Company Performance Metrics
- Nate Watson: SpeakerPast Role: Contemporary Analysis, Director Of Operations
Gordon Summers: Data Manipulation and Management Trainer / Data Scientist
Data science is broken. Most companies either have a very good R&D team who struggles to stay engaged as more and more of their job is maintenance and not invention; or, they are on their 2nd/3rd/4th even version of their first data scientist (some can't even find one) who starts off well, but leaves taking all their
institutional knowledge with them.
What's wrong and why this is happening goes back to the idea that data science is just another type of employee; data-driven decision making another strategy to implement.
This is not the case.
CAN has been around since the beginning. Before the term data science. Before the term Big Data. Before the term predictive analytics. We started as economists doing econometrics. What we have seen over the last 11 years, and what we have discovered, is that data science (know by us as data-driven decision making) is a complete overhaul of philosophy of a company and a completely new type of employee.
This isn't easily implementable and it isn't done well by anyone we have seen. But why?
Because companies have been lying to themselves. Especially those that are pre-internet. They say they use data (they don't). They say they are proactive (they aren't). All software and tech really has really done, when you pull the covers back, is make the same processes companies were doing in the 70's, faster. That's right. Companies have changed nearly nothing and its starting to show.
CAN was invented to help companies transition from reactive (making plans based on what happened last week, last month, and last year) to proactive. Looking at your data, looking at the trends, and making decisions based on what the data is telling actually you is extremely hard.
You see you cant innovate internally. I know many have tried and a few outliers have succeeded. But the vast majority of companies cannot just hire a senior data scientist and expect them to lead the change. Its a recipe for disaster we have seen many, many times.
So we went and invented the right way to implement data science into an organization where it will work, flourish, grow, and in the end be owned by the company where it can be successful.
We cracked the code.
It's not easy and it's certainly not a short road. True paradigm shift never is. But, with a partner like CAN, true change management from reative to data-driven decision making is possible. We have seen it, done it, and currently doing it.
You need but only reach out.
