At Xentity, we love data and love to espouse its massive importance in the operations of companies and organizations everywhere. This time, our focus is on data supply chains and how they support the creation of data ecosystems for datasets, and we will tie the activities of some organizations back to the data ecosystem and supply chain. The express goal here being to once again demonstrate how data is practically everywhere in this day and age.
First, a couple important terms to go through. The data supply chain is a lifecycle for data that propagates and procures data on behalf of corporations. Typically, the data supply chain consists of three parts:
1) On the supply side, you create capture and collect data.
2) Then during management and exchange, you enrich, curate, control and improve data.
3) And on the demand side, you utilize, consume and leverage data.
For a visual example, check out the image below from EDUCBA. This visual puts the supply chain into 5 steps. However, you will also notice that steps 2-3 and 4-5 arguably fall into the 2nd and 3rd parts we explained.
And Why Is It Important?
Data ecosystems are a collection of infrastructure, analytics, and applications used to capture and analyze data. Data ecosystems provide companies with data that they rely on to understand their customers and to make better pricing, operations, and marketing decisions. And we will be focusing on two types of datasets in particular: static and real-time datasets. Static data is data that does not change after being recorded. It is a fixed data set. By contrast, realtime datasets are dynamic forms of data that evolve in…real time.
Consider the kinds of datasets that exist out there, especially the aforementioned types. Imagine all the information out there that remains constant. Consider geographic coordinates as an example: latitude and longitude. Those never change, no matter what, and yet they are still just as important to certain companies and organizations as real-time (dynamic datasets). By comparison, a list of customers is constantly changing. In order for companies and organizations to be ‘on top of’ the constant updates of their customer list, the dataset needs to be ‘real-time’.
Regardless of your organization, the importance of both types of datasets does not change. Both exist as part of data ecosystems by the data supply chain. We create data from the information that exists (whether its geographic coordinates or customer locations). Then we enrich and utilize the data for organizations, creating these data ecosystems that companies rely on to understand various aspects of their operations.
Consider all of the organizations that use mapping, and as such use geographic coordinates, such as Esri, the United States Geological Survey (USGS), the latter of which is the United States of America’s National Mapping Agency. To understand how the supply chain would create an ecosystem here to help mapping organizations with their efforts, consider the three parts of the data supply chain, while also considering the three elements to every data ecosystem (infrastructure, analytics and applications).
Infrastructure is the foundation of the data ecosystem. It’s the hardware and software services that capture, collect and organize data. Think back to the first part of the data supply chain, which falls into the supply side: the creation, capturing and collecting of data. Through both the hardware and software services used by organizations and companies, along with the supply side of the data supply chain, the infrastructure of the data ecosystem is formed. The software that organizations would use here would be designed to store and capture data, such as mapping coordinates.
We use the analytics of a data ecosystem to search and summarize data within the infrastructure. This is crucial because analytics can provide far deeper insight into data. Analytics fall into the second part of the data supply chain. This where they manage and then enrich data. This is where even static data can sometimes become dynamic, because the results of an analysis can change over time. Geological surveys do not always have the same results, even though the locations remain the same. There’s also other kinds of information to examine, like who’s visiting what location and in what quantity? Through further enriching the data to provide more intuitive insights, organizations have the ability to use those insights to identify their desired conclusions, such as ideal customers.
Finally, we have the applications element of data ecosystems. These are the services that make data usable. This is where we hit the demand side of the supply chain, where data is utilized, consumed and leveraged. In our case, everything about latitudinal and longitudinal data can be exported into mapping software (GPS, CARTO, etc.). We then see these applications of static data deployed for public and private use. With all this said, you could make the argument that a data ecosystem for static datasets is mainly for public use. Mapping organizations and agencies may need static data for their software, but it’s ultimately the general public that needs consistent location-based data for mapping applications and software.
It is basically the same structure for real-time datasets and their ecosystem. We will be discussing each element of the data ecosystem (with the subject being real-time datasets). Then, how it aids the data supply chain.
Through the creation and capturing of data from the supply-side of the supply chain, there comes a need for softwares that and services that capture and store data. We mentioned customer lists as the example of real-time datasets (or dynamic data) earlier, so we will continue using this example. Data on customers (names, emails, their status as ‘current’ or ‘former’ customers) is supplied, so organizations began to build the infrastructure of the data ecosystem of their customers. We say it this particular way because every data ecosystem is different, varying from subject and organization.
Afterwards, we manage and organize the data to reach enrichment. In doing so, we use analytics to further the data ecosystem. You can take the list of customers you have, for example, and then filter it further, or use analytics to track what customers have bought from your organization, or what they have searched for. In this case, analytics can segment real-time datasets such as this and use them to make various marketing decisions (targeted ads, altered marketing strategies based on the popularity of products, etc.)
Finally, we reach the demand-side of the data supply chain, the leveraging of data. We mentioned earlier how the data ecosystem allows organizations to make educated marketing decisions. This is exactly how that happens. Once the enriched customer data that came as the result of the analysis of the data, perhaps said customer data was imported into appropriate marketing applications, allowing marketing and sales teams to respond properly. Not just the aforementioned targeted ads based on user activity, but also price changes and sales based on product popularity. The applications element is where the data ecosystem truly shines through, because the overall goal of the data ecosystem is to provide companies the means to make better decisions through data.
It All Comes Back to Data
If the whole point of a data ecosystem is to help a company make educated decisions on how to operate, educated decisions that come through a better understanding of customers and the responses to their products, then you absolutely need data to accomplish all of this. We have all heard the term ‘making an educated guess’. Educated guesses come from knowledge and experience.
But where does that knowledge and experience come from? Could you imagine what making an uneducated guess would be like? That’s basically what it would be like if you made any kind of guess without data to back you up. That is exactly why the supply chain exists. To provide us with the opportunity to create, enrich and leverage data as organizations for public users. Each part of the data supply chain takes part in the creation of each company’s data ecosystem. That’s whether the ecosystem holds static or real-time datasets.
It all comes back to data, and this is exactly why at Xentity puts “I” back in “IT” and “GIS” as a data consulting firm. After creating all that technology, the information (data) has practically been left in the dust. Yet without it, we are unable to make those ‘educated guesses’ we as humans love making so much. It would just be gut instinct at that point. So take a moment to appreciate the data supply chain, because it’s process is exactly what creates the ecosystems that allow us to make educated decisions as organizations.