07. Recognizing Data Analytics Project – Content [259]

Now that you understand a lot about data science and how it works (remember what we said about negative talk) we will now start to identify Data Science opportunities that are all around you. These opportunities are where you see a problem or situation at your job for improvement which can be achieved by having the right information at the right time. This section will provide steps for starting to identify these areas and outline what information can help to solve the problem.

An Example:

ACME International seems to always not have enough inventory for orders at random times during the year. This has a negative impact on customer service and creates a loss in revenue. You know that if the company had a good forecast of orders 6 weeks out then that would solve or at least lessen the problem. At this point, you are only identifying the problem and the information that could be useful to solve the problem. Now that the problem is identified we will discuss later how to go to the next level of putting an informal or formal proposal together as a project to be considered to be worked on. Note that you have all the order data in the sales system and all the items that have been back-ordered. That can be a good start for the type of data that you need to start to look at the problem.

You as well as everyone else in the company has a unique perspective and vantage point in the company. This means that you sometimes see things that others don’t. The idea behind this section is to have you recognize that gift and have you look at situations with a more critical eye towards identifying problems that may only be obvious and empower you with the ability to describe it in terms that others can also see it and make a positive impact on the company’s bottom line.

Here is a step by step technique for identifying and start to describe a problem that may be a candidate for a Data Science solution.

1. Observe and take note of activities where there are work output gaps or inefficiencies

One of the best methods of understanding why a business succeeds or fails is to take a close look at the available business processes. Doing this will give you an insight into why things happen the way they do.

The answers to these questions listed above can be deduced with the right data science model and approach. Taking a closer look at the company’s data could provide the best solution to solving the reason why a company or business keeps losing profits.

As an employee, you tend to have a closer connection with the customers you serve and that attribute provides a better solution towards addressing your employer’s challenges. So first make a list of all the problems that you see around you and would make a difference to do something about it.

2. Find out how much your company or department is losing due to the problems mentioned above? 

Nothing happens by accident; every action attracts an equal but opposite reaction. Taking note of the numbers can reveal quite a lot of information. Let’s say for example we know that every backorder cost the company %5 of the sale. And we know that we have 1M in back orders last year so that cost the company  50K in revenue lost. If we can reduce that cost to half then we have saved the company $25K/year.

This was a back of the envelop type estimate but by identifying problem areas and starting to look at simple analytical thinking starts the ball rolling. 

3. Define What A Potential Process Could Be With The Right Data Science Attached to it.

Now that you have identified a problem then what questions if answered can help you develop a better process? This step allows you to start to understand what information is missing from making a better decision. Let’s start to identify data sources where you can solicit those answers. Ask the question does our company have data that can help me understand my ordering pattern to fun analysis on the data and predict future sales?

a. Identify the Data associated with the project

All companies tend to have a record of daily activities ranging from customer ordering processes, customer demographics and customer buying history. Start to list those systems that are potential data sources. They can be formal like a CRM system or more informal like a spreadsheet on someone’s desktop. Each of these sources is something to consider when proposing a project for data analysis. 

b. Think through if you have enough information to solve the problem or will you need more data

The higher the level of data available to you, the easier it becomes to offer the right solutions to address your company’s needs. Remember that your employers may only see the numbers, while you have the best information to explain why. This exercise will give you insight into other possible data sources that you did not consider at first. Don’t let not having enough data prevent the proposal of the project because there may be other data sources that you are not aware of but are in line with the needs of the project. Open discussion is very valuable. 

c. Inform your stakeholders about the need for discussions about the type of data analysis to solve problems. 

As stated earlier, employees have a direct relationship with the customers, and this can serve as an advantage in getting to know what they want, why they behave the way they do and the possible ways to remedy the situation. Once you are sure of your findings, you can pitch it to your manager by formally writing to him or her or discussing the matter in a meeting. The goal is to point out the problem and make sure your manager understands why this is a step in the right direction. The next chapter gives you a form to help you organize your thoughts and conversation.

Bottom line

You don’t have to be a data scientist to be effective in using this new data analytics. Your part can be to take the knowledge that you have gained overdoing your job and combine that knowledge with the ability to identify company areas of improvement. Then apply a data analytics perspective with suggestions, opportunities for the company to make a difference to its overall bottom line.

  1. Take note of activities where things are broken or inefficient
  2. Find out how much your company or department is losing due to the activities mentioned above?
  3. Define What A Potential Good Process Could Be With The Right Data

Data science is an invaluable addition to company values, objectives and mission as long as the companies can identify when they need one. Data science saves you time while giving you value for your money.