Big Data has taken the world by storm, and even though it can be of huge value to some companies, for others it can be worthless. That is why your objective is to define that specific type of data that has the most value for your business and your users and then utilize it in a way that will enable you to extract that value.
Today, data is everywhere, and it is up to you to find the ideal way to properly utilize it in order to extract the highest amount of benefits for your company out of it.
Properly utilized data will empower leaders to make decisions based on facts, trends and statistical numbers, or in more direct terms — it will help you minimize the amount of money, time and resources that you’re wasting, as well as maximize the overall efficiency of your business.
However, the value of data is subjective. This means that, while something is valuable for one business or user, it can be worthless for another. Now that we established that fact, we can move on to talk about the value of data in general.
The value of data
When we talk about the value of data, we usually talk about 2 of its final outputs:
- Its ability to generate cash flow
- Its ability to solve problems
For a modern business, both of these outputs are equally important because they aren’t just closely dependent on one another, but also because they are closely connected to the following 5 aspects that have proven to be extremely important when it comes to determining and extracting the value of big data.
Analyzing data of an organization can identify a variety of inefficiencies within it, and lead to an increased transparency by leveling out the differences between departments. This can be very important as transparency has been proven to be the number one factor for employees’ work satisfaction. That is because transparency leads to trust and trust is important in every relationship — especially the one between an employer and an employee.
As enterprises create more and more transactional data, its analysis can provide new insights and patterns that might have not been identified before.
Analyzing big data can lead to creating different customer profiles and result in customizing products to fit specific market segments. Customization creates value for customers and that is the reason why they are willing to pay up to 20% more for a personalized product or service.
Algorithms that analyze big data can replace manual decision-making, thus optimizing processes and improving accuracy. By automating processes, your company can reduce costs and increase productivity.
By analyzing the data related to user behavior, companies can discover patterns that can identify the need for a new product or an upgrade of an existing one. This approach shows you what your users want, and that will ultimately help you generate more sales.
Every single one of these 5 aspects can increase a company’s ability to solve problems and generate cash flow, but we should also be aware of the metrics that can affect the value of data.
What can affect the value of data?
These 6 metrics are the primary variables that affect the value of data — they can affect it positively or negatively.
This metric measures how the value of data changes during the course of its lifetime. Is it evergreen in a way that it retains its value, or it decays over time, maybe it has a steady increase or decrease… all of this is explained through this metric.
This metric measures how valuable specific data is after all legal limitations are taken into account, because different types of data have different legal constraints and can’t be used in certain cases.
This metric takes into account the environments in which a specific dataset can be used. The more applicable a dataset is, the higher its value will be — unless we’re talking about extremely niche high-value environments.
This metric measures the quality of the data. For data to be useful to a business, its quality needs to be high when it comes to its accuracy, completeness and reliability.
This metric measures how much does it cost for a business to acquire a specific dataset and store it within their data warehouses before it can be utilized.
This metric determines the value of data based on its ability to train AI or machine learning systems, as well as the potential future value that systems trained on a specific dataset could generate.
These metrics can affect the value of data positively or negatively, but that, as well as how much will the value be affected, ultimately depends on your overall approach and business strategy regarding the gathering, processing and visualization of big data.