Turning Data into Solutions: Data Mining Best Practices and Trends

Data mining has been rapidly evolving over the past few years with the advancement of technology and the increased availability of vast amounts of data. With the rise of big data adoption and evolving data warehousing technology, the ability to transform raw data into useful knowledge and insights is in high demand. So, what exactly is data mining, how is it useful, and what skills and tools are required to do it successfully? We are here to answer some of these questions and demonstrate how D&G is uses #datamining to develop #solutions.

Sometimes referred to as knowledge discovery in data (#KDD), data mining is the science, art, and technology of analyzing large and complex bodies of data in order to discover useful patterns and trends. Its purpose is to improve organizational decision-making by providing insightful and actionable data, improve risk management by pinpointing risks early on, increase #innovation by identifying areas of opportunity and growth, and improve customer acquisition and retention by defining customer needs. Data mining is currently being used in some aspect in almost every industry. Its wide range of uses means that it is useful to everyone in some capacity. With a number of data analytics and #datavisualization tools, extracting key insights and information from large raw data sets can be done with considerable ease.

There are four main steps to data mining. The first is setting objectives for the project. This includes identifying which data is important, what problem you or your organization is trying to solve, and setting specific objectives for what the desired outcome is. The second step is gathering data and preparing that data for analysis. Once this is complete, next comes applying data mining #algorithms and using data mining tools and applications to assess and analyze the data. The final step in the process is evaluating and analyzing results.

In the applying data mining algorithms phase, there are primarily two different ways to set up the analysis. The data analyst can look at existing data and develop insights solely based on the target dataset or they can use #machinelearning algorithms to predict future outcomes from that dataset. Regardless of the technique used, both organize and filter data, extracting the most interesting information (for the purposes of what is being studied) ranging from behavioral data points, data detecting security breaches, bottlenecks, demographic information, fraud detection, or whatever other information is deemed relevant.

The methods used to assess the data mined include the use of association rules, neural networks, k-nearest neighbor (KNN), and the use of decision trees.

• Association rules emphasize finding relationships between variables in a given dataset.
• Neural networks are predominantly used in deep learning algorithms and process training data by mimicking the human brain through layers of nodes.
• K-nearest neighbor, or KNN is a non-parametric algorithm that classifies data points based on proximity and association to other data points. It assumes that similar data points will be located near each other.
• Decision trees use classification or regression methods to classify or predict outcomes based on a set of decisions.

Utilizing these tools and methods is what makes for an effective data miner. D&G supports DHS I&A, CISA, the USCG, and USTRANSCOM with data mining, data collection, and advanced #dataanalytics activities. We use an array of tools to provide actionable insights to our federal clients, ensuring that they get the most accurate, up to date, and useful information to make informed decisions. Our team utilizes many of the methods listed above to create actionable solutions.?

Recognizing the gap between the demand for advanced data mining and data analytics positions and the supply of people with the necessary skills to complete such positions, D&G encourages and supports our team members in acquiring certifications and training to improve and expand their data analytics capacity. In a rapidly changing technical environment, keeping up to date with best practices and innovative data analytics tools is essential. A forward-thinking approach is what sets D&G apart in the data mining and analytics space.

Data mining is an important tool that can help ensure long-term success for businesses. The methods and techniques for data mining are constantly evolving and are being applied to a number of different industries. Of late, we are seeing an increase in automation of data mining, embedding data mining into other software, and a consolidation of data mining vendors. The impact of these shifts in data mining practices will make themselves known in time. For now, data mining continues to be in high demand for its ability to turn data into solutions. D&G’s team is highly equipped, passionate, and brings innovative tools to provide top-tier services to our federal clients.?

SOURCES

What is Data Mining? | IBM

Current Trends & Future Scope of Data Mining | Datamation

Data Mining Tools, Techniques and Methods | University of Nevada, Reno (unr.edu)

Five Ways Big Data Can Help Your Business Succeed | Entrepreneur

What is data transformation: definition, benefits, and uses | Stitch (stitchdata.com)