Almost every company that undertakes a strategic planning effort sets a goal of reducing costs and increasing profits. And many of those companies fail in one or both of these goals. Failure often occurs due to an inability to measure the business’s current state accurately. 

What gets measured gets managed.

- Peter Drucker

Hidden costs are hard to eliminate. Lost revenues don’t add to the bottom line. And when you don’t know why you’re spending money on activities that don’t drive results, you might as well be throwing money in the trash.

Fortunately, advances in data science are making a significant impact on businesses. Data science methods help companies achieve strategic objectives, enhance day-to-day operations, prepare financial analytics, and improve customer interactions. 

However, many companies struggle to implement data science techniques. And they often struggle for one or more of the following reasons:

1. They don’t have the data to support a data science project.
2. They haven’t sufficiently defined the problem worth solving. 
3. They hire the wrong people to lead the data science project.

Companies that don’t have sufficient data should start capturing data immediately.

Many companies choose not to collect data, overwrite existing data, or delete stored data. People often believe they are saving money on data storage. Unfortunately, this penny-wise, pound-foolish mentality does more harm than good.  

Spend more time than you believe necessary to define the problem.

Many executives believe they know why their business isn’t performing. And they hire consultants to prove their theories correct. Sure they may get what they want, but this doesn’t mean they will achieve their business goals. Start with a problem rather than a solution. 

Technical skills should be the last factor for hiring a data scientist.

The most important trait of a good data scientist is curiosity. And you can’t train for curiosity. A good data science team will ask questions about your business that you haven’t considered before. If you simply want someone to code up a solution that you’ve proposed, you’re doing product development.

Make no mistake.

The data science team must be capable from a technical standpoint. Statistical methods, data analysis, and machine learning are skills that must exist. However, without curiosity, communication, and domain knowledge the team will be doing more technical work than business problem-solving.