Datasets can be basis for conversations that create engagement and collaborations with decision makers.
Building upon Voiovich’s program, Arjan Singh spoke Wednesday about “how to build an early warning system”. He shared lessons learned in how to develop such constructs.
Singh teaches courses on creating strategy based on valid data points. In his work, he asks: What is the competitive advantage in using the same data sets as the competition? The real key in analysis is in creating data sets that do not currently exist.
Singh is responsible for delivering three year strategic plans for his organization, and he discussed how early warning devices are built into the strategic planning process.
CI provides actionable intelligence to support decision making across an enterprise. The aim of CI is to align organizations on the risks and opportunities to current and future business.
The key is to make decisions with the least amount of information, says Singh. Fewer options exist as we come closer to events; but organizations need to become comfortable making decisions with less information, because when making decisions with less available information, potential exists to be a market leader. The only time things can be determined with 100% certainty is after the fact; it is sometimes challenging to feel at ease making decisions in advance of “known” information.
CI pros can help management gain comfort in making decisions in uncertain circumstances by building stories and sharing sources consulted to reach the conclusions and assumptions along the way to providing options for consideration. A transparent framework is critical for delivering impactful intelligence. With transparency, the conversation changes from “this is what will happen” to “here’s what we did, here were our assumptions—if you disagree, you have information we do not (and we need it) or you have different assumptions.”
This creates a ‘co-creation’ space of collaboration with decision makers.
It is easy to tell people what they want to hear; delivering bad news is difficult. The result may be questioning of data sets and so on, but transparent approaches can be impactful in diffusing these obstacles.
Tactical and strategic intelligence
Tactical is here and now, and consists of questions like “How do we execute? How do we get this to market?”
Strategic intelligence is more future oriented, and considers inquiries like “How do we prepare for the future? What will it look like?”
Interesting vs actionable intelligence
Interesting intelligence springs from questions like “Tell me everything about…” and “I need it tomorrow morning” and the observation “It’s interesting…”
Actionable intelligence is linked to decisions. It is critical, tangible and focused leads to different types of conversations.
Determine your starting points by considering deliverables and impact within the organization. What kind of questions are you asked? Think about the types of intelligence (tactical, strategic, interesting, and actionable) and their combinations to assess various situations.
Tactical/interesting situations are reactive; tactical/actionable information considers the competition. “How is the competition selling? What are they saying?”
Strategic/Interesting perspectives investigate: “I wonder, what the world will look like in four years” (This is the ‘most dangerous’ type of question, per Singh.) Strategic/actionable asks: “What can we do about it now?”
Think about where you are (as a CI pro) and consider where you want to be—the answer may depend on your industry and company; the mix of what you want to produce will vary on existing constructs.
Develop your roadmap
Consider again strategic, tactical, interesting and actionable data.
Business strategic early warning systems
Business early warning systems can take a lesson from early warning systems we see regarding weather predictions. Satellites, sensors, and other devices exist to provide comfort via a process as they produce data sets about storms and other weather conditions in the short term. And long term perspectives can also be gained via early warning alerts, as well—The forecasting of El Nino conditions serves as a good example.
In a business setting, key building blocks of strategic early warning systems are timelines, focus, and ultimately, the process. From a timeline perspective, examine different industries and realize that depending on the nature of the business, timelines will vary. In the tech zone, three years commonly comprise a planning horizon, for instance, and in the energy industry, ten years is more common in decision making cycles due to varying data sets and greater investments required.
Next, consider key stakeholders. What are they thinking about? How far in advance are they thinking? Likely, they are existing more in the short-term, although this varies depending on their role within the company. Know their needs with your observation of their tactical vs strategic needs.
Strategic and tactical questions vary on the differing needs and timelines of the industry.
In early warning systems, how far ahead will you provide deliverables? You need to consider where you want to make an impact.
In building early warning systems, ‘make it land in your business.’ Consider timelines, responsibility and accountability. Deliver analysis close to business issues and timelines. Provide tangible options; not action advice. This builds trust and grows comfort levels. Know what is acceptable in your corporate culture.
To determine focus of an early warning system, define future industry/world scenarios, articulate key long term strategies, and consider issues for senior stakeholders: What is “top of mind’ for them? Be sure to create ongoing dialog.
To build a system, first identify and categorize them. In a pilot stage, test information quality, design and trial early warning alerts, and determine how to disseminate the information. Then enter deployment phase.
Objective of an Early Warning System:
- Track direction of change
- Identify key areas of change and implications for the company
- monitor the activity of competitors in the evolving space
- Highlight specific opportunities and threats
Singh provided examples to further illustrate his commentary and theories, emphasizing the importance of data sets, transparency and connections between alert issues and impact on strategy for each business unit. His examples further demonstrated the value CI can provide within organizations by assigning dollar value to various projects, as well as with explanation that non-revenue risks can be avoided with CI—loss of credibility in the marketplace, for example.