In a world awash with data, true gems of knowledge are seldom discovered through surface-level digging; proverbial data “mines” are hard-won and must be actively sought out.
And as data mining is concerned with finding meaningful patterns in data — a task unlikely to be accomplished through manual efforts — data mining tools serve as the heavy lifters in the transformation of data into information.
The current data mining software landscape provides some crucial insights into data mining prevalence and adoption across industries: according to analyst predictions, the global data mining tools market will increase from $552.1 million in 2018 to $1.31 billion by 2026, at a CAGR of 11.42% between 2019 and 2026. This rapid growth reflects the rise in both enterprise and SME demand for solutions in the data mining software tools category.
Software tools aside, data mining predates the arrival of computers by at least several hundred years. The ability to find patterns in vast quantities of data has been a centuries-long endeavor for modern societies — even contemporary data mining methods incorporating the Bayes Theorem trace their roots to the 1700s. The 1800s saw the emergence and evolution of regression analysis; a century later, some of the more advanced techniques, such as clustering, decision trees, and support vector machines, would make their way onto the scene with modern computer science.
These days, data mining as a process under the larger data science umbrella is also referred to as knowledge discovery in data (KDD), reflecting the distinction between data and knowledge — a key theme among today’s data science aficionados. The large data sets of today and tomorrow not only require sophisticated software tools for extracting meaning, they also need proper contextualization for value creation in the relevant verticals.
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5 Trends in Data Mining
Data mining specialization per vertical is just one of a few rising trends in data mining that data science professionals should be aware of. Here are 5 more rising trends in data mining to keep an eye on:
1. Data Mining Dominance in the Pharmaceutical and Health Care Industries
Both the pharmaceutical and health care industries have long been innovators in the category of data mining. In fact, the recent rapid development of coronavirus vaccines is directly attributed to advances in data mining techniques for pharmaceutical testing, more specifically — in signal detection during the clinical trial process for new drugs. In health care, specialized data mining techniques are being used to analyze DNA sequences for creating custom therapies, make better informed diagnoses, and more.
2. Increasing Automation in Data Mining
Earlier incarnations of data mining involved manual coding by specialists with a deep background in statistics and programming. Modern techniques are highly automated, with AI/ML replacing most of these previously manual processes for developing pattern-discovering algorithms. Today’s data mining solutions typically integrate ML and big data stores to provide both advanced data management functionality alongside sophisticated data analysis techniques.
3. Embedded Data Mining
Data mining features are increasingly finding their way into a myriad of enterprise software use cases, from sales forecasting in CRM SaaS platforms to cyber threat detection in intrusion detection/prevention systems. The embedding of data mining into vertical market software applications enables prediction capabilities for any number of industries and opens up new realms of possibilities for unique value creation.
4. Rise of Spatial and Geographic Data Mining
With the new space race currently underway, more focus than ever has been placed on data mining for a myriad of commercial space-related use cases: zero-gravity cancer research, spacecraft design/testing, and — appropriately enough — asteroid mining, among others. Back on Earth, spatial and geographic data mining have already become fixtures of life through geographic information system (GIS) offerings, such as GPS-powered navigation and Google Maps.
5. Data Mining Vendor Consolidation
If history is any indication, significant product consolidation in the data mining space is imminent as larger database vendors acquire data mining tooling startups to augment their offerings with new features. The current, fragmented market and broad range of players in the data mining arena resembles the adjacent big data vendor landscape — one that continues to undergo consolidation.
Once relegated to the cubicles of statisticians and number crunchers, data mining is garnering expanded interest across different functional roles (e.g., developers vis-a-vis data mining APIs, niche users via specialized data mining apps) and industries. In terms of data mining tools, advances in AI/ML-based pattern detection coupled with vertical-specific feature sets are fueling new value creation in industries not traditionally involved with data mining. All these trends make this particular corner of data science one to keep an eye on in the coming months and years.