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
  • QA/Testing

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:

The limit of the data visualization is that it’s only as good as the data set the visualizations are built on. Data analytics, on the other hand, goes a step further than data visualization: data analytics looks at the connections between and within data sets to find patterns.

Because the two are so similar, are Data Analytics group includes both pattern-finding and visualization tasks. For example, we have one client who relies on our Data Visualization team to:

  • Meet with stakeholders to hear their data visualization and reporting needs and document the ask
  • Determine which data pipelines are available, and if the existing pipelines could serve the need
  • Work with the Data Engineering team to create new pipelines as needed
  • Translate this ask into a standardized report and visualization which can be pulled in the future

And that’s just one use case for our highly skilled data analytics team.

Data Analytics jobs come in a variety of job titles, such as:

  • Data Scientist
  • Data Analyst
  • Business Intelligence Analyst
  • Business Intelligence Specialist
  • Business Intelligence Consultant
  • Intelligence Analyst
  • Consultant (Analytics)
  • Big Data Software Developer
  • Quantitative analyst
  • Marketing analyst
  • Transportation logistics specialists

At Aptude, our data analytics team is a fast-moving, highly capable team able to transform data into highly actionable, easy-to-understand visualizations and dashboards using tools such as Power BI and Tableau.

What is Data Science

The third area to explore is data science.

Data science is, according to Wikipedia, “an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.”

At Aptude, our data science team includes capabilities such as:

  • Algorithm development
  • Machine Learning
  • Artificial Intelligence
  • Predictive Analytics
  • Big Data
  • Natural Language Processing
  • Statistics
  • Hadoop, Python, and R

As a result, we can help with all of the advanced data initiatives that are possible once your data is cleaned, standardized, and put into data pipelines so they can be used.

Take our aforementioned example of a transportation company wanting to develop dynamic routing and pricing of loads: our data science team is the one which would design, develop, implement, and optimize the final result alongside the Data Engineering team.

How to Determine Your Next Steps

While an extensive data initiatives would likely include all of the aforementioned data capabilities, that’s probably not where you’ll want to start.

Here are some questions to ask your team:

  • How siloed is our data?
  • How clean is our data?
  • Do we have a large enough data set for the initiative?
  • Do we have a clear use case?
  • Which parts of the project can our internal team handle now?
  • What kind of ROI are we looking for?
  • Do we know which area we might need more urgently than others?
  • Do we really just need visualizations first before we try ML?

If you’d like some expert helping in figuring out where to start and what you need in terms of data, manpower, tools, and budget, we can help. Many of our projects involve data-related initiatives, especially since we now have a Python Center of Excellence in Mexico City, Mexico.

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Remember, 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.

We’ve encapsulated the questions in this blog into a downloadable and printable worksheet for your team to reference. Fill out the form to the right to claim your free copy.