Data has become an essential commodity for businesses and organizations in today's world. Companies rely on external data acquisition to gain insights, make informed decisions, and stay ahead of their competition. However, the costs of acquiring external data can quickly add up without you even noticing. In this article, we will explore why you should care about external data acquisition costs, what factors contribute to them, and how to reduce them effectively.
Why care about external data acquisition costs?
External data acquisition costs can significantly impact a company's budget, especially for small and medium-sized businesses. High costs can reduce the amount of data available for analysis and decision-making, limiting a company's ability to stay competitive. By understanding and managing these costs, companies can make better use of their resources and optimize their data acquisition strategy.
What goes into data acquisition costs?
There are several processes involved in acquiring external data, and each one adds to the overall costs. It's important to review each step involved in data acquisition to fully understand how they contribute.
Data sourcing
One of the main factors affecting external data acquisition costs is the process of sourcing the data. This includes identifying relevant data sources, negotiating contracts, and paying for access to the data. This process can take significant time, oftentimes numerous months, Depending on the industry and data type, the costs for acquiring data can vary significantly.
Data quality and cleansing
Data quality is a critical aspect of data acquisition. Ensuring the data collected is accurate, up-to-date, and reliable requires significant effort, including data cleansing and validation. This process can be time-consuming and expensive, especially when dealing with large volumes of data or complex datasets.
Data integration
Once external data is sourced and cleaned, it must be transformed into a format compatible with existing internal data systems. Data may need to be further combined, enriched, or aggregated with other data sources to be useful. Integrating data typically requires significant resources, both in terms of time and money, as companies often need to invest in custom development and ongoing support.
Legal and compliance issues
Acquiring external data involves navigating various legal and compliance issues. These may include data privacy regulations, data usage restrictions, and intellectual property rights. Ensuring compliance with these regulations can be complex and costly, but failure to do so can result in significant penalties and reputational damage.
How to evaluate data acquisition costs
At the end of the day, all the factors contributing to the cost of data acquisition can be organized into three main buckets: money, time, and risk. By examining each, decision-makers can better understand the full scope of costs associated with a data project.
Money
Money is probably the first and most obvious aspect to consider when evaluating data acquisition costs. And certainly, a lot of costs can be saved by finding ways to reduce the amount of money spent on data.
One of the main reasons why external data acquisition costs can be high is due to the fact that the process often involves a significant amount of overlapping or duplicated efforts. This can occur when different departments or teams within a company are each pursuing their own data acquisition efforts, resulting in duplicate costs and inefficiencies. Moreover, it can be challenging to determine whether you are getting the best price for the data, as it may be hard to compare prices across different data providers or identify the ideal data sources to meet your specific needs.
To reduce data acquisition costs, companies should centralize their data acquisition processes. By doing so, organizations can streamline their processes and reduce the resources required for data sourcing, cleansing, and integration.
Time
Researching and locating data, negotiating contracts, building infrastructure, and processing large, unstructured data sets are all time-consuming tasks that increase the cost of acquiring external data. These tasks extend project timelines and divert expensive FTEs, such as engineers, data scientists, and lawyers, away from other value-generating activities.
In order to minimize costs and maximize efficiency, organizations should look for ways to reduce the amount of time these processes require:
- To minimize the time associated with sourcing data, companies can make use of data marketplaces, where they can quickly identify relevant data sources.
- Negotiating contracts can be made more efficient by standardizing contract terms and conditions.
- Building infrastructure connections can be streamlined by using existing data platforms or APIs, rather than building custom integrations from scratch.
- Processing large datasets can be accelerated through the use of machine learning algorithms and other advanced data processing techniques.
By reducing the time involved in these processes, organizations can allocate their resources more efficiently and generate value more quickly.
Risk
Data governance is a critical component of any data strategy. Rules and regulations regarding data use and privacy are continuously evolving, and any data strategy that is not diligent in its approach to data governance risks exposure to substantial future costs through fines, legal challenges, and loss of customer trust or goodwill.
To minimize risk and uncertainty in your data strategy, businesses should prioritize controls, security, and compliance. Establishing control over data management and ensuring security at both the data and organizational levels can reduce the need for expensive corrective actions down the line. Maintaining compliance and good data governance are key to staying ahead of changes that could have a financial impact on your organization.
Strategies for reducing data acquisition costs
Reducing external data acquisition costs can help businesses maximize their return on investment and stay competitive. Here are some ways to reduce external data acquisition costs:
Form data partnerships and collaborations
Forming partnerships or collaborations with other companies can be a cost-effective way to acquire external data. By sharing data resources, both parties can access valuable insights without the need to invest in acquiring the data independently.
Leverage open data initiatives
Open data sources, such as government or public databases, can be a cost-effective way to acquire valuable information. The data they provide is typically free, and acquiring it rarely requires negotiations or contracts. However, open data sources may not always be relevant or comprehensive enough for a company's needs, and the quality of the data may vary. Additionally, open data sources may not be updated frequently, which can limit the usefulness of the information. Companies should carefully consider the benefits and limitations of open data sources before relying on them as a primary source of external data.
Centralize data acquisition
Centralizing data acquisition is another effective strategy for reducing external data acquisition costs. When data acquisition efforts are decentralized, there is often a duplication of efforts and a lack of coordination, which can result in unnecessary expenses. By centralizing data acquisition, organizations can streamline their data acquisition processes and reduce the resources needed for data sourcing, quality and cleansing, integration and storage, and legal and compliance issues. Centralization can also improve data quality and timeliness by ensuring that data is collected, processed, and managed consistently across the organization. This ultimately leads to cost savings and improved efficiency for the organization.
Utilize a data collaboration platform
Using a data collaboration platform can be an effective strategy for reducing external data acquisition costs. One of the most significant benefits is the cost savings it can provide. By centralizing data acquisition efforts and streamlining data processes, organizations can improve efficiency and reduce the resources needed for data sourcing, quality and cleansing, integration and storage, and legal and compliance issues. By providing a single source of truth for data, a data collaboration platform can also help ensure duplicate data is not being purchased. This can result in lower costs and more accurate, reliable data.
In addition to reducing costs, a data collaboration platform can also help reduce the amount of time required for data acquisition. By automating data cleansing and integration processes, a data collaboration platform can significantly reduce the amount of time required to complete these tasks. This can also help eliminate the need for expensive FTEs, such as engineers, data scientists, and lawyers, to perform these tasks, freeing up these resources for other value-generating activities.
Finally, a data collaboration platform can help reduce risk by ensuring that data is collected, processed, and managed consistently across the organization. This can help minimize the risk of data breaches, regulatory violations, and other legal and compliance issues, reducing the need for expensive corrective actions down the line.
Conclusion
External data acquisition is essential for informed decision-making, gaining a competitive advantage, and enhancing product development. However, the costs associated with data sourcing, quality and cleansing, integration and storage, and legal and compliance issues can be significant. To reduce these costs, businesses can consider strategies such as data partnerships and collaborations, open data initiatives, centralizing data acquisition, and utilizing data collaboration platforms. By implementing these strategies, companies can reduce their data acquisition costs while still benefiting from valuable external data insights.