3 Different Types of Analytics Project Teams and
3 Questions for How to Get to Innovation Faster

Guest Post by Rosemary Hossenlopp

Running an analytics project is not like any other project. There are only a handful of organizations who are running data projects that link data-driven initiatives to corporate innovation goals. This shows that knowledge on how to run a Big Data project isn’t widely understood by most companies. Analytics projects also fail more than other projects so there are risks which most organizations ignore in their haste to get into the data-driven project space. Who says so?

Few Organizations Have a an Analytics Project Process or Plan

According to PwC, a consulting firm powerhouse, only 4% of companies have an effective data strategy. So doing the nonquant-intensive math, this means that 96% of global organizations neither have a process, nor a plan that allows them to use their data assets for competitive growth or internal improvements. To See this Sad Math Summary, click here.

Most Analytics or Big Data Projects Fail

According to Gartner, most projects fail as projects are treated just like any other project. To see this Second Sad Summary, click here.

Analytics Teams Must Understand The Next Big Step to Take

Before this post starts to sound like the opening night of the United States Republican Convention which was touting and trumpeting (pun intended) doom and gloom, there is a path and framework for teams to quickly see how to strategically move their teams to faster launches by understanding Which Type of Analytics Project They Are On. Who can analyze their organization to understand their Big Data Project Type? The likely role suspects would be Project managers, Product Owners, Engineering leaders, Project Management Office (PMO) leaders. Yet first, why should Analytics Project Teams assess their Project Environment?

Benefits For an Analytics Team to Understand Their Current Project Type

Leadership has high expectations for Analytics and Big Data teams to produce results. So teams focus on getting to the next sprint demo rather than stepping back and seeing if there are hidden organizational issues that will trip them up on their way to launch success. In the old engineering days, we called this an undetected defect. Now we need to apply the same focus on finding and removing technical bugs to looking for process and planning steps, that if skipped, will tank your project.

What are the benefits of assessing your Big Data Project Environment?

  1. Increase time to value – avoiding missteps in process & planning will prevent politics and push back at project launch.
  2. Increase team motivation – technical execution is terrific yet ultimate lack of user adoption is terrible.
  3. Increase your motivation – who wants the three cousins of churn, change and chaos on your daily agenda demanding attention?

3 Types of Data Science, Big Data & Predictive Analytics Projects

We are not looking at the Red, Yellow, Green traditional dials for project health but an assessment of where each team is at on its journey to Innovation and Growth Mastery. There may be 3 stages your Analytics team is at:

Technology Tinkering

Definition: Build-out of data science talent, skills and infrastructure for pursuing market opportunities.This is a critical step as organizations try to solve an organization process issue, a market problem or look for intellectual capital insight in their data. There are thousands of technical and talent development user stories in these teams backlog. Many teams can get stalled in this work-intensive stage and not even prioritize monitizable customer use cases.

Market Experimenters

Definition: Coordinated Data Science, data information and Business Process Efforts. These are highly focused, and disciplined efforts within a functional group or business unit. As an example, marketing operations teams are driven to understand their customer base to increase reach and revenue. Marketing product owners have so many experiments to run in their own back yard that they often donít unlock bigger use cases. Why? It is so hard to work with outside teams like finance, compensation, and support to access their data or impact their processes.

Growth Hacking

Definition: Visionary exploration of data driven hyper-growth strategies. Many organizations have visible and articulate spokesman/women for innovation ideas. Demoís may be limited to proof of concepts and not ready to scale. So organizations are in the hype cycle and may not be able to scale to production ready systems. Visionaries may not have the patience to understand the work, time and money needed to build out the infrastructure and data governance.

Next Big Step Plan for Analytics and Big Data teams

There are hundreds of urgent actions for each project type. Yet consistently there is just one question for each project type that can break teams out into a path towards innovation and growth.

Technology Tinkering Teams

  • Big Next Step: Customer Value Creation
  • Key Action Needed: Product Owner working with all stakeholders to gain agreement on problem to be solved & key metrics.

Market Experimenters

  • Big Next Step: Cross-functional Discipline
  • Key Action Needed: Product Owner and Leadership gaining alignment with enterprise stakeholders on end- to-end user stories, which solved, would produce innovation results.

Growth Hacking

  • Big Next Step: Metrics towards scalable & Monitizable Growth
  • Key Action Needed: Gaining agreement on all governance, funding and feature prioritization process.

Question: What Big Next Step would you add or questions do you have? Ask Connie and I in our FREE September Webinar

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