In today's data-driven business landscape, data projects are becoming increasingly important. But with the increasing amount of data also comes a higher risk of data breaches, data loss, and other data-related threats. It is up to project managers to mitigate risks in data projects and ensure their success.

Here are some steps and best practices you can take to mitigate risks in your company's data projects.

Conduct a risk assessment to identify risks

The first step in mitigating risks in data projects is conducting a risk assessment to identify potential data-related issues that could arise.

Start by determining the scope of your data project, including the specific objectives, processes, systems, and data involved. Once you’ve done that, brainstorm and identify potential risks that could affect your data project. Consider various categories, such as data security, data quality, compliance, project management, and resource constraints.

Some of the most common risks in data projects include data quality issues, data corruption, data loss, security breaches, failure to comply with data protection regulations, insufficient backup plans, and inadequate project planning. Third-party vendors and technology used in the data project could also pose various risks, so make sure to assess those, too. Document all in a risk register, prioritize the risks, and define measures to mitigate them before they become a roadblock in your data project. 

Analyze and evaluate your data projects risks

Once the risks are identified, the next step in mitigating risks in your data projects is to analyze them, assess the likelihood of each risk occurring and the impact it could have on the project's timeline, scope, goals, performance, and budget. Once the analysis is complete, you and your project team can develop various risk management plans.

To evaluate your data projects risks, use a scale (e.g., low, medium, high) or numerical values (e.g., 1 to 5) to represent likelihood and impact. For example:

1. Likelihood: Low (1) – There is a remote chance of the risk occurring.
2. Impact: High (5) – The risk would have a significant impact on the project if it were to occur.

Multiply the likelihood and impact values to determine the risk severity score. For example, if a risk has a likelihood of 3 and an impact of 4, the severity score would be 12 (3 x 4).

Develop a risk management plan

Your risk management plan should include project management strategies for mitigating, escaping, and accepting risks. The plan should also outline the steps the project team will take for each identified risk. For example, the risk of data loss can be mitigated by establishing a plan of regular data backups and data recovery procedures. Data security breaches, on the other hand, can be mitigated by implementing strong passwords, encryption, firewalls, and access controls. 

Keep your stakeholders in the loop

Stakeholders are the support system of your project (and company) and are directly impacted by the project's progress, performance, and risks. Communicating with them regularly to update them of potential data risks and your project manager's data mitigating strategies is of utmost importance. By keeping stakeholders informed, you're keeping everyone on the same page and ensuring that the data project is moving forward smoothly – one of the top project management goals you should strive to achieve. 

Monitor your data projects risks in real time

You must continuously monitor data risk throughout the project's lifecycle. Real-time data monitoring can be accomplished by using analytics software. It helps identify the occurrence of new risks, evaluate the effectiveness of the risk management plan, and take action to mitigate or promptly eliminate risk. 

Test and validate your data

Quality data is essential for data projects. Before you start deploying a data project, make sure that the data to be used is: 

• Accurate: the data should reflect the actual state of what it represents.
• Relevant: the data should meet the requirements for the intended use.
• Complete: all required data elements should be present and correctly recorded in a data set, with no missing values or data records.
• Timeless: means it should be up to date. 
• Consistent: the same data should have the same meaning and value wherever it is used, accessed, or cross-referenced with the same result. 

By ensuring data quality, you can mitigate the risk of errors in your project. 

Establish a plan of action in case of a risk event

No matter how many measures you take to mitigate risks in your data projects, you are never guaranteed that a problem will never occur. For that reason, it’s crucial to be prepared with an action plan in the event of a risky event. Such a plan should outline the specific actions to be taken in case the initial response strategy does not work, identify potential solutions, and determine the best course of action. Every data project is different, but a contingency plan for identifying alternative resources, changing project scope, or adjusting timelines is vital.

By following these data project risk mitigation strategies, you can deliver data projects on time and with minimal risk. Because your responsibilities may include more than just data projects, we recommend investing in an online solution that can help you manage and track resources, establish budgets and execution, as well as monitor your data projects from start to finish, in-context, and in real-time, all from one application.  

About the Author:  Casey McGuigan holds a BA in mathematics and an MBA, bringing a data analytics and business perspective to Infragistics over the past decade. Casey is the Product Manager for the Slingshot digital workplace platform and the Reveal embedded analytics software. She is instrumental in Infragistics product development, market analysis and product go-to-market strategy.