Ever wondered where to start when embarking on a new data science or analytics project?
It’s hard to know where you should start once you have determined that you need to kick off a new project.
Just thinking about all the tools and technologies you might need to employ can make your head spin.
What do you do first, what data will you need to access and what skill sets will you need for successful project delivery?
To help crystallise this, I’ve outlined the 10 key points which I believe are essential to the success of any data science or analytics project:
1. Understand the problem or opportunity you are solving for. Define the problem statement, align with business strategy and quantify the cost/benefit.
2. Consider the options you have. Understand what actions need to be taken and determine the impact of these actions on your P&L.
3. Begin with the end in mind and formulate the end state vision and road map. In other words, what does success look like and how will you get there?
4. Set the right expectations. It is imperative that senior management understand what data analytics can and cannot do and that they buy into this.
5. Check that the required data is available. Make sure it is accessible in the format and structure required to enable the analytics.
6. Understand the skill sets needed and ensure the project team is adequately resourced. Include business experts to incorporate business / industry knowledge. Where there are resource or capability gaps, get the help you need to plug these gaps ASAP. Do not wait until the project is on a critical path!
7. Put in place technology that provides the analytical capability required for current and future projects such as data manipulation, predictive modelling and data visualisation supported by a data infrastructure that enables fast, agile analytics.
8. Apply a project management system. Ensure that you have a methodical approach to make the process efficient and keep stakeholders informed.
9. Create an adoption plan. A successful analytics project ends with the business adopting the solution or applying the actions recommended by the analytics.
10. Build in a feedback loop mechanism to enable continuous learning and improvement. Where there are models involved, make sure there is ongoing model risk management and governance in place.
Undertaking these steps means you have made sure that:
If you want to know more about how to deliver a successful data science or analytics project or indeed have a project that you need support with, we’d be delighted to offer you a free Discovery consultation.
If you’re open to that, please click here to contact us and we’ll be in touch to schedule a call.
Founder and Managing Consultant Blue Label Consulting
Brendan launched Blue Label Consulting in 2011. With innovative use of Data through AI, ML and other quantitative methods, he delivers robust analytics and actionable insights to solve business problems.