Data Sensitivity Analysis

What is Data Sensitivity Analysis?

Data Sensitivity analysis aims to understand the magnitude of the impact (risk or cost) that data or information (asset model inputs) have on their population of assets.

At Modla, we are experts in asset analytics and specialize in providing data sensitivity analysis to help businesses optimize their data collection initiatives. Data sensitivity analysis can be used to prioritize data transformation, inform business cases for data collection initiatives, justify which information or data is cost beneficial to collect from the field, and enable better data governance and change control of sensitive data.

The Why

Determining which data is most important for an organization can be a complex process and may require a thorough understanding of the organization's goals and objectives. However, there are some general considerations that can help guide this process:

1. Business Objectives: The data that is most important for an organization will typically be the data that is most closely tied to the organization's business objectives. For example, if an organization's primary objective is to increase output, then data related to asset reliability, condition, and performance would likely be considered more important.
2. Impact on Decision Making: Another way to determine which data is most important is to evaluate the impact it has on decision making. This may involve analyzing the role that different data plays in informing decisions, as well as assessing the potential consequences of making decisions without certain data.
3. Regulatory Requirements: Regulatory requirements may also play a role in determining which data is most important. Depending on the industry or geographic region, there may be specific data that is required for compliance with regulations, and failure to collect or report this data could result in penalties or fines.
4. Cost-Benefit Analysis: Finally, organizations may need to conduct a cost-benefit analysis to determine which data is most important in terms of the resources required to collect, process, and analyze it. This may involve weighing the potential benefits of certain data against the costs of collecting and managing it.

By carefully evaluating the organization's goals and objectives, their current asset base, as well as the potential impact, sensitivity, and cost of different data, organizations can prioritize the data that is most critical to their success.

Use Cases:

Prioritizing Data Transformation

Data sensitivity analysis can help businesses prioritize the order of data transformation activities. By understanding which data points or model inputs have the greatest impact on asset performance, cost and risk, businesses can prioritize their data transformation activities to ensure they are focusing on the inputs that have the most significant impact.

Informing Business Cases for Data Collection Initiatives

Data sensitivity analysis can inform business cases for data collection initiatives by providing valuable insights into the impact of various data points or model inputs. By understanding the sensitivity of different inputs, businesses can make informed decisions about how much the data is worth to them.

Justifying Which Information or Data Is Cost Beneficial to Collect from the Field

Data sensitivity analysis can also help businesses justify which information or data is cost beneficial to collect from the field. By understanding the sensitivity of different inputs, businesses can determine which data points are most critical to collect and which can be collected at a lower frequency or not at all, ultimately reducing costs without compromising future performance.

Enabling Better Data Governance and Change Control of Sensitive Data

Data sensitivity analysis can enable better data governance and change control of sensitive data by identifying which data points or model inputs are most sensitive. By understanding which inputs require the most attention, businesses can ensure that sensitive data is properly managed and controlled, minimizing the risk of data breaches or other security incidents.

Conclusion

At Modla, we understand the importance of data sensitivity analysis in optimizing how you manage your assets. By prioritizing data transformation, informing business cases for data collection initiatives, justifying which data is cost beneficial to collect from the field, and enabling better data governance and change control of sensitive data, businesses can make better data-driven decisions.

Contact us today to learn more about how we can help.

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