Why is data the least important part of Competitive Intelligence?
Competitive Intelligence is essential for our better understanding of competitors. Notably our industry and changing consumer landscape. But why is data the least important part of Competitive Intelligence? Especially when the business world is full of data and tools for data. Lots of ways to manage data, thousands of platforms, and many many data experts. The next big thing in data must include a reference to AI. Or throw away statements about Big data, massive data or data lakes.
The biggest offender within Competitive Intelligence is probably VUCA. Standing for Volatility, Uncertainty, Complexity and Ambiguity. It sets out to describe the world we live in. Expect it doesn’t. We agree with Ben Gilad in his excellent book “The opposite of noise“. He has another meaning for VUCA – Virtually Useless Catchy Acronym. He states that VUCA is an acronym to sell more noise to unsuspecting people. Built around the instinctive fear of the unknown. Even if you can describe your VUCA world, what will you do about it? What solutions can be found knowing you are in a VUCA world? Competitive Intelligence is all about telling us what we don’t know. What’s next, and how we can do something about it.
It was a VUCA world when we emerged from the primaeval swamp and answered our first land-based question. “Arh. right, that’s what these four long things on the end of my body are for!” We think Ben would agree that data and all the noise surrounding this causes brain freeze. No amount of data or data lakes of AI platforms will obliterate uncertainty.
Again Ben suggests that when we come across a piece of data, ask this compelling question:
Can we use it to gain an edge as a company?
So AI-powered big data is becoming as annoying and tiresome. As irritating as phrases like growth hacking, boil the oceans and core competencies. Onboarding, run it up the flagpole, peel back the onion, synergy and outside the box. Then there are websites with pictures of chess pieces. And those forward-looking companies who claim to be data-driven!
We need to point out that we are not Philistines. We have our thoughts on a future Competitive Intelligence Saas platform. But the technology isn’t around yet for it. And we love some of the platforms on the market at the moment.
AI and big data definitely have their place. And there are some fantastic tools out there. But there is plenty of rubbish around too. Sucking unsuspecting people into a subscription. The best AI or Machine Learning is what saves people time. Especially in the medical assessment field. And probably in the enemy military communication and satellite imagery collection.
Basex consulting states that information overload costs the US economy $900 billion a year. Mainly in reduced employee productivity and lower innovation. Competitive Intelligence professionals mustn’t let insight be washed away in the information flood. The correct data helps us make informed decisions. Evaluate strategies, and build a competitive advantage over our competitors. But as we research and collect data, it’s essential not to be overwhelmed and confused to the point of giving up. Because the problem lies with gleaning meaning out of the information gathered. And it’s knowing when you have too much information. And knowing when to stop looking.
So when we say that data is the least important aspect, we mean that you’ll find answers in the data. The other parts of Competitive Intelligence are far more important than data. Before you start looking for data, it is critical to do two things first.
Firstly, you have to plan. What do you need to know to solve a problem? What’s the problem, and what is the decision-maker trying to do. How can we limit uncertainty for the decision-maker? What is it they need to know? No data has even been looked at yet. Not one byte.
Secondly, ask the right questions. Sit down and define the questions you need to answer to solve the problems isolated in the planning. Your questions need to be well thought out, and clearly, you need to avoid a yes/no question. They need to be written so a non-expert can understand the question. Then boil the question down into ideally one sentence of no more than three lines. Perhaps with only one dependent clause.
The agreed questions have to be inclusive and broad enough to cover what you think you’ll need to cover. But narrow sufficient to provide a helpful answer.
Then ask the question whether the question meets the decision maker’s needs. Remember, the more questions you ask; the more diluted the answers will be. So split the project into two rather than trying to squeeze as much into a single project.
After collecting the data, it’s then it’s the analysis. To do the analysis, you need to be able to sort the data. One way to do this could look like this:
- Place data into real or virtual baskets and remove anything that doesn’t fit.
- Play with the data
- Distinguish between what is known, unknown and what we think we know?
- How many are graded green? Too confident?
- Determine Hard drivers (capability) and soft drivers (intent)
- Focus on the gaps in my knowledge and how can we address them?
- Is there data which does not fit into one of the driver baskets? What do we do with it?
Analyse the data
Use structured analytical techniques. Break all the information down into smaller pieces. Then sort and look for patterns and trends. Use tools like Analysis of Competing Hypotheses to explain what’s going on. Define the who, what, where, when, and how of the situation. Look to use strategic business model techniques, especially Porter’s five forces. Look at mapping and conducting traffic analysis. Once we have done this, we bring all the information together and reach our conclusions.
Try sitting down and asking yourself and your team what’s really happening. And think about is the data and analysis are telling you. If your thinking is wrong, what would you see?
The aim of reporting the insight is to ensure decision-makers receive and understands:
- What’s going on?
- What’s going to happen?
- And what can the decision-maker do about taking advantage?
So it’s important to write a clear and concise report. It’s not an academic masterpiece designed to impress the professor. Get the message across and hammer it in with a mallet. Isolate the essential findings and recommended action. A good thing to do is to list all the original questions. Then use the analysis and data and answer them in no more than two sentences. If we struggle to do this, we find out that we need to work more on the answer. Simple and easy way to tell that more work is required.
Oh yeah, the data
You have reasons for doing Competitive Intelligence in the first place. And have created some excellent questions. You will have the ability to focus on the data you need.
Eight times out of ten, you dont need to spend a penny getting the data. It’s out there if you keep looking. The fact that you are focused on questions means you will not be blinded by information overload. Any data you can’t find can be answered by someone. And someone will know the answer. If you have a choice of primary (talking to someone) or secondary (desk research), always go with the primary. It’s usually more helpful for your needs.
Why is data the least important part of Competitive Intelligence?
In conclusion, Competitive Intelligence is essential for understanding our competitive landscape. By analysing data, we can develop a comprehensive understanding of our competition. And finding out what strategies they are using to succeed. Yet, data is the least important part of Competitive Intelligence. The most important part is the analysis and interpretation of that data. We can use this information to make informed decisions about our own business and how to improve it. And yes, Octopus can help you with all aspects of Competitive Intelligence.