Using data effectively has a tremendous impact on marketing and product management efforts. With the right data, organized and reported the right way, people are empowered to make better decisions confidently. Hidden connections and insights become clear. Transparency increases.
As the industry matures and the biggest companies develop astounding sophistication in how they apply data, many organizations know they could be doing more, but you already have to stay on top of:
- Complicated technology from different vendors
- Data collection systems implemented by different people, across a mix of teams and external agencies, over a significant period of time, often with little or no documentation
- Bringing together reporting from agencies that only covers certain campaigns and data from tools that only report parts of the whole picture
- Prioritizing and conducting analysis of key areas
- Training people to use data and tools
It’s easy to see that there’s a lot to get right, that most organizations will have things they’d like to improve, and many may not know where to start.
With over a decade of experience helping small startups through to Fortune 500 companies leverage data to achieve their goals, I can be your reliable source for analytics guidance and experienced implementation services.
Data Strategy
It’s important to see the big picture, or a lot of time and money can be spent with limited results. Understanding how your organization currently uses data and where that can be expanded and improved is key to taking a planned approach in prioritizing and executing analytics projects.
Many organizations tackle analytics project by project: figuring out and satisfying immediate requirements in an ongoing fashion. This leads to big systems that are hard to maintain and hard to navigate, as pieces keep being added according to the needs of one project or another, without the benefit of a framework to keep the pieces consistent in terms of implementation and output.
Other organizations try to avoid this by attempting to predict possible requirements years into the future, and end up tracking just about everything they can think of. While this approach will benefit from consistency the above method lacks, you are, as a matter of course, gathering a lot of data you probably will never need. That has implementation and cloud costs, as well as making it harder for people working with the data to find the bits they need to make better decisions.
The name Informed iteration reflects a balance of these extremes. We won’t pretend that we can figure out everything we need to know before we even start, but we will figure out what the core long-term goals are so that we can keep moving towards them and see the progress we make. We’ll figure out frameworks and processes that work for your organization so that we can expand solutions to meet new needs in an organized and coherent way.
With an understanding of your goals and a solid but flexible approach, we can then iterate effectively project to project, sprint by sprint. We set a roadmap, but accept that it can and will be refined as we see both what the data tells us, and how the people in your organization adapt to the availability of better information. External factors can force more dramatic changes. Every new campaign or feature to analyze might have different functionality, technology, stakeholders, and goals, and requires us to adapt to best serve the team members using the data.
But we do this with the benefit of frameworks and processes that will keep our solutions easier to maintain, expand, and use.
Solution Design
Much as we started out our strategy by assessing what we are trying to achieve, we’ll take a similar approach at the level of individual projects and solutions. Instead of thinking about what tools are available or what data we should gather, we’ll figure out what success for this project looks like, and work backwards.
Along with the goals, we’ll figure out the constraints and resources we’re working with. Crucially, we’ll connect the goals to people who need to use the output of the solution: What audience is being served, what are their skills, goals, and perspectives?
Then we’re ready to consider the technology: How will what data be brought together with which tools to enable our audience to achieve the goals we’ve established? This is where we go from business requirements to technical requirements, and figure out what work needs to happen to get to our end result.
Depending on the project, these questions can get very complicated, and with demanding stakeholders with short timelines and long requirement lists, it can be hard to find the right answers. But, you don’t have to do it alone. I have designed and documented solutions in dozens of demanding use cases, including Google Analytics 4 setups supporting 100s of report users for websites with millions of daily visitors.
Analytics Implementation
Many very skilled analytics professionals and consulting firms will do parts of what is described above, write up a strategy for you, and call it a day. Your organization can then either have internal resources read up on docs in an attempt to implement the strategy, or hire yet another service to look after implementation.
While it’s common for people in analytics to specialize either in business facing or implementation roles, I have done a lot of both in the last decade. I have no problem taking a data project from stakeholder ideation through requirement definition, solution design, tracking implementation, pipeline building, data visualization, and QA, all the way to training the user on the end product.
Implementation requires communicating a lot of very specific requirements to technical contributors, often spread across different parts of your organization or third party vendors. Some of these people may be used to working with analytics related requirements, and some may have never seen them before. Because I’ve done so much implementation work and collaboration with developers and engineers, I’m able to assess familiarity, meet people at their level, and get them up to speed quickly.
We generally document things by spreadsheet and text document, but for larger or ongoing engagements, I’m more than happy to jump into your PM or ticketing system, and have plenty of familiarity with Jira.
I have deep experience with the Google stack, including Analytics, Tag Manager, Looker Studio, BigQuery, and Cloud Platform. The further we get from those platforms, the less I can do in a hands-on sense, but my implementation abilities still make it easier to collaborate with your resources in other areas.