Before data science had fully developed into what it is today, businesses practiced “data mining”. Since the 1990s, significant advancements in technology have allowed us to combine computer science with data mining to create what we now know as “data science”. As more companies started dabbling in data science, we entered a period of “data overload”, in which there was more data than humans had the capacity to sift through. This is especially true in marketing, which has become a big data problem for most companies. Companies that wrangle and organize the influx of marketing data for automated decision making have a competitive advantage. Marketing data science to include machine learning and AI have greatly enhanced our ability to do this.
In essence, marketing data science asks specific questions and analyzes information to solve complex problems in business funnels. The methods used will vary greatly depending on the size and budget of the business, the amount and type of data, and the specialization of the data scientist. Anywhere from one individual to a large team can be tasked with using data to uncover valuable information. If you want to prove ROI and maximize your sales and marketing potential, here’s what you need to know.
What is Data Science?
From start to finish, data science is the process of collecting, storing, examining, and gaining insights from data. From there, company leaders can make changes that support revenue growth and sustainability. Within this overarching process lies many complex steps which, depending on the problem, may involve A/B testing, anomaly detecting, or other things that fall under the umbrella of data science.
Let’s look at data pipelines as an example. Data pipelines are automated systems that help businesses move data and keep track of information. Data pipelines need to be created, managed, and repaired as problems pop up. A business might want easy access to sales records to predict amounts of inventory to order. This is one of many ways pipelines can drastically reduce your workload.
Business owners can’t have eyes on their company at all times. This is where machine learning can provide a valuable monitoring tool called anomaly detection. This tool detects outliers in data. These insights can protect a business from selling a faulty product or even being the victim of fraud. Anomaly detection lets businesses know when something isn’t quite right. Teams can then focus on tasks without worrying about extraneous circumstances.
Data science also allows businesses to store data securely (data warehouses), examine large sets of information (data mining), and identify corrupt items from a data set so they can be deleted or modified (data cleaning). Data cleaning ensures that businesses have accurate data and can eliminate things like duplicates and misleading information. These are just some of the myriad ways businesses benefit from data science.
Why is Marketing Data Science Important?
Gone are the days when big data was only useful for big corporations with even bigger budgets. According to a Forbes survey, “95 percent of businesses face some kind of need to manage unstructured data, with over 40 percent of businesses saying they must do so on a frequent basis.”
A business may want to customize user experience. To do this they need data on how their customers are currently interacting with their brand. If a business wants to map social networks, they need to properly interpret social analytics. Perhaps they want to understand target demographic behaviors, track brand feedback, or solve recurring marketing conundrums. All of this requires some form of data science to get definite insights.
It’s not enough to simply have volumes of information without an expert to interpret it. And while all marketing professionals have some expertise in examining data, there’s a difference between being literate in data and being an expert in data science.
Data science essentially tells marketers whether the decisions they are making are paying off – or not. It also provides guidance for tweaking marketing strategies, budgeting smarter, and making more accurate predictions. Without true data interpretation, marketers are essentially shooting at targets in the dark – They can make educated guesses about what activities will provide ROI, but they can never know for sure. That certainty is something data science comes closest to providing.
According to 2019 estimates, more than 2,000,000,000,000,000,000 bytes of data are created every single day. In the past few years, we’ve seen a stark shortage in data scientists compared to the demand. This means businesses without a team of data scientists will need to learn more, keep their data organized, and invest in tools that help them stay in the game.
Where to Begin
Utilizing data science can at first seem overwhelming, but by pinpointing exactly what your company needs to know, what the budget is, and who is qualified to do it, you’re already halfway there. Identify the biggest confusion areas in your business, or the areas where new insights could be gamechangers. As predictive analytics and machine learning algorithms advance, companies can create environments that support the effective use of data for decision-making and problem-solving.