It is important that strategic planning not be linear. Strategic planning has moments of parallelism, most notably at the beginning and the end (there is really no end, but we’ll get to that later) of the project. So as you have your team ruminating on the mission statement and agreeing on the process, you can engage a wide range of people on the environmental scan. If you do not plan on conducting an environmental scan, do not create a strategic plan. That is a simple axiom. No data, no plan.
So what should an environmental scan include? That depends on what kind of organization you are. Typically these scans include the STEEP elements, which are:
- Economic, and
As you can imagine, public sector concerns are significantly different than commercial ones, and all of them vary by industry.
STEEP categorization helps force coverage of topics that might be ignored. In interviews, and in research, participants and stakeholders must confront for instant, the social implications or a new product or policy. Technologists need to look at not only social impact, but how their invention may be regulated.
STEEP is used as an organizing principle for gather uncertainties in scenarios planning exercises. It is just as useful when confronting near-term certainties. STEEP reigns in a very unique kind of confirmation bias: looking only where you want to look.
STEEP reigns in a very unique kind of confirmation bias: looking only where you want to look.
Other Things to Consider
No matter your area, also gather data on:
- the competition (even government agencies and schools have competitors)
- the audiences you are trying to attract (understand their needs and their current relationship to you)
- sources of funding and revenue and what will affect your audiences
Later, we’ll put this into a deeper context as I discuss SWOT, or WOTS-UP analysis, which will provide a way of evaluating the data against goals. For now, the key is understanding the environment, not evaluating it. Don’t spend a whole lot of time looking at the data, spend most of your time thinking about what data you will need. Do worry about quality, but not implications. There will be plenty of time for implications later.