Question

Evaluate various approaches organisations use to monetise digital data.

• Discuss what is meant by data value.
• Argue how companies should formalise data valuation practices.
• Investigate how a company could embed data valuation into company-wide strategies.

Answer 1

The word 'Organization 'is derived from the Greek word organon which means tool or instrument and organ.An organization is an entity comprising multiple people,such as an institution or an association, that has a particular purpose

Data monetization refers to the process of identifying and marketing data or data based products to generate monetary value. Data products(I.e.,products based on raw,refined or analysed data)are at the heart of data monetization.

Organizations Approaches to Monetise Data

Companies can take three approaches to monetizing their data:

1)Improving internal business processes and decisions.

2)Wrapping information around core products and services, and

3)Selling information offerings to new and existing markets.

Improving Internal Processes

Using data to improve operational processes and boost decision making quality may not be the the most glamorous path to monetizing data,but it is the most immediate. Companies see positive results when they put data and analytics in the hands of employees who are positioned to make decisions such as those who interact with customers, oversee product development or run production process

When Satya Nadella became CEO of Microsoft Corp. In February 2014,he urged employees to find ways to improve the company's processes with data. Within a year,they created a new integrated customer system that could produce 360 degree views of Microsoft relationships with corporate customers including what customers bought,what issues they encountered and how the company engaged with them.It also led the sales people to forecast more accurately, better sales pipeline data and in turn improved pipeline management.

Wrapping Information Around Products

Companies are wrapping I.e.,using data and analytics to enrich their products, services and customer services their offerings with data to escape commoditization and increasingly hard to please customers- with the goals of generating sale increases, higher prices and deeper customers loyalty. FedEx Corp was an early exemplar of wrapping when it introduced online package tracking as a free service in the 1990s.

Wrapping activities are best viewed as extensions of a company management processes. Doing so requires comparable levels of scrutiny and control.Infact,exposing data to customers reveal quality problems and a lack of analytical sophistication. Thus ,in most cases,wrapping doesn't damage their reputation or undermines their value proposition. It may entail heavy investment in data quality programs, advanced computing platforms or data science talent.

Selling Data

Many executives are eager to sell their companies data,convinced that it has an inherent value and can generate important new revenues for the company. We caution that selling represents the hardest way to monetize data,mainly because it requires a unique business model that most companies are not set up to execute. Yet it can be done to potentially great effect under the right circumstances .Companies should think carefully about the operational capabilities, investment, and commitment required to successfully sell data.

Conclusion:Companies can increase their "earnings per byte" by reimagining a future where they not only maximize value creation internally, but also create a market for their highly valuable data and insights. The approaches mean that they will not only changing the playing field ,but reinventing the game ,and securing market dominance early on.

DATA VALUE

The value that derived from processing the data using different analytics that contributes to problem solving. A data value is an element of value domain.Data value is defined as the composite of three sources of value:1)the asset,or stock, value;2)the activity value; and 3) the expected, or future value

Think of data as an asset;organizations deploy assets to create value for different stakeholders Data valuations affects companies of all sizes. Companies must understand how to value their data to be able to monetize it accurately. There is no common standard models for data valuations. It is complex as the value of data depends on various factors and even the same data can have different value for different users.It is a serious challenge for both potential investors and company itself.

Once you know the value of data assets, you can

*Prioritize and fund information management initiatives that have high business value

*Monetize your data

*Drive innovation and digitization.

Conclusion:It is hard to estimate the company's business value and future potential accurately. What makes it difficult is that data is an asset not yet recognized by generally accepted accounting principles.

COMPANIES FORMALIZING DATA VALUATIONS PRACTICES

Financial statements formalization involves adjusting non recurring expenses or revenues in financial statements so that they can reflect only the usual transactions of a company .Its purpose is to eliminate such anomalies and provide accurate historical information that enables reliable comparison.

Makimb6 implicate data policies explicit codified and shareable across the company is a first step in prioritizing data values.

Embedding data valuation into company wide strategies.

No matter which part a company chooses to embed data valuation into company wide strategies the research and covers three practical steps that all the companies can take.

1. Make valuation ploicies explicit and shareable cross the company it is critical to develop company wide policies in any area for example if your company creating a data catalogue so that all data acids are not? Are you tracking the usage of data asset much like a company tracks mileage on the cards on trucks it owns making implicit data policies explicit codified and shareable across the company is a first step in prioritising data valuation.

2. Build inhouse data valuation expertise.

several companies are exploring ways to monetise data as its for sale aur licensing to third parties however having data to sell is not the same thing as knowing how to sell it. Several of the companies relied on outside experts rather than in house expertise to value their data companies seeking to monetise their data as its first need to address how to acquire and develop valuation expertise in their own organisations.

3. Decide whether topdown or bottom up valuations process are the most effective within the company.

In the top down approach to valuing data companies identify the critical application and assign the value to the data used in those applications whether they are a mainframe transaction system, customer relationship management system or a product development system approach has the benefit of prioritising their internal partnership between IT and business units need to be built if they are not ready in place.

A second approach is used to define data value heuristiclly in effect working up from a map off data usage across the core data sets in the company. Key steps in this approach includes assessing data flows and linkages across data and applications and producing a detailed Analysis of data usage patterns.companies may already have much of the required information in data storage devices and distributed systems.

Conclusion

Which ever approach is taken the first step is to identify the business and Technology events that trigger the business need for valuation I need based approach will help senior management prioritise and drive valuation strategies moving the company forward in monetizing the current and future value of its digital asset.