We continue elaborating on the four most often used cliches about data management demonstrated in Figure 1.
Figure 1: Logical relationships between the four most often used data-related cliches.
In the first article of the series, we discussed the implementation aspects of the statement “Data is a company’s asset.” If a company recognizes data as an asset, then the company should be able to get value from it.
In this article, we will:
Discuss the types of business values in relation to data and diverse business models
Demonstrate techniques to assess the business value
We will reach these goals by analyzing key steps the company can perform to get the value from its data.
Step 1: Identify the types of values
To implement the cliché “get value from data,” we first need to agree on the definition of “value.”
Figure 2: Different definitions of the term “value.”
Let us interpret these definitions in the “data” context:
“Value is “the monetary worth of something”. (Source)
In the “data” context, the “monetary worthiness” can mean a revenue in monetary terms.
A company should have a business model in which data is one of its products offered to external customers. Another example is savings in some costs due to improvements in data management. For example, IT costs can be decreased as the results of a reduction in data duplications and redundancy. The monetary value can be generated by avoiding potential losses. It takes place when a company avoids paying fines due to a data breach, for example.
The monetary worthiness indicates the measurability of the value.
Value is “a fair return or equivalent in goods, services, or money for something exchanged”. (Source)
The “fair return” can mean gains in monetary terms that a company can receive by offering some additional data-related services to customers. Even if customers don’t pay for these services, the attractiveness of such services can extend the customer’s base. The most important part of the value from the “data” perspective is still the measurability of the data value.
Value is “relative worth, utility, or importance.” (Source)
In the “data” world, data delivered to regulators has importance to both parties. Regulators get mandatory information about the company’s performance, and the company meets the external reporting requirements.
Value is “something (such as a principle or quality) intrinsically valuable or desirable”. (Source)
In the “data” content, data of required quality is valuable to its users.
By the end of Step 1, you should have a clear vision about the “value” definition to be used.
Step 2: Define the goals
The practical assessment of the data value requires a substantial business and financial analysis. To perform it, the data management capability should be operational and possess many artifacts related to application and data architecture, data modeling, data chain management, etc. Therefore, the goal of such an exercise should be important to the business and feasible. The optimization of existing data chains is an example of the goals to get the value from data.
Step 3: Map values to stakeholders’ groups and assess values
To assess the value that data delivers, we need to identify the beneficiaries of these values. The company itself is not the only beneficiary. The company as a whole enjoys to the greatest extent the monetary values expressed in revenues and gains. But besides the company, owners of the company would also profit from these monetary values. The truth is that the company has various external and internal stakeholders’ groups. These groups have different interests in the company and correspondingly in data. Therefore, these groups may get different values from data.
So, the second step is to list the stakeholders’ groups and identify the value that the company’s data generates for them.
The “Orange” data management framework uses the modified business model (link to the Data Lineage book), as shown in Figure 3.
Figure 3: The modified “business model canvas.”
Every company creates value for its external and internal customers by means of the value chain creation. In this model, the term “customer” has a broader meaning. In fact, we speak about the company’s stakeholders. However, the company’s partners are also stakeholders. To avoid the mismatch in the definitions, we still use the term “customer.”
To create value, the company should get resources from the partners. The company mainly delivers value by offering products and services. Data and information are products and services. The company maintains relationships and delivery channels with both partners and customers.
The purchased products and services generate revenue and financial gains. Along with the rest of the value chain, the company bears costs and expenses. The data chain maintains the value chain. If the data is a product or service to customers, then along the data chain the company also generates revenues and gains, and bears costs and expenses.
This model serves two purposes:
Analyze the data (management) value proposition per stakeholder group
Assess the value, monetary and non-monetary, and profitability per data chain
Of course, in real life, the situation is much more complicated for the following reasons:
Every company has multiple data chains
These data chains intersect each other
The same data is used in multiple chains
Step 4: Limit analysis to the critical customer groups, data chains, and data sets.
To make the assessment feasible, the company should limit the scope of the initiative. The concept of “data criticality” is one of the means to prioritize any data management initiative. Critical data (link to Data Lineage book) is data that is critical for managing business risks, making business decisions, and successfully operating a business. Critical data defines the critical chains and corresponding customer groups.
Assume, a company generates some revenue by selling particular data sets to a specific customer group. This customer group and data sets should be considered as critical for the business operations. Data chains that process these data sets are also critical. So, the value assessment will be limited to these critical data sets and chains.
If the data is not the company’s product, the value analysis is still worth to performing. Operational costs associated with data management are pretty high in each company. Application license and maintenance costs, salaries, data management processes are examples of these costs. The financial analysis of these costs can recommend the required data chains’ optimization.
Step 5: Elaborate on business possibilities to increase the value from data
By now, we have discussed how to analyze the value delivered by data using existing data chains. But of course, every company should evaluate their own strategic initiatives to get more value from the data.
To elaborate on the business opportunities, the company should be very clear about their focus on monetary or non-monetary value. The same analysis per customer group should assist in developing strategic views.
Let us briefly discuss which changes in business could lead to the increase in the monetary values:
Changes in business models An online platform is one of the business models that generates revenue from data. Amazon Marketplace, LinkedIn, Google Search, Booking.com are the most famous platforms that provide digital goods and services and are extremely profitable. The current trends in business development demonstrate that many companies focus on developing online services.
Focus on customer behavior and support Data analysis using artificial intelligence and machine learning technologies assist in understanding customers’ behavior. Companies can, in a more efficient way, recognize and satisfy customers’ needs. By doing that, they extend the customer basis that leads to the potential growth in revenues.
Focus on the cost savings and avoiding potential losses Many companies bear incredibly high costs associated with data processing and management. The following business initiatives should lead to the reduction of costs and avoidance of potential losses in the long term:
Replace legacy technology
Increase efficiency in business processes and data processing
Comply with data-related regulations
The goal of this article was only to highlight the key steps that the company should undertake to start getting value from data. In practice, accomplishing this goal requires great effort and resources. However, this approach is the only way to maintain the competitive advantages in the long term.
In the next article, we will discuss the “data-driven” concept as one of the means to get value from data.
Irina is a data management practitioner with more than 10 years of experience. The key areas of her professional expertise are the implementation of data management frameworks and data lineage.
Throughout the years, she has worked for global institutions as well as large- and medium-sized organizations in different sectors, including but not limited to financial institutions, professional services, and IT companies.
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