organizations, and even when successful initiatives are in place they aren't always known to the business as a whole. Getting direct customer input is also important. 

Also website monetization is other process of converting existing traffic being sent to a particular website into revenue. The popular ways of monetizing a website are by implementing pay per click (PPC) and cost per impression (CPI/CPM) advertising with Google Adsense. There are two ways companies typically monetize data-  

- Direct Data monetization - sale of Data to third parties.

- Indirect Data monetization - using data to develop new business models and/or      boosting operational performance.

Data monetization depends on six main capabilities -

- Data acquisition and processing ( e.g. MDM, Meta Data Management etc.)

- Data platform tools (Hadoop, cloud, edge server etc.)

- Data science activities (Data exploration, Build models, Train and Test models, finding insights, visualize data etc.)

- Understanding customer needs behavior providing better customer experience (e.g. collect inputs from the Sales team about customer needs, analyze social media data to understand customers' behavior)

- Use of Data for meeting all concerns - regulations and compliance.

- Building data-oriented decision management culture at all levels in the organisation.     

Data is transferred in the form of bits between two or more devices. Two methods are used to transmit data between digital devices - Serial transmission and Parallel transmission. Serial data transmission sends bits one after another over a single channel. In parallel data transmission, multiple data bits are transmitted over multiple channels at the same time. This means that data can be sent much faster than using serial transmission methods. 

Given that multiple bits are sent over multiple channels at the same time, the order in which a bit string is received can depend on various conditions, such as proximity of data source, user location and bandwidth availability. A scenario where parallel transmission is used to send data is video streaming. Video streaming data sent is also time-sensitive as slow data streams result in poor viewer experience.    

Following four steps are needed as how to get started with data started as a service 

1) Choose a DaaS solution.

2) Sign up for and activate your DaaS platform.

3) Migrate data into the DaaS solution.

4) Begin leveraging the DaaS platform to deliver faster, more reliable data integration and data insights. 


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