A Framework for Using Data Science in Your Company: Four Approaches
Data science, machine learning and AI are buzzy topics. Many leaders within companies are excited to explore these technologies to gain a competitive advantage, deliver more value to their users, or avoid getting left behind. In our experience, managers often know they want to make better use of data or have a data problem. What is less clear are the specifics of what they can solve with data science.
In order to help businesses think about the types of problems or tasks they might approach using data science, we’ve developed a general framework for how you look at opportunities to use data science. This is meant to jumpstart a brainstorm as to where data science might be applied within an organisation, and to ensure it delivers value.
1. Repetitive and time-consuming tasks
As a general rule, if you can teach a person, you can teach a machine. Machine learning and data science can be very well suited for repetitive and time-consuming tasks. This generally involves classifying large volumes of information by subject matter.
An example of this problem is our project with Concentre. They have a large volume of documents to check, and previously this was a completely human-powered endeavour. We effectively trained a machine to do this task for them, which cut the time they needed to spend checking these documents down dramatically.
Another great example of this comes from a cucumber farmer in Japan. Cucumbers need to be sorted by size and curvature once picked. This task fell to people. The cucumber farmer realised this is a perfect problem for image recognition and deep learning. So, he built a tool that took images of the cucumbers and automatically sorted them into groups based on those factors.
2. Error-prone and/or highly permutative tasks
Tightly related to the above, these are tasks that humans often make a mistake when doing. This might be due to the amount of data a human needs to process or consider to complete these tasks. Or it could be because as above, the repetition might lead to boredom and mistakes being made.
A great example of this in our past work is a major consultancy. Their large team of consultants had specific skills and experience and were located across the world. Their clients needed certain skills, over specific time periods in specific locations. Assigning the right teams for the right jobs at the right times is an enormous undertaking, with countless factors to consider. You have to do many permutations, each change having knock-on effects for other assignments.
If you’ve had a wedding, you might have experienced a similar problem when setting up your table assignments. You want to have the right people together at tables, and there are always pairs of people you can’t seat together. Uncle Joe doesn’t get along with Aunt Emma. Each change you make leads to a cascading effect on other tables, and it ends up taking way more time than you thought. Then you share it with the groom’s mother in law and she doesn’t like her table and so you start over. Machine learning is a perfect solution. You could tag participants by personality type and group them together, keeping in mind pairs that need to be excluded from each other.
3. There is clarity around the problem
Don’t do data science for the sake of doing data science. There needs to be a complete understanding of the problem you are trying to solve. For the cucumber farmer in Japan, they needed to classify cucumbers by size and shape. For Concentre, they needed to validate files against the metadata.
Starting with a solution or technology you want to use is the wrong way around. You can’t just throw algorithms at something and hope it returns something useful for you. Data science, machine learning and AI are not magic solutions.
Wanting to use data science to improve marketing or sales is not enough information to start with. You should avoid top-down approaches like this, and instead work up from the problem to see if data science is the right tool for the job. An example problem for sales might be not having a good way of predicting which leads will convert into customers, which leads to inefficiency and salespeople wasting time on the wrong leads when they could be nurturing the right ones. Now, this is an interesting problem to solve, and potentially one for data science. Which brings us to the next part of the framework.
4. It’s clear why solving the problem is valuable
This is also related to the previous point. Is the problem a big enough headache that it is worth spending time-solving? Does it deliver enough value to users, or enough savings, or increased revenue to embark on a potentially uncertain and time-consuming journey? This needs to be clear and obvious to all stakeholders from the beginning because it’s not just about kicking off the project, but also ensuring everyone is brought into final delivery of the data science product. For example, a product manager will need to see value in the proposed solution enough to prioritise getting it into a roadmap. A sales manager will need to be clear on why it improves outcomes enough to ask his sales team to change their workflows to use it.
Using our previous sales example, a good way to quantify this is to try and calculate how much time is spent trying to qualify leads, and how effective sales teams are at this currently. Then, you can try and better understand how a data science solution might improve accuracy, and/or cut down on the teams time spent researching leads.
For Concentre, this was clear from the outset. They had consultants doing these checks manually for 8 hours per day, in which time a consultant could complete 20 checks and do the necessary next steps in rectifying errors. With our solution, they were able to do the same amount of work in 30 minutes because our tool took away the time-consuming portion of the task in checking the metadata against the file name. Now they can utilise those consultants on other projects for their clients.
This is meant to be a framework to help you start to think about how you might approach solving problems in your organisation, and in particular which of those problems are well suited for data science. We are here as a resource to help you make use of this framework so do reach out if you have questions or want to run through specifics in your organisation.