By Rachel Lee - August 24, 2021
Businesses are capturing data at an unprecedented rate and simultaneously struggling to turn that data into useful information that drives business decisions. In fact, a recent Gartner survey showed that only 23 percent of senior-level marketing leaders are satisfied with their own investments in data analytics, even while the majority are deciding to scale up those investments.
It speaks to the optimism surrounding data analysis as a competitive advantage, along with the maddening reality that the benefits are elusive to many.
Good news: You don’t need a computer science degree to get a grasp on data analysis. Today, with the help of artificial intelligence and machine-learning algorithms, there are plenty of big data tools on the market offering sophisticated forms of data analysis.
But knowing the basics of what these tools promise to do can make all the difference in getting the right results for your business.
A computer scientist named Gregory Piatetsky-Shapiro coined the term “Knowledge Discovery in Databases” or KDD in 1989, to describe the growing field of looking at the process of extracting useful knowledge from data (what we broadly refer to today as data analysis).
The term later caught on in the artificial intelligence community. But others deployed another term, data mining, which the press used interchangeably, resulting in a messy lexicon over the years.
So first, let’s clear up those definitions.
Humans and computers find hidden patterns in data by applying algorithms to large, structured datasets. That is data mining. Data mining is just one step in data analysis, or turning data into knowledge, but it’s also one of the most critical stages.
Artificial intelligence increasingly augments data mining so decision-makers can keep up with the amount of data being analyzed in today’s businesses.
Machine learning—the ability of AI systems to self-learn and autonomously improve their algorithms over time—has expanded the ways we can synthesize data to perform pattern recognition more efficiently, far quicker than human intelligence is capable of.
Deep learning, a subset of machine learning involving neural networks (computer systems modeled on the human brain), vastly improves our ability to continually monitor data with AI to prevent bottlenecks in your data analytics.
Data mining is a subset of data analysis and lays the foundation for more advanced business processes involving data, such as predictive analytics.
Piatetsky-Shapiro included the word “knowledge” in his coinage of KDD for a reason: He felt that data mining implied a process that was rote, pejorative, and unglamorous with “no indication of what we are mining for”.
The unifying goal of data analysis is ultimately discovering knowledge in data, which is a broader goal than finding patterns in data warehouses. Thus, data analysis encompasses several steps that come before and after data mining.
For example, data analysis includes the processes of data collection and data profiling, which is the initial discovery phase of surveying datasets for inconsistencies, high-level observations, and anything else that could lead to hypotheses.
Structuring data within an organized system like Python is also part of data analysis that is a prerequisite to effective data mining. Data analysis also entails data visualizations, which are key to human comprehension of raw data and set the stage for higher-level insights.
After finding patterns through data mining, additional stages of data analysis help further transform patterns into knowledge. These include advanced analytics systems and business intelligence engines.
Although data mining isn’t the preliminary step of data analysis, it identifies the patterns and trends that will be the building blocks of solving more advanced problems with your data, such as optimizing consumer advertising and growing productivity in the workforce.
Marketing professionals use data mining techniques to influence strategy around consumer behavior all the time. They use data mining to monitor social media mentions of a product and then categorize consumers based on engagement levels with promotional campaigns. Often, they’ll then feed these consumer-behavior trends into predictive analytics systems to determine a course of action for the future that better engages with those consumers.
Data mining tools are also used throughout healthcare. For example, algorithms periodically perform data mining on patient hospitalization records, resulting in reports to administrators covering recent trends.
To go one step farther, data analysts and data scientists may then combine those insights with additional historical data to do regression analysis or tap prescriptive algorithms to find ways of mitigating those negative hospitalization trends.
These are just two examples of industries around the world using data mining techniques and data analysis to boost return on investment.
Sisu has the right platforms to boost your data mining and data analysis with the help of machine learning. Whether you’re trying to automate steps of your business intelligence or trying to use predictive analytics to better assess the effectiveness of promotional campaigns, you can quickly surface the insights you need with Sisu’s data engines, which bring you one-click applications of advanced data science.
Schedule a demo with Sisu today to see Sisu in action and get the facts on how customers like Samsung, Mastercard, Wayfair, and Gusto are using Sisu to get more return on their data investment.