“Data are of course important in manufacturing, but I place the greatest? emphasis on facts.“
Taiichi Ohno, Data vs Facts, Toyota Way.
It’s easy to say what you want with data. Most leaders divine the message they want from data like tea leaves. They do this due to confirmation bias, not necessarily manipulation.
The problem is that improper acquisition of data leads to inaccurate conclusions. You can have the wrong data model tell you things that aren’t relevant to your desired outcome.
A systematic approach helps you decide what facts matter for the decision at hand if you want results. From a problem statement, you can develop a root cause. That root cause gains support and solutions from exact facts supported by data.
When Interpreting Data, You Must Have Appropriate Methodology Before Facts
The method comes before the data and facts. It is easy to misinterpret data or even have the wrong data without the right approach. Poor data interpretation is a common mistake in every prominent business today.
What you need is someone who can see beyond the emotions of the decision. Data matters, but not as much as how you’ll apply it for specific deliverables. The correct interpretation creates facts that stand validation beyond feeling.
Excellent decision-making is built on methodology, approach, and high regard for facts. Unfortunately, this kind of thinking is not a priority in public education or many business environments.
It doesn’t matter if you are manufacturing or delivering a service. Knowledge built on actual occurrences beats raw data every time. Today it is so easy to collect data that has no use.
The Human Mind Has Flaws When It Comes to Data Interpretation
It’s easy to make mistakes with data. The volume of data supports a feeling of completeness; this is an availability heuristic bias. Cognitive biases are natural in data collection, analysis, decision-making, and interpretation of data. If you do not emphasize facts, you will make these data mistakes.
- Collecting data because you can, not because you should. Stop wasting resources collecting data you won’t use or don’t need. Worse, collecting data because it is easy to do. Leaders are swimming in data.
- Data decisions by committee. The more people you have looking at your data, the more facts they will find. Without specialized understanding, everyone has opinions, not facts.
- Interpretation before collection. It’s not the statistics that lie; it’s the people. Forming a conclusion before proper analysis is malpractice, yet it happens daily in business.
- Not testing for statistical significance. Just because something happens twice doesn’t mean it will happen under the same conditions a third time. Look to predict behavior rather than interpret data.
- Sampling narrow scope of time or volume. You need enough data to find trends if any exist. Small samples often mean nothing. Be aware of conclusions based on a narrow scope of review.
- The average is often useless. Many data types give you convenient math averages, but that says nothing about behavior, investment, or conditions. Stopping at a mathematical average is a huge mistake.
The list of common mistakes when interpreting data costs businesses billions each year. False starts, bad decisions, and missed opportunities are the total costs of feelings over fact.
Do you have the talent necessary to find facts from your valuable data? Statistics alone do not stand for the truth. You need testing and validation methods to extract facts applicable to decision-making.
Not Knowing How to Interpret Data Makes Business Decisions Costly
A typical example where flawed interpretation can lead to costs is in marketing. If you misuse marketing data, you'll make bad decisions about where to apply advertising dollars.
Your marketing model could be direct response or database marketing; the problem can be the same. Brand advertising exhibits these mistakes in making assumptions about why marketing works rather than measuring.
In marketing, many leaders believe a few actions form a trend. Without an emphasis on facts, leaders in marketing ask the wrong questions or even look in the wrong places for customers. Marketing is full of waste due to this.
You may be getting new customers from social media than direct mail; therefore, invest more in social media. Suppose you are deciding without understanding key metrics. In that case, you can make what seems a rational decision that has no basis.
Data could include the number of sales, total expense by channel, or even time from the campaign to the first customer. However, deriving facts from cost per lead, cost per sale, profit per sale, and customer lifetime value increases accuracy. And those facts are tested by statistical accuracy.
It’s more complex than this, yet every day otherwise, innovative businesses leap into advertising engagements without any hope of profit. Measures are wrong before they even start. Marketing agencies will make this mistake because they want into your deep pockets.
Even after leaders notice campaigns aren’t working, they often keep spending because they don’t have the support to help them see the mistake. Worse, data presentation can obscure why marketing isn’t working.
The only solution is clear thinking around data and facts.
Without The Appropriate Methodology and Interpretation, Data Has No Value
The difference is poking around in the dark versus correcting customer behavior predictions. It is entirely possible to rule out channels that could be profitable simply due to emphasis. Facts, NOT feelings, are what grow business.
When you have questions about what influences behavior, include a business analyst who understands how to interpret data to make accurate decisions. You don’t have to go with complete data science, especially if you don’t have proper methods to make base decisions.
In my consulting, I’ve helped clients save millions with accurate thinking. A correct review of the data reveals hidden profitable markets. Beyond marketing, this concept also improves risk management, supply chain, and project management. Write with your questions.