Business and technology leaders are expected to deliver data and analytics initiatives that move the business forward and provide measurable business outcomes and value. But D&A initiatives are notoriously challenging, and the vast majority fail to deliver the value promised.
Gartner articulates the challenges perfectly in Top Priorities for IT: Leadership Vision for 2021:
… [C]hronic issues have become more acute. Siloed data, lack of trust, misalignment to outcomes, a focus on data for its own sake and an assumption that a technology acquisition will be sufficient can be colossal challenges to overcome because there is no quick fix.
Forward-thinking, future-oriented data leaders are looking for a new way to deliver business value. But before you can move forward, it’s important that you identify which stage of data delivery your organization currently falls into.
In this piece, we’ll examine the three stages of data delivery that every organization goes through, and guide you to the most mature stage of data delivery, based on product management principles.
Stage 1: Transactional or request-based
The first stage of data delivery is transactional or request-based.
Most organizations start here. Typically, at this stage, one individual or sometimes a small team of IT professionals is responsible for delivering reports on a recurring or ad hoc basis. Business leaders make a verbal or written request, and IT delivers the report.
In some organizations, IT even has a centralized ticketing system in place where stakeholders can request analytics in a more formalized process.
Data professionals know this stage well. And you know just how frustrating it can be.
Because, under the hood, it’s never as simple as submitting a request for say, a 2021 Black Friday sales forecast based on the last two years.
The challenges inherent in request-based data delivery are often hidden from executives who only see the end result. They don’t have visibility into the multiple email chains necessary just to determine where the correct data resides; the data pull from three different systems, followed by data quality checks; and the one-time manual updates and data set linking that goes on in the background.
This transactional reporting is inefficient, not scalable, and worst of all wastes your data talent on repetitive, low-value work.
Stage 2: Waterfall and Agile project management
The next stage of data delivery is project-based. This data delivery model is more organized and efficient than the first. In larger enterprises that have moved beyond a request-based approach, this is how most traditional data and analytics efforts are delivered.
There are two main project management approaches:
Waterfall project management
First introduced in the 1970s, the waterfall approach to project management prioritizes completing your project in stages with a linear, step-by-step approach.
It’s not surprising to discover that waterfall originated in the manufacturing and construction industries, as they require a highly structured production process.
Waterfall then became popular in the product development and data worlds for years because it provided stakeholders with a clear, linear framework and adhered to strict deadlines.
The challenges of waterfall is that it can delay progress, limit collaboration and innovation, and doesn’t account for new learnings gained throughout the project.
Agile project management
Developed by software engineers who wanted a more flexible framework for designing software, agile was introduced about 20 years ago as an alternative to waterfall.
Agile embraces four key principles:
- People over process
- Working products over documentation
- Collaboration over requirements
- Change over strict plans
Agile uses short work cycles (known as “sprints”) that allow for rapid production and iterative improvement.
How project management data delivery works
During this stage, organizations will typically allot a portion of IT’s budget towards a data modernization project that aims to take some of the pressure off of IT by centralizing data and creating self-serve reports that can be accessed by business leaders across the organization. Leveraging classic project management techniques, the project owner compiles the requirements (often without consulting the lines of business) and scopes out the project plan while IT sources the tech. They may even bring in a consultancy to assist for the duration of the project.
The project team stands up a data warehouse, creates a few standard reports for finance and marketing… and that’s it. Project over.
While a project-based data delivery model is far more efficient than a request-based model, it’s also a short-term, band-aid solution that doesn’t solve the long-term challenges facing businesses.
As business needs change, old reports become obsolete. New data and analytics needs arise, so IT and data teams are once again forced to build ad hoc reports.
“Data is not a project, and people tend to forget that,” said David Dadoun, Head of Data at Bombardier Recreational Products. David joined host Christina O’Reilly on the Data-Driven Leaders podcast to discuss his approach to data initiatives:
It’s like, ‘Oh, we’re doing a project for data. We’ll be done in three years,’ he continued. “And then what? We’re not finished. There’s always going to be a new data source. There’s always going to be a new algorithm. That’s just the reality of data. If you think that you can go at it in a pure waterfall approach and have an end date, then you’re missing the boat entirely.
Additionally, there is the creeping risk of shadow IT as departments purchase non-vetted software that provides them with some of the insights they are looking for.
Luckily, there is a better way.
This leads us to our final stage of data delivery maturity…
Stage 3: Product management and design thinking
It’s time for a change.
A new approach has emerged. Companies are starting to approach the analytical needs of the business using a product management framework. Product management takes the best parts of agile — the focus on collaboration, continuous improvement, and people over process — and takes it to the next level.
Instead of gathering rigid requirements and focusing on milestone deliverables, product management empathizes with the end-user and strives to define their challenges. It uses a technique called design thinking—used by SaaS and B2C companies to build successful products for years now—to understand the end-users needs’, define the challenge they’re facing, and iteratively build a solution that solves their challenge.
The end-users in our scenario are the business leaders who use data and analytics to make strategic decisions. With a product management approach, these end-users are involved in product development throughout the entire process. This ensures that solutions are tailored to their changing needs, deliver measurable business value, and solve the problem of low user adoption.
The product management approach to data delivery is an iterative, long-term engagement that produces value over time as it stays in lockstep with the needs of the business user.
So, what’s happening under the hood now when an executive opens their dashboard and reviews a report?
It’s simple, clean, and automated. No more email chains, manual data quality checks, and meetings to determine where the right data lives.
Instead, automated systems refresh data nightly and perform linking and data quality checks. Reports are automatically updated with new data before the start of the business day.
With a product management approach to data delivery, business leaders can be successful in their role because they have access to the data-driven insights they need to make better business decisions.
Data leaders across industries are starting to embrace product management.
“Design thinking and [agile] are really taking off,” said Rupinder Dhillon, Head of Enterprise Data Office at Sobeys on the Data-Driven Leaders podcast. She continued:
I think it’s because it allows for people to put themselves in other people’s shoes and really put the focus of what you’re building, on what is the outcome you’re trying to drive, and what’s the experience of the person that’s going to have to use it and trying to bring those two worlds together.
IT can focus on high-value, skills-based work instead of generating time-consuming one-off reports.
And CDOs and other data leaders have the data tools to take the business to the next level, in an organization where data and analytics are viewed as a key component of business success.
Use a product management approach to deliver business value
Are you prepared to challenge the status quo and transform your data into meaningful insights?
With a product management approach, you can bring your business to the next level and start delivering data that is aligned with business goals and outcomes.
Are you prepared to shift the mindset at your organization?