Data Projects: Feasibility Review & Calculating ROI
You’ve engaged your stakeholders, gathered ideas, handpicked the most relevant ones and now comes the feasibility review which is crucial in ensuring that all data requirements can be delivered. So how should you go about it?
Data Availability And Quality
The first step in many of our projects is a Data Dictionary. A data dictionary captures and defines KPIs across all business functions. It outlines the business and technical definitions for each metric along with other useful information. This should provide the blueprint as to which sources of data are needed for the data initiative, how complete that data is, the accuracy and consistency of the data, and any timing issues in terms of data availability.
Technical Feasibility
Evaluate the idea vs your existing technology stack. If you are implementing something new, or where you haven’t used that particular tool for that particular use-case, it’s often sensible to look at forums, FAQs and product guides to identify any problems early on. If possible, you might want to spend some time ‘playing’ with the tool and see how easy it will be. Tool providers are often guilty of promising the earth, so be sure to do your own research! There is nothing worse than being sold that something is possible and then later finding out the desired outcome is very difficult to achieve. Another important aspect to think about when looking at technical feasibility is scalability. Data volumes are only going to grow- can your setup handle it? What are the potential cost implications? How easy/difficult is it to potentially migrate away?
Resource Assessment
Who is going to work on this project and how long will it take? Do you have the technical expertise in house? What training or hiring might be necessary? What other priorities does your analytics team have? When it comes to failure points, we often find that shifting priorities mean that data projects get delayed or abandoned altogether. It can be very difficult to assess how long a project might take if it’s a new area, but we always advise on breaking it up into smaller chunks, ensuring that there is a clear deliverable at the end of each chunk of work. While another priority might come along, this at least gives you a natural point to restart.
Regulatory And Compliance Considerations
Depending on your industry, there might be specific regulation and standards in place that you need to adhere to and may affect your way of working. As well as ensuring that the project adheres to relevant data protection and privacy regulations, you should also consider any wider ethical issues. Many companies try and get as much information as possible on their customers and sometimes use blurry lines or fuzzy logic to justify their actions. It is important to safeguard the company from any potential fines or damage to their reputation and doing this at the feasibility phase is important.
Risk Assessment
Outside of regulatory matters, consider any other risk factors involved and how they can be mitigated. This is especially important where you might be changing any tool infrastructure or core models. Map out all of the different elements, related models, dashboards etc that would be affected by the change, as this will help you to understand the true risk if something goes wrong. Identify key points of failure within your pipeline and be especially careful when making changes to these! There is also often a risk in NOT doing things, especially when a data initiative centres around updating or migrating data.
Cost-Benefit Analysis
This is a clear area in which your data team will need the input of Subject Matter Experts in order to understand the true impact to your business, but it is still difficult. Analytics typically underpins success and informs decision-making. So it is the end decision that gets all the headlines whilst the data can be forgotten about. Management often has an expectation that each and every data process or model needs to generate a significant return on investment in order for the expense to be justified, but it might take weeks or months to really see the impact in surfacing those insights. Benefits typically boil down to:
Cost-Savings: Reducing manual work, using data to find wastage, improved resource utilisation.
Revenue Generation: Increasing opportunities, conversion, sales, retention and lifetime value.
Time Savings: Efficiency gains, reducing manual work, faster processes.
We would typically advise that while it might be tempting to create big numbers on impact in order to sell a data project into your business, it is always better to err on the side of caution rather than promising something that isn’t delivered. While the benefits and ROI can often be fuzzy to understand, costs are normally straight forward:
Tools: Costs associated with hardware, software, licences etc. As we spoke about before, it is important to think about scalability when mapping out potential costs. Also consider maintenance and support costs.
People: The cost of people working on your data initiative, freelancers, agencies etc
Activation
How will end users adopt the data solution? What tools/apps/programmes are your team already using and can your data solution integrate? How likely is it that people will actually use the data dashboard? Your stakeholders might be asking for a dashboard, but if dashboard usage is low at the moment, is that the best way to bring the idea to life?
Your Feasibility Review should never just be about asking ‘is this possible?’ it should be about finding the best format to bring data to life. Activation is a key point of failure for many companies who underestimate the need for training, awareness and reinforcement.
Alternative Solutions
Last but not least, you should also consider what alternatives are available that might work to solve the same problem.
Conclusion
A Feasibility Review should flesh out your ideas and give stakeholders a solid understanding of which ones to take forward and why. Be sure to present this back to everyone involved in generating ideas, take them with you on the journey and help them understand why you have recommended a particular course of action. These recommendations can then form part of a wider data roadmap giving a clear overview to your organisation on what data has accomplished and how.
If you need help evaluating your data ideas, understanding which projects will bring the most value and what a realistic timeline may be, why not get in touch with the 173tech team today?