I’ve helped people in businesses ranging in all sorts of industries from healthcare to music to transportation. Yet even in these widely different industries, I see the same common threads of struggles with data science with most of these companies.
Turns out, most of the struggles I see can be grouped into a combination of one or more of the buckets below.
1. Seeing data science as “too sophisticated”
So often business owners look at all the innovation coming out of MAANG (Meta, Amazon, Apple, Netflix, and Google) companies, and immediately tell themselves that because they are a small business, they could never hope to operate on the level of innovation and sophistication that MAANG companies do.
Often, these business owners see data science as one of those “sophisticated” things that will never be accessible to them. Data science is seen as a big, scary thing that only people with oodles oodles of resources can ever implement. However, in today’s world, being a data driven company is fundamental to any successful business..
But never fear! Data science and operating a data driven business is more accessible today than it ever has been before. Many companies (including Google and Amazon) are focused on creating products that make data science available to basically everybody.
For those looking for more of an out-of the box solution, there are also third party solutions that will create dashboards, analysis, and machine learning models for you. Some specialize in e-commerce, others in physical products, and some in other specific industries.
And for those with a DIY spirit, there are cloud solutions that allow you to pick, choose, and design the best data science solution for your business. In these DIY solutions, you can pay for only the bells and whistles that you want.
2. Getting overwhelmed with the amount of data
Our world is rich with data. We live in the golden age of data. Where every company is using data in some way or another.
Data is used to recommend everything from which advertisements will be most effective on you as a consumer to what boots you are likely to buy when the weather gets a bit chillier. We use data to determine which roads need to be built where and with what type of material. We utilize data to create revenue projections so we can know how much money we can budget for the new year.
Data is everywhere. To the point that it can easily overwhelm. This leads many small businesses to try and run their business with insights from a series of spreadsheets, or even a gut feeling.
Gut feelings can often be counter to what is actually happening in the business. And spreadsheets leave us feeling cluttered and lost in the numbers.
Thankfully, there are tools designed specifically to help us get a snapshot of what would alternatively be an overwhelming amount of data. Spreadsheets and databases on their own can be very overwhelming very quickly. However, taking those spreadsheets and creating some insightful graphs, reports, and dashboards can help us get a handle on our data.
3. Jumping into data science without investing in data analytics
I’ll see companies get really excited about the idea of data science and go and immediately hire their first data scientist (paying data scientist prices). Then the company will ask the data scientist to give them insight into their data.
However since the company has not built its data science foundations, they often end up disappointed as they aren’t using the full potential of the data scientist.
Even though we get really excited about things like Data Science, Machine Learning, and AI, there is a lot of value to be gained at a lower effort and lower cost of entry. Things such as Data Analysis, Data Engineering, and Data Storytelling provide an incredible value for a fraction of the Data Science price tag. So we can pay less money, and spend less resources, and still get powerful insights.
Data Science, Machine Learning, and AI deal in the art of figuring out what is unknown. However, there is a lot of value to be gained in understanding what is known.
This is a situation where the Pareto Principle applies. Said another way, 80% of the value from a business’s data can be derived with 20% of the effort and resources.
Understanding what is happening in the present and the past does not require the high price tag of Data Science and can instead be achieved with Data Analysis.
Data analysts can help to clean your data to make it more useful. They also give insight into data, connecting the numbers to tell a story. For example, a data analyst can help answer questions such as “Is my revenue going up or down over time? What are some factors that drive the revenue increasing?”
Understanding your past and present before you understand your future will save you time, resources, and heartache.
4. Jumping into data science and expecting to get MAANG results in a month
When looking at a MAANG company or some other Data Innovator company, it is easy to say to yourself “Look at all these cool models these companies are creating! Data Science can do anything!”
We have to remember that most Data Innovator companies (Google, Meta, Amazon, Microsoft, etc) have been in the world of data science since it began in the early 2010s. These guys were the innovators for it. So they’ve had a track record of at least a decade (which in the tech world is basically a century) of learning these things. They’ve had their data foundations set for a long time.
This gives them the time, space, and resources to solve incredibly complex problems in a relatively short amount of time. However, they have spent years reaching that capability.
We can learn from their trials and errors to build data science capabilities for our businesses in quicker times than the decade they have spent, but still must understand becoming a data-driven business is a journey and process. Not just a one-time month-turnaround project.
5. Not getting started with data science
Another issue I see businesses fall into is simply not getting started. In most scenarios (excluding time travel, of course) data has to be captured in the present.
For example, if a lead visits your website on a Monday, yet you only begin tracking traffic on your website on that following Friday, the Monday visit won’t be tracked.
Much of data analysis and data science deals with historical data, so beginning to capture that data as soon as possible is important.
Secondarily, many small businesses understand the importance of capturing data, but feel lost when it comes to how to make that data useful. So they will capture the data on some spreadsheets or some sort of database and simply put it to the side to be dealt with at a later date.
While this is better than not capturing anything, not acting on your data in the age of data is costing you money. Making decisions in your business without understanding the numbers and data story behind your business is leaving you at risk of making ineffective decisions.
There is an entire process and journey associated with being a data driven business. All journeys have a starting point that is often messy and imperfect.
However, perfect is the enemy of done. Every day you don’t start (even with an imperfect solution) is a day you are leaving money and opportunity on the table.
There are small and practical steps you can take today to further your progress on the Data Journey. Begin to capture more data points. Begin cleaning that data in ways that will make it more useful. Look into creating a simple dashboard that will give you basic insights into your data.
These are all bits and bobs that we can do to incrementally further our Data Journey. Increasing our data knowledge, increasing our ability to be data driven, and solidifying the data science foundations of our business.
And all that to say, everyday I see business owners struggle with these problems. I wonder, do any of these seem to ring true? If so, reach out and we’ll schedule some time to chat about how we can overcome these struggles.
