In the realm of computer science, the term data mining is interchangeable with the term knowledge discovery in data (KDD), and for good reason. There’s a tremendous amount of valuable, actionable information hidden in the mountains of data that almost all businesses collect and warehouse. This guide explains how this raw data can be accessed and analyzed so that it can be turned into actionable information.
What Is Data Mining?
Modern data mining is the computerized, algorithmic process of searching through those massive piles of raw information and “discovering” or revealing the profitable, efficiency-enhancing knowledge that’s buried there. The process of data mining is the bedrock of any data analytics program. This already crucial aspect of information science is only going to grow more prominent as the emerging technologies of data collection and data storage improve.
What Is Data Analytics?
If data mining is the process of bringing hidden gems of information to the surface, data analytics is the method of refining that raw digital material into profitable ideas and beneficial actions.
In simple terms, data analytics is the sorting and organizing of information into related sets and subsets. Once the large amounts of bulk data are configured into manageable arrangements, the data analytics process can get underway in earnest.
Data analytics best practices involve combing over data sets to discern beneficial patterns and relationships that can be exploited to improve organizational efficiency and, ultimately, boost profits.
The data analytics process has money-making — as well as money-saving — potential. This process applies to almost all areas of operations, including the following:
- Sales
- Marketing
- Accounting, especially risks and audits
- Organic growth
- Growth through mergers, acquisitions and partnerships
- Human resources
- Inventory management
- Legal, compliance and liability
Why Is a Data Analytics Program Important?
Data science is a rapidly growing, increasingly important aspect of modern business. Organizations that neglect data analytics best practices risk being left behind by more agile competitors that know how to collect, evaluate, and utilize all the information available to them.
A professional plan of data analytics integration can yield significant benefits quickly. Quality analysis of data has an almost predictive element. As if by magic, management will know exactly which resources to deploy and where to deploy them.
Here are some key areas where data science can play an important role.
Improved Efficiency
Big problems that cause business slowdowns and inefficiencies don’t happen out of the blue. There are almost always warnings signs. But those signs are often hidden in obscure collections of confusing data.
A proper course of data analytics can spot potential problems long before any issues become actual problems, allowing management to be proactive rather than reactive. By the same measure, data mining can identify rewarding opportunities as easily as it can spot trouble on the horizon.
The proper attitude toward information science is bound to increase efficiency across the board.
Optimized Content
Every time a customer or employee opens and reads a piece of digital content, hundreds of data points are created and stored. Thanks to modern technology, managers in sales, marketing and human resources can now know what content is effective and what content goes straight to the trash. Data mining can tell a manager which emails are being opened and which are deleted, while data analysis can tell let them know who read these messages and what action (if any) was taken.
In short, data analytics allows management to customize and optimize content for maximum results.
Expansion Opportunities and New Product Development
For a manager, staying on top of data means staying on top of the industry they're working in. Being first to market with an innovative product or expanding into new geographic areas can make huge differences to the bottom line.
Sales data by product and geographic area, website traffic data, and other trends that can be revealed through data reviews help businesses make informed decisions on expansion plans, as well as product development.
Data Analytics Tools
An excellent place to start is with KnowledgeLeader’s Data Analytics and Mining Guide, a fully customizable, 24-slide Microsoft PowerPoint presentation. KnowledgeLeader subscribers can enhance and personalize the guide by adding their logos, photos, charts and illustrations and can also add, remove and edit the text as they see fit. This comprehensive guide is designed to broaden an entire organization’s understanding of data analytics and help companies develop and maintain a robust data analytics and mining process.
Here is a look at some of the highlights of this valuable tool.
Definition of Data Analytics Terms
When dealing with complex subjects like data analytics and mining, a critical first step is defining terms. The Data Analytics and Mining Guide offers straightforward, working definitions of:
- Data. What the word means to businesses.
- Data Analysis. What constitutes analysis as opposed to collection.
- Data Mining. What to look for in the information.
Reasons for Robust Data Analytics
People are generally more productive when they know why they’ve been assigned a task. Four slides in the guide are dedicated to reminding key personnel of the value of data analytics. We highlight several of the most important reasons for employing data analytics best practices, including:
- Transforming “data” into action
- Identifying risk and mitigation
- Conducting accurate, comprehensive auditing and testing
- Determining error rates and success rates
- Increasing productivity
- Finding cost savings
Methodology (Process) Overview
When it comes to optimizing organizational data, designing and implementing a process that works for a specific organization is essential for success. KnowledgeLeader's guide is a methodology template covering three broad areas:
- Planning and data request
- Opportunities
- Solutions
- Assessment (testing and auditing)
- Acquisition and validation
- Acquire data
- Load data
- Profile and validate data
- Analysis and testing
- Basic analysis
- Sharing results
The overarching goal is to uncover opportunities in a high-volume data environment and ensure the integrity of information through testing and controls. This is facilitated by making some key determinations, which is done by identifying:
- The objectives of the analysis
- The correct analysis tool or tools
- The method of data acquisition, taking transfer considerations and file formats into consideration
Presenting Findings
A vigorous program of data analytics is bound to uncover opportunities and generate money-making ideas. It may also uncover problems that need to be addressed. The final sections of KnowledgeLeader's Data Analytics and Mining Guide are devoted to the proper and appropriate presentation of findings to management, officers and other interested parties. Recommendations include:
- Confirming both positive and negative findings
- Identifying root causes and the basis of findings
- Summarizing findings in plain terms
- Preparing a report that can be backed up by facts
- Incorporating data findings into all appropriate operations
- Cleaning up the presentation by deleting unnecessary information
Wrap Up
Every company wants to reduce costs, increase earnings, grow the business and generally make better decisions. Data mining and data analysis, when done correctly, can achieve that.
The tools and training available at KnowledgeLeader make employing a customized data analytics best practices policy as easy as possible.