Data Value Chain: What is it & How to Create Value[4 Facts]?
Creating value inside and outside of the working environment brings mutual benefit, do not forget to use data to skyrocket!
TL;DR Value creation is an important step in understanding internal and external factors as well as the way of obtaining and structuring data. Data Value Chain process is a good starting point that leads towards Big Data Value Chain that provides further insight into data-driven value creation for digital companies.
What is Value Creation?
What every successful entrepreneur or CEO knows is that by creating value for your clients, the company will receive financial or non-financial benefits.
However, we have not yet defined what value creation exactly is?
Well, by definition value creation is giving something valuable to receive something else that is beneficial for you as well. This means that the company is producing value for its shareholders or stakeholders in return for its trust for example.
It could also be the mission or a vision of your company that highlights your long-term goals where you would like to contribute to certain non-profit aspects of society for example.
There needs to be a clear understanding of the benefit vs. cost of value creation where both sides are not worse off.
Check the below video for customer value creation:
The easiest example would be that through content optimization of your web page you would have pages that highlight queries of your potential client base in a certain field of operations where your company is active.
The answers to the client queries would be informative so that the reader who asked the question receives an answer in the given topic while at the same time is engaged in other similar topics and gets interested in the content of your web page.
By doing so, you are building trust as well as giving value while at the same time you are “promoting” your services.
On the other hand, your company might be having a shortage of workers in certain specialization and you are struggling to find the proper ones. How do you help yourself?
Exactly by creating value!
The company should once or twice a year make a free academy or a boot camp for certain degree related students or professionals that will guide them towards learning and implementation of the existing company’s tasks. On top of that, at the end of the academy cycle, certain competition should be made for the top ones to be elected as the new employees.
By creating such value, some of the academy students will gladly accept a career change or an entry position while at the same time you are creating the right employees for the right positions!
Therefore, by knowing where and how your company would like to produce the value that is more effective than your competitor one you are ready to understand what data you need to make that happen.
However, what if you have an intangible digital service or a product and traditional value creation is not working because you need data insight as well above the client/employee basis.
Let’s find out!
How Do You Get The Insight?
Obviously, you will not be producing content, creating workshops, or engaging in local community projects without proper research of the client’s interests and what data you will need in case of an intangible asset.
According to Michael Porter, the value chain (VC) is the key solution to this issue.
The value chain breaks down the organization’s business activities into the most strategically important ones to simplify the process of value creation.
The evolution of VC began in the 1980s by M. Porter as an analytical tool for distinguishing main and supporting business activities responsible for creating value.
Marketing and sales were considered as main activities, while technology development was a secondary one among the few. None of the models included data in the value chain process meaning intangible assets were completely neglected.
A more detailed overview is below:
Although the traditional process is efficient for tangible assets it is not for intangible ones. Therefore, a data-centric approach instead of a product/process-centric process is implemented — Data Value Chain (DVC).
Digital-oriented organizations have therefore become familiar with the DVC model that relies on data extraction for useful insight. DVC defines a set of systematic methods for the data extraction and its cycle from raw data to insight value.
According to some scientific papers, DVC consists of nine steps but we will focus on four core ones:
Data management has changed over the years with many new technologies in place as well as various storage options.
None of that would be possible without Big Data that captures raw data circulating through any device connected to the Internet, at the same time stores, analyzes, and uses data to predict behaviors or events.
Therefore a new bigger concept needed to be implemented the Big Data Value Chain (BDVC) but we will leave it for the next time as it is an add-on to the DVC model.
Data Generation
Data can be generated both internally and externally depending on the strategy of the company and its goal. This part is the beginning of Big Data as it provides huge amounts of information.
In the business way, data is obtained internally by checking business intelligence, sales & marketing data while externally it could be a basic questionnaire to gain customer satisfaction with a certain service or a product.
External can also be a local government decision regarding the implementation of certain projects, laws, etc. that affects your organizational activities as well as competition.
In the other sense, data generated by the web on certain platforms (search engines, social media, etc.) and in certain fields are a great asset for specific companies operating in that niche.
By obtaining potential client base trends and interests through Big Data, those organizations could position themselves better on the market.
Data Collection
This part consists of collecting, validating and storing data that will be useful for a certain organization.
In the first stage (collection), the relevant data is collected while keeping attention to the source. The second stage (validation) ensures the quality and related benchmark requirements, while in the last stage (storage) the data is stored in a database.
Data Analysis
This stage is the most important one as it gathers the collected information and extracts it for certain decision making.
Data can be historic, present, or in a form of a prediction for a certain new product or a service for example in the form of a business report. The report might be used by the company for every week’s sales target evaluation by using “fresh” data compared with the past ones of the same period of the year.
Data Exchange
The last part is the one where the company has its data in the form of a business report, is structuring it as it wishes by its key points and further evaluates its business position, product launch predictions, or any other reason needed for value creation.
In other words, organizations should,
- Determine their organization activities (primary & supporting)
- Analyze value/cost ratio of their activities to be efficient
- Check the competition and “push” only those activities where they have an advantage
- Identify opportunities from data to create competitive value
The Main Goal of Every Business is Success
To understand the value, organizations need to understand their potential and current customers, their strategy and the field of operations. By knowing these, the next steps are their competitors and their advantages and disadvantages that are vital for value creation.
Therefore, the knowledge of the DVC process will provide a good starting point and insight into what to look for in providing value that will benefit others but will benefit themselves even more!
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I am a Content Marketer with an MSc in Finance, you can contact me if you are interested in cooperation on business, finance, aviation, or technology topics.
