Transforming data into meaningful insights is critical for modern organizations.
You know that ensuring your business has access to fast, quality data is key to growing your business.
You also know that organizations that use data to make strategic business decisions outperform their peers in terms of revenue attainment (+85%) and gross margin (+25%).
But 70-80% of data initiatives fail. Of the $1.3T spent on data transformation in 2020, it’s estimated that $900B of that went to waste.
Why do so many data transformation projects fail? Why are these business-critical initiatives so risky?
Data transformations fail because the traditional approach to handling them is inherently flawed. When data and analytics initiatives are treated like one-off technology implementation projects instead of business-critical initiatives, they are doomed to fail before they’ve even begun.
They fail when they don’t provide tangible business value to the organization, and when end-users refuse to adopt the new technology.
As a technology leader whose job is to identify the strategic goals of the business, align your data initiatives to those goals, and mitigate risk wherever possible… what can you do?
What if we told you that there is a process that reduces risk and increases the chances that your data and analytics initiative is a success?
There is.
It’s called “design thinking” and it’s been used by consumer-focused SaaS and B2C companies for decades.
A new process for a new kind of data transformation project
Most data and analytics initiatives are approached from a project management mindset. This involves collecting requirements and creating a project plan. There is usually a defined start and end date. Success is typically defined by completing the project within scope, budget, and on time.
This type of project methodology focuses on completing tasks—rather than focusing on outcomes or the end-user.
That’s why we take a product management approach to data and analytics initiatives. This approach involves empathizing with the organizations’ strategic goals, providing tangible business outcomes, and delivering iteratively to drive value quickly.
None of this can be done successfully without “design thinking”—a process that emphasizes asking hard questions, testing assumptions, and collaborating with the end-user every step of the way. All things that SaaS companies and consumer brands do exceptionally well when building new, innovative, and ultimately successful products that users adopt with enthusiasm.
Design thinking is just one aspect of the product management approach (along with the Agile methodology) that reduces the risk of your data transformation project falling flat.
Remember the $900B that goes to waste every year when data initiatives fail? A large chunk of that is spent at the technical implementation stage.
The great thing about this process is that, when applied to expensive and risky data transformation projects, the really hard work is done before any code is written.
So, how does it work?
Data transformations fail because they are treated like technology projects instead of business initiatives
From the Interaction Design Foundation:
“Design Thinking is an iterative process in which we seek to understand the user, challenge assumptions, and redefine problems in an attempt to identify alternative strategies and solutions that might not be instantly apparent with our initial level of understanding… [it] provides a solution-based approach to solving problems.”
In short, design thinking begins with knowing that you don’t have all the answers. When done correctly, it ensures that you build a valuable and adoptable product that solves real business problems.
These are the six steps in this iterative, ongoing process:
- Empathize with your end-user. In this step, you learn everything you can about your end-user—what keeps them up at night, what gets them out of bed in the morning.
- Define the challenge. This step leverages your research to define the core problem—and likely challenges you and your customer’s assumptions along the way.
- Ideate and brainstorm a solution to your end-user’s challenge.
- Prototype the solution. Prototypes can range from a simple sketch on a napkin to a clickable, interactive model.
- Test and validate the solution with your end-user. This stage is key to user adoption and change management. The more interaction your end-user has with the solution at this stage, the more likely they are to adopt it in the next stage.
- Implement the solution.
Applying a product management mindset to data transformation projects
Our clients often come to us with a straightforward request: They need to migrate off their legacy data infrastructure onto a modern stack they’ve purchased.
Most consultancies would say “Easy, our people are very skilled at that. We rely on zero-copy replication, and we’ll work table by table towards a cutover. And it will take approximately nine months.”
We can almost guarantee that this outdated approach will put you in the 70-80% of companies who never complete their data transformation project, wasting valuable company time, money, and talent.
This project management approach puts your organization at too much risk.
Instead, we start by asking a series of “why” questions. Why do you need to move off your legacy solution? What is the business value of migrating to a modern cloud platform? What problems are we really solving?
And when we’ve finally drilled down to the real business problem—say, that data is so siloed and reporting so slow that sales leaders across the country don’t get access to their daily sales figures until well after 5:00 pm, and are consequently out of sync with the rest of the business, working on action items well into their evenings—that’s the power of design thinking in action.
It’s about making meaningful changes in people’s lives. After all, data is used by humans to make real-world decisions with real consequences for the business.
That’s why the first stages in the design thinking process are empathize and define. During the empathy stage, we seek to understand not just the business but the people behind the business—their pains and challenges and what they need to do their jobs successfully.
The end-user—the consumer of the final data— is involved every step of the way, validating assumptions and providing meaningful feedback.
Gensquared is transforming data and analytics initiatives with a product management approach
For over a decade now, Gensquared co-founders Patrick Siconolfi and Phil Chen have challenged how traditional data consulting is conducted.
From day one, our philosophy has always been to approach data and analytics initiatives as business projects first and technology projects second. In order to serve our customers real value and limit risk, we take the time to truly understand and align to their business needs and priorities first.
The results speak for themselves. What follows are just two of many success stories where product management and design thinking helped mitigate risk and drive successful outcomes.
With product management and design thinking as our guide, we completed the largest data migration in Canada to date with North America’s longest continually operating company HBC, reducing reporting time from 20 mins to less than a minute. Today, business leaders across HBC can make data-informed business decisions in a time-efficient manner.
And with the Property Management firm Cadillac Fairview, we built a centralized, enterprise data platform that reduced intensive data tasks, allowing more time to analyze the data rather than wrangling it. Over 300 end-users access this new self-serve BI platform to make smart, data-driven business decisions.
Wants results like this for your data transformation project?
Your next data and analytics initiative doesn’t have to be one of the 20-30% of projects that fail.
There is a better way. SaaS, B2C, and other product-centric companies have used the product management approach for years, and it works.
It’s time for data initiatives to catch up.
We can help.
We’ll show you how a modern approach to solving your data and analytics challenge is the key to success.