Every business wants to be ready for future events before they happen, yet many companies still don’t organize their data. The ones that do organize it might not have methods in place for ensuring data integrity, either.
As Devsu’s own Senior Data Engineer, Hiansen, puts it: “The biggest problem we have working with data is that people start working before they understand the business and the data problem.”
Avoid this common pitfall, and capitalize on the data collected for your marketing strategies! Start with our helpful blog on making the most of your data sets, then get any resources and personnel you need to make your plans a reality.
Data Quality 101
It’s understood that a company’s data has value, usually going untapped. But it’s not as simple as saying all data is important. Some information is more important than others!
Let’s say your business is a ship. If the hull has weak spots, you’ll want to know about that before hearing it needs a coat of paint. But to notice these critical weak spots in your business, you need to analyze your data and assess what’s most important.
That’s what the six dimensions of data quality are for: to help you be ACCTUV in your data development.
These sometimes go by other names–you might see “timeliness” called “freshness”–but the principles are generally similar.
Core Aspects of Data Quality
In layman’s terms, Hiansen summarized the steps for maintaining high data quality. “The core thing to have is a good team to manage all this data, transforming everything as you need to: clean your data, format your data, remove all strange data.”
- Data cleansing
In the case of Janet B. Smith and Jane Betty Smith in the table above, you would clean up data by comparing the other information on these two customers. Are their emails different? Addresses? Birthdays? If these all match, you know they’re the same person. This means you can merge their profiles and clean up a duplicate account for more accurate data.
- Formatting data
Using the birthday example, you know that 30/04/1990 is April 30th, 1990. By having this match your company’s format for dates, you can more easily and precisely analyze this data across customers. A consistent template means streamlined data development, so your business can keep moving forward.
- Handling strange data
You might wonder about what “strange data” refers to. The simple version is that, as with anything, human error is a factor. If someone put in that they were born in 1690, you know that’s definitely not correct. This is information that doesn’t need to be archived or analyzed, because it’s obviously not true.
Painting a Picture with Data Visualization
Collecting data and maintaining its quality makes a solid start, and analyzing it all with a rockstar team of data developers gets the ball rolling. But that only helps if you and your team understand what it means for your business.
That’s what data visualization is for! Hiansen described the practice as “the best way to make data interactive.” And what’s better for employee engagement than interactive business intelligence?
Data analytics takes reports and presentations beyond pie charts and bar graphs. Look over different types of statistical models below to see the possibilities. (It should be said that these are also machine learning models, and we’ll go into more detail on the uses of machine learning shortly.)
Regression models
The main goal of this kind of model is to demonstrate how variables affect each other. Seeing which independent variables have the most impact on dependent variables, businesses have better control over the direction of their company.
- Types of regression model visuals: Line plot graphs or tables showing logistic, linear, polynomial, stepwise, ridge, lasso, or elastic net regression
Classification models
Using an algorithm, this type of statistical model analyzes a given data set to classify that information. Because the data analyst looks over the analyzed information, this is a kind of supervised machine learning.
- Types of classification model visuals: Naive Bayes, decision trees, nearest neighbor, and random forests
Would you believe data visualization gets even more advanced than this? “If you want to have smarter data visualizations,” Hiansen explained, “machine learning would be a nice way to put intelligence in your visualization.”
Machine Learning Applications
While we’re on the topic of machine learning and data development, let’s talk real-time data reporting and in-depth analytics.
As Hiansen said, “You can find anything in data by using the right technologies.” Here are just a few applications of machine learning to show how these technologies can work for you.
Predictive Analytics
Product development sees a lot of growth from predictive analytics, and machine learning is an integral part of that. You can tailor visualizations to your business as well, either by providing the predictions for a clean presentation or including the insights that led to the predictions for a thorough report. A robust analytic approach will also include ad-hoc statistical analysis, predictive modeling, data mining and optimization, and other methodologies.
Prescriptive Analytics
Built on predictive analytics, this kind of analysis paves the way to solutions and new opportunities within your business. Because machine learning is the foundation of prescriptive analysis, it can process large, complex data sets for informed business strategies. Data-driven decisions come more easily and quickly with predictions mapped out with suggested actions.
Neural Networks
While neural networks were originally made to reflect the way the human brain thinks, the industry soon found new ways to expand on its abilities. By choosing neural networks for dedicated tasks, from speech/facial recognition to construction management, we can see greater results in a fraction of the time.
Where data development is concerned, neural networks let companies process huge amounts of structured and unstructured data. Deep learning was the natural next step for using neural networks, since it’s basically layers of these networks designed to collect and mine larger amounts of data–and big data as well. Using neural network techniques, qualified AI engineers can develop deep learning systems that can process big data sets while also including a wide range of variables.
“You can generate dashboards and reports with no intelligence,” as Hiansen pointed out, “but if you have a nice team of data scientists and they know how to use machine learning and other artificial intelligence, you can generate smarter reviews and reports.”
Neural networks and deep learning are two methods that make more intelligent reporting possible. When managed by people with the right skills, these can provide versatile and current data to help teams in your company make informed choices for the business.
Discover Opportunities in Data
Data governance and development are vital aspects of business that often fall through the cracks while the day-to-day tasks of doing business take center stage. This also means they’re an exceptional opportunity to set yourself apart from your competition. Your customer data has a story to tell, and data developers know the techniques and technologies to help you uncover it.
“The data of a company can be like a rhythm of what’s happening in the company,” Hiansen discussed the uses of collected information. “You can’t do good work if you don’t deeply understand what you need to achieve and what you’re really trying to fix.”
The real-world applications for data development are endless, but it all begins with taking those first steps. Whether you’ve never organized the data for your company or you’re ready for data migration to an upgraded system, get a quote today and learn how data developers can ignite growth for your business.