Choosing The Right Data Projects
Many companies struggle to go beyond simple reporting and generate real, actionable value. So how can you ensure that your data initiatives make a tangible impact? How do you involve business stakeholders in this journey, excite them about data’s potential, and set realistic expectations regarding outcomes and timelines? More importantly, which data projects should you pursue to maximise business value? Here’s a step-by-step guide to achieving meaningful data transformation in your business.
Step 1: Understanding the "Why" of Data
Before embarking on a data initiative, you must clearly define why your company needs data. While most businesses will say it is to make better decisions, data can provide five distinct advantages:
1. Holistic View
Integrating data from various systems, channels, and tools creates a comprehensive picture of your business operations. This clarity allows for better decision-making across departments such as finance, marketing, procurement, and operations.
2. Agility
Recognising early signs of change enables proactive responses. Initially, data initiatives focus on reacting faster to change, but over time, they evolve toward anticipating and predicting trends.
3. Customer Closeness
Data insights reveal customer demographics, preferred channels, lifetime value potential, and risk of churn. Understanding these factors allows businesses to enhance customer experience and retention.
4. Streamlining & Cost Reduction
Data helps identify inefficiencies, automate processes, and allocate resources more effectively. This leads to smarter spending and improved ROI.
5. Automation & AI
Data-powered automation simplifies decision-making, whether through AI-driven recommendations or automated workflows.
Actionable Step:
A common problem is that different stakeholders have different priorities and often whoever shouts loudest gets the most attention. A practical exercise is to have stakeholders distribute a total of 100% across these five benefits based on their priorities, and then add them up amongst the group. This later allows you match your data ideas to the priorities of the business, and ensures that you don’t get tied to one particular benefit. (For example having to find a cost reduction with every initiative.)
Example:
Step 2: Background Knowledge
The best data initiatives are those that are embraced across the organisation, not just mandated from the top. Idea generation should involve:
We have found that in order for people to embrace change they need to feel ownership of that change. If data is just a mandate from management, it won’t get the buy-in it needs to be successful. Encouraging input from all levels fosters a sense of ownership, which is key for adoption. However, for meaningful contributions, stakeholders should have a basic understanding of data pipelines and their potential applications.
Useful knowledge to frame your discussions:
. How does a data pipeline work?
. What are the different stages/tools?
. Some basics around cost-implications - ie Stakeholders often want 'real-time' data but may not actually need to leverage that data at such a frequency.
. An idea of typical timeframes for projects - or at least which aspects take longer and which are easier.
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A little bit of knowledge can go a long way to align stakeholder expectations and later in democratising data.
Step 3: Idea Generation
With a strong understanding of what is important to the business and how data actually works, you can begin formulating ideas! There are no ‘wrong’ answers at this stage and it is important to encourage contribution. The point of the exercise is to get people excited about data, map out different areas/ processes where it might be useful & uncover areas for integration. Try and foster conversations around data touchpoints, sources and where data can be activated.
A broad ideation phase can be daunting for data teams, as not every idea will be feasible. This is an opportunity to educate stakeholders about what is possible and guide discussions toward realistic and impactful initiatives.
Step 3: Establishing Priorities for Further Review
Once ideas have been generated, they must be rigorously analysed for feasibility and impact. But before you go into each and every idea, you should first:
1. Refine the Idea
A strong data initiative should focus on driving decisions and actions rather than just identifying problems. For instance, predicting customer churn is valuable, but it must be accompanied by clear interventions, such as targeted retention campaigns. How does the data ladder up to a decision?
2. Popularity Among Teams
While some ideas may be more complex, those that generate excitement among employees can improve adoption and long-term success. While these may be bigger ideas for tomorrow, you can start working towards them today and creating data products that teams will enjoy using should always be a priority.
3. Alignment with Business Priorities
Refer back to the business priorities exercise. For example, if customer closeness is the main goal, then customer analytics projects should take precedence.
4. Business Impact and ROI
Early-stage assessments can help estimate potential returns and set KPIs to measure success.
5. Implementation Complexity
Certain initiatives may require foundational work before they can be executed. Understanding dependencies helps in structuring a realistic roadmap.
After an initial review, you will of course need to conduct a deeper feasibility review, but a top-level look is useful so as to provide feedback from the session quickly.
Step 4: Keeping Stakeholders Engaged
The final, and often overlooked, step is communicating back to participants. It is essential to provide feedback on which ideas will move forward and why. There is nothing more disheartening than coming up with an exciting idea and then never hearing anything about it again, but of course not all ideas are actionable.
Conclusion
By following this structured approach, your organisation can:
If you’re unsure which data ideas will bring long-term value, how long they would take to implement, or the costs involved, consider booking a consultation with the 173tech team today: https://173tech.com/data-workshops/