As a leading provider of data-related services to our many industry-leading and blue-chip clients, we sometimes have to explain the difference between our various data-related capabilities. Aptude does a lot of high-profile data projects, which means we’ve developed breadth and depth of capabilities.
If you’re looking for a clear return on investment (ROI) from your data projects, it’s important to know what kind of data project you actually need to get the benefits you want. Almost all data science projects will result in ROI of some sort; that benefit could be due to decreased errors thanks to clearer data or it could be better marketing spend as a result of cleaner, more accurate data. Or even cost savings as a result of real-time routing and pricing.
However, in order to realize these benefits, it follows that you as a business leader and decision-maker must know what you want to achieve first. In this blog, we’ll clearly delineate between different data activities you could invest in. Finally, we’ll discuss how to determine which of these you need to start with for your next data-driven project.
The Problem with Lumping All Data Activities Together
But first — what is the problem with lumping all the data activities together?
Data is data is data, right?
Not so fast.
While it’s possible to get ROI out of any extra attention to your data quality and visualization, in order to get maximum results, it helps to know what kind of results you want… and then work backward to determine which data activities might get you there.
Let’s say, for example, you’re a transportation company that wants to implement more dynamic routing using artificial intelligence and machine learning. In order to make this happen, your data team will need to coordinate with multiple teams within your organization to coordinate and determine:
- Your current routing and pricing processes versus your ideal future state
- Your organization’s ideal future state processes using data-driven technologies
- Your current pricing rules and error rates
- Your routing rules and error rates
- The cost in lost time, lost productivity, or lost revenue as a result of errors
- Available vs needed data sets
- Current data sources and quality
- The algorithms needed to create dynamic routing and pricing
- The data sets needed to implement and test the algorithms
And that’s just a fraction of what you’ll need to implement such a project.
To move forward with these tasks, can you easily determine if you need teams which can do what?
- Data Engineering
- Data analytics
- Data Science
- Predictive Analytics
In this blog, we’ll discuss each of these elements of data science so you understand why specialists in one area are not interchangeable with specialists in another.
What is Data Engineering
The first segment to explore is data engineering.
At Aptude, we define Data Engineering as the capabilities involved in making data ready to be used by data analysts and data scientists. Data engineering involves:
- Strategizing and creating “data dictionaries” which can become a reference for what data means, how it’s related to other data, its usage, and its format
- “Cleaning” data so it can be standardized across data sources and trusted when it’s used in visualizations and algorithms
- Transforming data lakes to data warehouses
- Importing data from unstructured sources and transforming the data into a structured, standardized format
- Creating “data pipelines” that consolidates data from multiple sources and makes it available for data analytics and visualization
Data engineers come with a variety of job titles, such as:
- Hadoop Developer
- BI Developer
- Quantitative Data Engineer
- Search Engineer
- Technical Architect
- Big Data Analyst
- Solutions Architect
- Data Warehouse Engineer
- Data Science Software Engineer
- ETL Developer
- Data Architect
- Computer Vision Engineer
- Machine Learning Engineer
- Business Intelligence Engineer
- Big Data Engineer
- Data Quality Specialist
At Aptude, our advanced data science initiatives such as Machine Learning, Artificial Intelligence, and Predictive Analytics projects are dependent on the work of the data engineering team.
What is Data Analytics
The second data science area to explore is data analytics.
At Aptude, our data analytics group involves both data visualization and true data analytics. Data visualization is the process of representing data in a visual format, as you can see in our dashboards: