Do you have a data-driven product development mindset vs. a project mindset?
In today’s post, we’ll be uncovering why this is crucial for your business success and how you can try this new approach.
“It’s time we migrate to the cloud.”
What happens next? In our experience, many organizations typically kick off a cloud migration using a project mindset approach. A project plan is put together, usually with a defined start and a defined end. Success is typically defined by completing the project within scope, budget and on time.
The downside? This type of project methodology focuses on completing tasks – rather than focusing on outcomes. When migrating your data platform to the cloud, you should be evaluating the impact to the business as a whole, or better understanding how this data migration would align to business objectives. Think of it as solving the symptoms of pain, but not the root issue.
That’s why we approach data projects a little differently. In this article, we’ll be uncovering the differences between having a project mindset vs. a product mindset.Do you approach your data and analytics project with a data-driven product development mindset vs. a project mindset? #datadriven Click To Tweet
Project Methodologies: The Way Data and Analytics Projects are Undertaken
Most data and analytics projects follow a project methodology. This linear, waterfall approach means that you have a linear and sequential list of actions to complete during each phase of a project. When you complete one, only then do you move on to the next.
This type of waterfall approach prioritizes completing your project in stages:
- Once you complete the conception and ideation phase, then you can move on to analysis
- When you complete the analysis, you can design, test, and implement
This provides stakeholders with a clear, linear framework (everyone knows what has to happen before the project can continue).
The Cons of this approach: It can delay progress, limit collaboration, and not account for new things learned throughout the project.
The waterfall approach for project methodologies is less adaptable to change.
When you’re applying this type of methodology to delivering on a data project, you’re focused on delivering exactly what’s being asked.
Someone may give you a set of requirements – I want a dashboard with these three metrics on it – you build it, deliver to the client, and move on.
You don’t work together to understand why those metrics are essential and what they want to solve.
When organizations prescribe to a project methodology, (also known as a waterfall approach), then they follow this same format when embarking on a data and analytics project, too.
Instead, we work closely with our clients to truly dive deep into understanding their business needs and then apply that to their data and analytics. We think of it as approaching a data project like data driven product development, rather than a project, incorporating agile methodologies instead of the waterfall approach.
That’s where our Data Team as a Service approach differs from others and what our clients have said makes us a key differentiator when working with us versus a consultant.Currently Reading: How To Have a Data-Driven Product Development Mindset #datadriven #digitaltransformation Click To Tweet
Having a Data-Driven Product Development Mindset: The Agile Methodology
Let’s discuss approaching your data and analytics project like a PRODUCT with the Agile Methodology.
Let’s take that same example I mentioned above. Your client says “I want a dashboard that has these three metrics on it.”
Before we build or deliver, we take a step back and ask questions to figure out why this is important to our client.
- Why these three metrics?
- What’s the bigger story they’re trying to tell?
We peel away all the layers until we get to the heart of the reason.
Ultimately, we help them answer this question:
Why is that report or that insight important and valuable to the company?
Once we work together to understand these questions, we then design how we will show it to the client and get them the information they need.
The result: A much more robust and helpful data-driven product that truly aligns with business objectives rather than a one-off report.
By spending this time up-front to get at the root issue, we save a significant amount of time down the line, which helps keep your projects on time and on budget.
It also means that down the line, we know why we’re building what we’re building and that we’re constantly incorporating the business objectives into it.
We like to approach your data and analytics project with a product mindset vs. a project mindset.
A product management lifecycle is not a linear set of steps we follow to solve a problem, but a cyclical lifecycle going from:
- Ideation to conception to MVP to testing and feedback and more
That way, we stay constantly plugged into what’s important to the business and continuously add value.
This approach is also a more agile approach where we work together to solve problems and constantly use our new findings to re-evaluate and make adjustments as needed.
Considering technology is changing so fast, it’s the only way to keep up-to-date because if you plan for something five years from now, chances are technology will have changed by the time we get there.
This type of methodology also means we don’t just provide one action to our clients; we help our clients build a data-driven culture that they’re engaged in themselves.
They’re part of the process rather than the recipients of a report, and that makes them more successful, too.
A New Approach for your Data and Analytics Projects: Product Methodology
This new approach for your data and analytics projects does more than help you deliver on one ask.
It enables you to foster and build a data-driven culture.
It makes digital transformations in sync with business objectives.
The biggest challenge for companies is not the technology behind digital transformation but the people behind your data.
In fact, a recent NewVantage Partners survey found that 48% of executives said that “people challenges” were the biggest barrier to being more data-driven compared to just 19% who said it was technology.
To overcome that, it’s time to incorporate the right people into your next data and analytics projects and to start by asking the right questions.
Only then will data and analytics projects be seen as ones that have continuous business value.
Have you Downloaded our Data Platform Migration Guide?
Find out the pitfalls to avoid and a success story from our experience working with the Hudson’s Bay Company.
Data-Driven Product Development Related Resources:
- Roadblocks to Building a Data-Driven Culture (+ Solutions)
- Culture, Not Technology Makes You a Data-Driven Company
- How a “Data Team as a Service” Model Works
- 5 Challenges Leaders Face When Launching Data & Analytics Projects
- Learn more about our Data Platform Migration Guide