External data can be used for business improvement. However, it is important to first examine the source and implementation of external data into management processes.
Every year, external data becomes more useful. The number of external data applications is increasing as acquisition becomes easier and more accessible to smaller businesses. However, proper data management remains a problem. An analysis of the last few years shows that even renowned enterprises can have trouble managing data.
Before I continue, I recommend reading the previous article on this subject. It would be much easier to jump into external data acquisition and management if you have done the necessary groundwork.
This may seem simple at first. External data can be defined as any data acquired outside of an organization. It is often called third-party or second-party data in marketing.
There are three key distinctions to be made between traditional and advanced external data. The first is that most people are familiar with the traditional sources of external data – government records and statistics departments, press releases, etc.
Although it is not used as often by businesses, it still has a place in the financial sector and many other industries. Advanced external data aims to reach a wider audience.
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Many companies use advanced external data, such as customer review tracking or social media sentiment analysis.
Alternative data isn’t a new type, but rather quality. Although there are many ways to define it, the most common definition is that it is the opposite of traditional data. It’s the process of taking data that hasn’t been used often and generating actionable insights.
Satellite imagery is a great example of alternative data. Satellite imagery is a great example of alternative data.
Research has shown that investors can deduce the value fluctuations of market participants and retailers from satellite imagery before anyone else.
Alternative data may be useful in such situations to help you make better investment decisions.
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It takes effort to collect external data. This is in contrast to internal data which is mostly a result of business processes. This data can only be obtained by creating an in-house team to collect it or by sourcing it through third-party suppliers.
Before any web scraping, automated data collection, or web scraping can begin, there are three things you need to decide: what type of data, how it will work, and where it will go.
In data warehouses, all business data should be stored. This applies only to data that isn’t being used daily. External data can be used to power day-to-day operations as well as for longer-term goals.
The data might not end up in a warehouse if it is being used for daily operations such as dynamic pricing. Long-term storage is unlikely to be an option in these cases as dynamic pricing involves complex webs of APIs, mathematical comparisons, and computations.
However, advanced external or alternative data cannot be understood unless stored and analyzed with additional information. These cases require more planning and are more complicated.
First, All data should be collected for a specific purpose. This purpose is usually to support or debunk a hypothesis. Similar to satellite imagery, data from such images should be kept longer-term and manually analyzed. It should be assigned a subject and an expectation.
Second, It is important to understand that alternate data may not prove useful in certain cases. Alternative data may not be useful in some cases, for example, because it is often data that has been hypothesized to offer insights into a particular phenomenon, but has not been thoroughly tested.
Third, data collection processes need more support and maintenance than traditional ones. Advanced external and alternative data collection will not be possible if the company does not have an analyst or extraction team.
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Support structures are required to make advanced external or alternative data useable. They can be quite simple in some cases if the data was acquired from a third-party vendor. Only a data analyst team will be needed and some governance practices. However, data quality vetting, and other processes, will still be necessary.
It’s even more difficult if no data vendor can provide the information required or if an in-house team must be formed for any other reason.
Because I trust my colleagues in technical development, I won’t get into the details. For most businesses, it is easier to find a vendor who offers scraping solutions.
A dedicated data team will be required to manage the flow of information, particularly if it comes from multiple sources. Before data can be transferred to a warehouse, there are three critical steps: normalization, cleansing, and assurance.
Data automatically will not be unified. There may be corruption or simply inaccuracy.
Data cleansing is therefore necessary. Before data can be moved, it must be cleansed. This involves fixing formats and naming conventions as well as other structural aspects.
Quality assurance is essential before data can be moved. Quality assurance is even more critical in cases where data has been purchased from a vendor.
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Things get even more complicated when external data is involved in the pipeline.
Automated data collection can increase costs as it requires technical expertise, analytical capabilities, or both. It is important to plan for the integration of external data into business processes.
External data can bring enormous benefits and create entirely new growth opportunities.
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