There are two parts to the definition of science: both the process of finding the truth and its result. Although there is sometimes consensus on commonly held truths and conclusions, science is always in flux. Science is constantly creating and reusing reality. Discussions about and within science almost automatically result in a step forward.
Within science, data science has become an increasingly large and important field. Data science is the method to and the result of extracting knowledge from data. Simply put, data are the object of data science.
Due to the exponential growth in the use of digital tools, the amount of data generated automatically within business processes and customer interaction has increased at an increasingly rapid rate. This data is not always neatly structured, correctly stored, accessible and comprehensible. In fact, many companies do not even know what amount of data they are managing or where the data is located. This is where data science comes in.
Scientists & engineers
Data science, data engineering and data analysis are often used interchangeably. These fields also have overlap. A data engineer is basically responsible for making data available and asking the following questions:
- What data is there?
- What sources can be used?
- Where is the data located?
- How can we access it?
Then it is up to the same engineer to transform the data into universal or readable formats. This sometimes requires code, but not necessarily. The engineer is someone who searches for data, cleans it up and links it together.
A data scientist is then the person who makes analyses based on the data. For this purpose, algorithms are sometimes used, like machine learning.
As mentioned, the collaboration between data engineers and data scientists is close and the roles are not specifically defined. It differs per company, per project and per issue.
Data Science Machine
At Newcraft, we’ve developed the Data Science Machine. This machine is not a physical object that’s roaring in our office, but is a method to quickly and efficiently unlock the right data for every company and project, merge it and search for applications. Through years of experience with various data sources and their processing, we can quickly retrieve data, structure it, make it transparent, link it and make it a central point in the data web. Based on this single source of truth, new applications and solutions are found, but the Data Science Machine also contains standardized solutions that can be applied quickly.
Applications
Examples of internal data sources are (behavior through) marketing channels, CRM systems, sales channels and web visits. This data can be enriched with external sources like markets, trends and location data. In this way, we can create the most complete picture possible that can be assessed, and on which you can make better decisions.
For example, what does the customer journey look like? Which customers research online but buy offline? Or the other way around? Which marketing channels are successful? How can the ratio be improved? Which customers are the most valuable? Why exactly those? How can service delivery be better? When is the best time for a campaign? And where should that campaign be run? How long, in fact?
But also: how does the data relate to external sources? What is the market doing? Where are the opportunities? What is the influence of sentiments, the weather, trends and competition?
The real power of data science lies in finding new questions. The world is always changing, data keeps coming and insights renew themselves. Newcraft’s Data Science Machine is an essential tool to help with this.
Newcraft’s approach
At Newcraft, we try to turn data into information. First we do an assessment, in which we try to ask the right questions, such as:
- What data is available now?
- Why is data being collected?
- How is data being collected?
- Where is data being collected?
- Who in the organization needs information?
- How does information help daily operations?
Then we try to combine data sources into a single source of truth. But sometimes we’ll also make suggestions for other and new data sources. Once we have the data sources complete, we can work our magic. The task now is to create the richest possible insights. In doing so, we start from our client’s daily operations. We create analysis tools and dashboards that are as clear as daylight in order to make the best possible decisions.
Developing your own intelligence
With data science, while retaining ownership of data and technology, you can develop your own business intelligence. For example, data science can help you make the right trade-offs between SEO and SEA. Or determining the value of customer segments.
If you want to read more about what our Data Science Machine can do, find out here.
