Overcollection of data can lead to wasted resources
Collecting more data does not necessarily lead to more useful information or better decision-making. In fact, unrestrained data collection could lead to wasted team hours, inaccurate analytics and reports, and unreliable decisions.
The Risks of Too Much Data
How overcollection of data impacts organisations
We know good data quality leads to better decision-making. To further reinforce this point, a Gartner study has shown poor data quality could mean an annual average loss of $12.9 million for your organisation.
As a fundamental rule if you accumulate too much data then you run the risk of collecting irrelevant, unusable, surplus data.
Excess data not only clutters the information pool; it also creates ‘noise’ rather than valuable insights. This ‘noise’ could lead to valuable data being overlooked.
Silos, in particular, could lead to additional wasted resources. For example, people from one department will waste valuable hours to recollect, repurchase and reanalyse data that already exists within the organisation if they don’t know that the data resides in a different department.
There’s still one crucial concern we haven’t discussed; privacy in relation to data overcollection.
The Risks of Too Much Data
How overcollection of data impacts organisations
We know good data quality leads to better decision-making. To further reinforce this point, a Gartner study has shown poor data quality could mean an annual average loss of $12.9 million for your organisation.
As a fundamental rule if you accumulate too much data then you run the risk of collecting irrelevant, unusable, surplus data.
Excess data not only clutters the information pool; it also creates ‘noise’ rather than valuable insights. This ‘noise’ could lead to valuable data being overlooked.
Silos, in particular, could lead to additional wasted resources. For example, people from one department will waste valuable hours to recollect, repurchase and reanalyse data that already exists within the organisation if they don’t know that the data resides in a different department.
There’s still one crucial concern we haven’t discussed; privacy in relation to data overcollection.
Why Does Data Overcollection Happen?
Organisational features that generate unwanted data
Overcollection of data is often related to the root causes of poor data quality.
Advancements in technology have led to the creation of numerous data-gathering channels. However, this strategy will only lead to data overcollection if the different sources are not integrated into a central system where duplicate or irrelevant data can easily be identified.
Furthermore, having too many channels to collect data can impose additional layers of complexity and challenges. Which channel should you use to get the most accurate data? Where is the most relevant information?
Siloed systems and departments often collect their own data sets, which could already exist within other departments or systems, albeit in a different format. This duplication in data across an organisation creates uncertainty in its quality. Especially if there’s a question over which version is the most accurate. Having irrelevant or duplicate data could lead to inaccurate reporting and, ultimately, an incorrect decision.
Using different processes to collect data can result in data waste – incomplete, outdated and irrelevant to its purpose.
For example, do you collect data manually, or do you have an automated process in place? Manual data collection can be prone to human error, particularly when done at scale, whilst an automated process can run the risk of collecting duplicate data. Which one do you need to implement? How can you manage it effectively?
Data governance is the process of managing data. A crucial part of data governance is data ownership and accountability. Poor governance can lead to silos, duplication of data and a lack of process to acquire new data sets
For example, we’ve seen organisations with multiple people across different areas paying for the same external data source.
Less Data Is More
How to prevent overcollection of data
Having a data-first objective is a great start. After all, it’s the foundation of effective systems, accurate forecasting and reporting, and reliable insights.
To gather data effectively, here are some key processes you may wish to consider:
- Understand exactly what it is you need to achieve with your data and how its use supports organisational objectives.
- Develop a comprehensive data strategy to reflect organisational objectives. Ensure your data strategy includes key components such as vision, governance, architecture, master data and culture, among others so that you reduce the likelihood of collecting or acquiring unnecessary data.
- Create a data governance team. The people who will oversee ongoing data governance should also be integral to the strategy development. They’ll help eliminate redundancy, ensure that data is leveraged and accessible across necessary platforms, and avoid double-up within organisational silos. They can also oversee privacy, protection and continual maintenance.
- Define your data architecture. This includes documenting that data that you have, where it exists, its purpose, who owns it, agreed source of truth, and other key components. Providing youwith a clear picture of what you have. You can then start to work out what it is you actually need and the path forward for it.
In our next article, we’ll do a deep dive into best practices around Data Collection Methods.
Meanwhile, if you’d like to discuss your specific data collection processes, contact a member of our Data Agility team today.
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