Dangers of Data Mining

Data mining is a powerful tool that can help companies in a variety of ways. From cutting costs, optimizing processes to improving sales, data mining is a game-changing tool that every company should have on their arsenal. There’s no shortage of benefits that data mining brings to organizations everywhere. But what are the dangers?

While data mining can put anyone at the forefront of every industry, it can quickly go badly for companies that don’t do it properly. From the collection process to the actual implementation, there are various entry points for the dangers of data mining. Let’s dive into each one.

Data Privacy

While data mining on its own doesn’t pose any ethical concerns, leaked data and unprotected data can cause data privacy concerns. Through the years, there have countless campaigns on stolen data that have caused an uproar in various parts of the world.

Very personal information like intimate photos, credit scores, or bank account log-in details have been leaked and caused real-life distress to users. People can lose reputations, their life savings, and maybe even their peace of mind in the process.

Ethical Dilemmas

As big data creates a better view of who people are and what they want, it begs the bigger question of whether or not monetizing sensitive data is ethical.

By accessing personal records to exploit people in the name of profit, we can blur the boundaries between what is acceptable or not.

Information such as medical records, location tracking, or even search history, used to manipulate users into buying things that are not clinically proven, they don’t need or cannot afford raises a lot of pertinent questions.

Inaccurate Data

At any given time, there are two main kinds of data available to data miners– bad data and good data. Unfortunately, the internet is rife with the former more than the latter. When companies don’t sift through data properly, they’re prone to using incomplete, duplicated, or outdated data.

Companies can be stuck with a half-baked analysis that won’t add value to their businesses and unnecessarily waste a ton of money in the process.

Overvaluing the Output

While data can help make decisions, it’s also not everything. When it comes to things like management or leadership, the most useful data can sometimes be the unstructured ones.

Not every great decision can be attributed to data, especially when the algorithms behind the process are not yet as refined. As time passes and algorithms get smarter, it may become more reliable.

To get the most of data mining, data miners need to know the difference between the actual business and data. Data gives a picture of a business, but it’s never the whole picture. To get the most out of data mining, it needs to be in collaboration with business units that can serve as a check and balance.

Data miners also have to remember that behind the 1’s and 0’s, there are actual people who will be affected by their recommendations.

Data Warehousing Fundamentals

Data Warehouses have long been a part of company standard operating procedures. However, as time passes, companies need more complex systems that can handle increasing amounts of data.

The main differentiator between the various stages varies in terms of data accuracy, triggers, and interactions between the operational systems and storage mechanisms. Between industries, there are different levels of sophistication required. They each have varying fundamental warehousing needs that affect workflow and customer experience.

Depending on the size, age, and industry of the company, Data Warehouses will be within one of the four stages or in a point of transition. So, where does your company stand?

4 Stages of Data Warehouses

Stage 1: Offline Database

In their most early stages, many companies have Data Bases. The data is forwarded from the day-to-day operational systems to an external server for storage. Unless extrapolated and manually analyzed, this data sits where it is and does not impact ongoing business functions. Transactions such as loading or processing of data have no effects on an operational standpoint.

Stage 2: Offline Data Warehouse

While not entirely up-to-date, offline Data Warehouses regularly update their content from existing operational systems. By emphasizing reporting-oriented data structures, the organized data meets the particular objectives of the Data Warehouse.

Stage 3: Real-time Data Warehouse

Real-Time Data Warehouses gathers information through operational system events-based triggers. Often, these come in the form of transactions such as airline bookings or bank balances.

Stage 4: Integrated Data Warehouse

Daily activities to be passed back to the operating system continuously in the Integrated Data Warehouse. Integrated Data Warehouses are the ideal Data Warehouse stage with the data not just readily available but also updated and accurate.

It’s pertinent to understand what stage your company’s Data Warehousing facilities are before you can improve them. The journey to creating the best version of your Data Warehouse takes time to set up and even more to integrate into your company’s workflow.

Companies need to invest time to transition their Data Warehouses to more efficient stages as their organizations become larger and more complex. Often, there needs to be regular training towards different business units to maintain the accuracy of the data.

Data Warehousing is not an overnight process and requires a long term strategy for both integration, adoption, and maintenance. There must be procedures in place to avoid fraudulent, obsolete, and incorrect data.

Companies must also be on high alert for possible security breaches that may affect the trust that customers place on the brand concerning their personal data. As the data compiled becomes increasingly granular, it becomes important to protect it from malicious intent.

Despite its many challenges, developing a good Data Warehouse is still a necessary investment that should be in every company’s roadmap. Having a working Data Warehouse is the first step to creating efficient Data Mining workflows. There’s no doubt that having a good Data Warehouse will be the standard for companies that want to get ahead and stay ahead of the competition.

What is a Data Warehouse?

 

A combination of different types of strategic data aids, Data Warehousing (DWH) is a process that collects and manages data within a system. More often than not, Data Warehouses gather data from multiple sources while acting as a core designed for analysis and generating reports. Data Warehousing helps business leaders make better decisions, faster.

Data Warehouses are not particular products but an entire ecosystem. They allow users to find past and present information in a more organized manner compared to traditional operational databases. A well-designed data warehouse can access information faster and create better reporting processes.

Data Warehouse Data Structures

Data Warehouses take information from various sources and acts as a repository. There are three types of data in Data Warehouses: structured, semi-structured, and unstructured.

Before users can make use of the data through Business Intelligence tools, it first has to be processed through Data Mining. Data Mining looks for meaningful patterns from the Data Warehouse to create a holistic view of the business and give better recommendations to relevant business units.

Types of Data Warehouses

There are three main types of Data Warehouses: Enterprise Data Warehouse, Operational Data Store, and Data Mart.

The Enterprise Data Warehouse (EDW) is a business’ central data warehouse that classifies data while giving access to the right users. While Operational Data Stores (ODS) are real-time data often used for routine activities, and Data Mart is Data Warehouses made for specific business units.

What are Data Warehouses used for?

Data Warehouses are used by businesses to sort through data from various sources and organize them by context to become usable. Data Miners use them as a preliminary step to find a meaningful pattern.

Many industries regularly use Data Warehousing to improve their operations and data mining practices. From retail chains looking to track items and manage inventory to airlines that need to keep track of repeat customers and route profits, there are infinite ways that Data Warehousing aids businesses from all over the world.

The Struggles of Implementing Data Warehouses

Data Warehousing is not without issues. Data Mining becomes increasingly common, so are the restrictions that come with the amount of data that companies are allowed to store.

The beginning stages of building a Data Warehouse can also be an overwhelming process for companies that did have a good data management foundation. It can be time-consuming and require additional training to adjust people to their usage.

Many large companies also struggle with organizing a large amount of complex data that may not find in the existing cloud storage solutions and may need physical storage that entails additional maintenance, hiring of personnel, and use of space.

Conclusion

While Data Warehousing may seem like a resource-intensive process, it’s an investment that will be useful for years to come. It can help users efficiently access critical data, and integrate systems that would otherwise be a pain to consolidate.

Overall, Data Warehousing should be standard practice and creates a good foundation for Data Mining.

6 Best Free Data Mining Tools (in 2020)

Data Mining is an incredible tool that can be used by businesses to turn large amounts of data into actionable information. Through different data mining techniques, data miners put a structure that enables companies to make better decisions every time.

Through the years, many data mining tools have come and gone. With many companies adapting to the data-driven world, there has been a steady rise of tools built to help them. While some need a subscription or a one-time payment, there are still some available tools that are free online.

In fact, among free alternatives to data mining tools, they have unique offerings that also include data warehouses and knowledge discovery mechanisms. So if you’re looking for free alternatives to expensive mining plans, here are few to consider:

Weka

Developed by the University of Waikato, this machine learning software is best for data analysis and predictive modeling. From data mining to regression, it works best when data is clean and readily available. Through various visualization tools, it can also be helpful with machine learning.

Revolution

R is a commonly used data mining tool by academic researches, engineers, and industrial companies. It is an interactive platform that can perform intricate statistical calculations despite being very user-friendly. It’s known to have great visualization tools and is very comprehensive.

Qlik

An easy to use data mining tool, Qlik allows users to analyze data from multiple sources all at once. With its unique drag-and-drop features, it can easily visualize data and make instant changes. Data security is a highlight feature of this tool. Qlik has sharing options that can let users access reports easily.

Uses of Data Mining

Rapid Miner

Using JAVA, this tool creates insightful predictive analysis and flow-based programming. It’s often used for training and business applications because it doesn’t require extensive coding experience to use.

Orange

A component-based machine learning software, Orange works with both scripts and ETL workflows. It has an exhaustive list of algorithms and various widgets that can use do data analysis and visualization. While reporting can be limited, it is one of the most simple tools to operate using Python.

TeraData

Best used by organizations that have most of their data already migrated to the cloud, TeraData has a range of data mining optimizing tools for every stage of the business. Supported by SQL, TeraData lets users distribute data without too much intervention.

Through the years, data mining has evolved. In fact, it will continuously grow and improve in the years to come. It will only get more granular, efficient, and effective with its analysis. To get ahead of the curve, one must be able to select the best tool and customize it to your company’s particular needs.

With the right tools, data miners can help businesses make better decisions in every aspect of the business. From lessening costs, improving products to increasing profit, there is a room of data science to make the kind of changes that will help companies flourish.

5 Data Mining Steps

Data Mining is a process that requires many steps. While many companies have data, not all of them are usable or necessary for the particular project objective involved. Here’s are the 5 Steps to Data Mining that you should know about:

Project Goal Setting

For anything to succeed, it has to have a plan. Goal setting is the foundation of every successful data mining project. Through aligning on their project objectives and timelines, business and data mining teams can have a smoother working relationship throughout the experience.

Goal setting allows teams to assign roles and make a clear plan to move forward. Expectation management is key to avoiding issues throughout the data mining process.

Data Gathering & Preparation

For every good kind of data, there is a mountain of bad data. From incomplete, fraudulent to out of date, bad data is everywhere. When not cleaned, it can ruin any campaign. The data gathering and preparation stage is all about making sure that the data is usable.

For larger, more established clients, there must be mitigation of security risk. Trust is a necessary element when dealing with sensitive information. Data processing often uses modern database management systems (DBMS) to improve data mining speed. It is also a primary precaution when dealing with data that is confidential to an organization.

Read about our Bad Data Series, what to avoid and how to provide clean data for your CRM

Data Modeling

With the use of mathematical models and various data visualization tools, there are meaningful patterns discovered in the data. Through conceptual representations of how data objects and rules go hand in hand, they form a Database.

A Database can be conceptual, physical, or logical, depending on the Data Model applied. With the right structure, it can help define relational tables, keys, and procedures. For Data Modeling to work, it needs to have quality data, security procedures, consistent semantics, default values, and naming conventions. There are two types of Data Modeling Techniques: Entity-Relationship (E-R) Model & Unified Modeling Language (UML).

 

Data Analysis

After the modeled data is analyzed, it is then extracted, transformed, and visualized. Data analysis helps bring together useful information to give insights or test hypotheses.

With a combination of business intelligence and analytics models, Data Analysis orders raw data in a way that is relevant to the project goals. Armed with visual representations and insight on previously unrefined data, it is then ready for deployment towards relevant business units.

 

Deployment

In the last stage of Data Mining, relevant partners test the hypothesis. There are four different types of model deployment: data science tools, programming language, database, and SQL script or predictive model markup language.

 

Mined data provides a single source of truth that can guide business decisions moving forward.With coordination between data scientists, IT teams, software developments, and business professionals work together to integrate the new models with the existing production system of an organization. Companies such as Hey DAN are experience and well organized in handling professional data mining.

3 Types of Data Mining Uses

When it comes to big organizations, there is a mountain of decisions at any given time.

Many companies only have a limited amount of resources. They do not always have the means to monitor every single one at the same standard. It can open a myriad of potential issues like delays and errors that stem from human judgment.

With Data Mining, organizations can delegate many aspects of the decision-making of routine without compromising results. Through various algorithms, models can not only collect data, but they can also analyze them.

From critical decisions to automated processes, Data Mining streamlines both issues and opportunities to increase an organization’s overall productivity.

Read about our Bad CRM Series and understand the advantages of having accurate and clean CRM Data

3 Types of Data Mining Uses

There are many advantages and uses for Data Mining for every kind of business. Here are a few ways Data Mining can be used to help yours:

Forecasting

Every success starts with a plan. In a hyper-competitive landscape, data-driven planning means all the difference between falling behind or staying on top. It can provide teams from all ranks with the necessary information that can keep them ahead.

Demand forecasting models take note of what has done right – past trends and current conditions. Then, Data Mining can be used to predict what to do better. From pricing strategies to inventory management, it saves a lot of wastage and maximizes the use of resources.

Data mining helps identify opportunities to improve processes, procedures, and experiences.

Cost Reduction

With better forecasting, companies save valuable resources. Data mining not only helps make better decisions but get the best results with the least cost. It keeps companies from investing in the wrong things, hiring people at the wrong time, or selling to customers who can’t afford their offers.

Whether it’s to prevent spoilage from over-ordering ingredients for a restaurant chain, retail stores running out of stocks, Data Mining can help reduce cost and increase sales.

Customer Insights

As consumers spend an increasing amount of time, so does the data that comes with their usage. With every virtual touchpoint, companies have access to a treasure trove of data on former or prospective customers.

However, having a ton of data is not always useful when they are not usable. There are more kinds of bad data than there are good. Data Mining involves taking a needle out of a haystack and finding the right data needed to make good decisions.

Data Mining helps create a holistic view of customers by using models to create a more accurate user persona. It is a primary component to personalize each person’s experience with the brand.

With increasingly short attention spans and a global marketplace to compete with, knowing a customer is a key to winning their attention and share of wallet. From serving timely ads to creating relevant products and services, data mining is a secret recipe for every great company’s success.

It’s no secret that we’ve only begun to scratch the surface on what Data Mining can really do, but there’s one thing that we do know – it’s here to stay.

What is Data Mining

Data mining is the process by which companies learn more about their customers through actionable patterns from their analyzing their data. Most data science fields deal with understanding historical data; data mining deals with more predictive analytics that tries to predict future ones.

Data Mining Uses

While data mining might seem intimidating to a lot of users, it’s present in a lot of features that we already interact with and are necessary to run our business systems today.

Companies use data mining in their marketing efforts; banks use it for their credit scoring and fraud detection. Many medical professionals and scientists use it for health predictions. Customers experience it through e-commerce recommendations, email spam detection, and search engine rankings.

When used ethically, data mining is a tool that can make sure that businesses stay relevant now and in the future.

Data Mining Benefits

Data mining helps companies make better decisions. From managing inventory, predicting volume to gauging pricing, companies can better adapt to their customer’s needs even before the customers know it themselves. With better predictive analytics, many retailers can predict things that can help with understanding their customers on a deeper level.

Customers will also get a better experience with being served content that is relevant to them and their intended activity. Customers need to feel that their customer experience is tailor-fit to their needs, whether it’s through personalized ads, useful recommendations, and products that solve real problems.

From companies to customers, data mining is integral to improving experiences for everyone.

Issues with Data Mining

Data mining is a gold mine, but only when you know where to look. Unfortunately, not all data are useful data; bad data, in the form of incorrect, incomplete, or fraudulent data, is more common than most people think.

Companies need to integrate data management systems that make sure that the information supplied is up-to-date, accurate, and complete. It’s also possible to over-fit, wherein the prediction may be correct for the sample that the model derived from but not the actual population. Variables are tricky because having too little or too much can skew the levels of accuracy.

Having a lot of data can also unlock a variety of issues like data privacy and theft concerns. Companies need to be careful with the protection and storage of their customer data from attackers with malicious intent such as spam, phishing, and identity theft.

Maximizing Data Mining

For data mining to work, it should be part of a holistic strategy that knows how to use it well. After enriching artificial intelligence techniques and complement machine learning models, companies should use the predictions to improve their processes, products, and services.

Data mining is a powerful tool that should be in every company’s arsenal. Customers are increasingly demanding more from brands, and companies need to respond accordingly to stay viable. While it may seem costly and overwhelming at first, investing in it can mean the difference between your company still being profitable a few years down the line.

Data Mining and Privacy

We all have a Kramer* in our lives –  that nosy neighbor that asks the personal questions or conveniently drops in uninvited for the football game and leeches on the six-pack and pretzels. Most of us feel the same way about Kramer and data mining, in that we think that both are invasive to the point that they undermine our privacy. But unlike our uninvited house guest, data mining actually has ethics.

Getting Personal

The general notion about data mining is that, the consumer’s personal information is being unscrupulously collected and sold without their knowledge or consent. Some have even thrown out the idea that companies collect intimate data, like photos from customer’s social media accounts. This is very far from the truth. In reality, all your data is yours to keep.


The Boogeyman doesn’t need your vacation photos or cat videos. Customers are always given a choice of either accepting or opting out of data collection. This is usually in the form of a data privacy statement that you can either choose to accept or not. Always read this. Even though most companies are very clear on how they use your data, there are still those who lack ethical concern and may choose to use your information otherwise. These companies run the risk of backlash from consumers and the government just to gain a competitive edge from their rivals. Ethical companies on the other hand, only collect certain data points that would help them in product development and improve customer experience. So in the end, privacy still rests on the hand of the consumer. Read and understand these privacy agreements, and steer away from those that have vague policies regarding the privacy of your information.

Speak and you shall find

Voice technology coupled with data mining can reap massive benefits for businesses. It can capture more usable information more accurately, resulting in richer data sets that helps you better understand your customer’s needs. More defined customer behavior produces better targeted advertising, better products, reduces production cost and helps you build a more tailor-fit customer experience.

For Better or Worse

Privacy is a touchy subject and rightfully so. Big businesses must be more transparent regarding their data collection methods and usage. Governments must work in tandem with businesses in protecting the consumers right to privacy. On the flipside, consumers must be well-informed of the consequences of sharing their information with companies. In the end, good companies turn out a profit the right way, while shady ones will not care about your data as long as they gain even the slightest edge against their competitors. Would it be worth it to gain a small advantage but lose your credibility in the process?  Even Kramer can answer that question.

* Cosmo Kramer, usually referred to as simply “Kramer”, is a fictional character on the American television sitcom Seinfeld, played by Michael Richards. (https://en.wikipedia.org/wiki/Cosmo_Kramer)

The Information Gold Rush

When you hear the word “mining”, the mind often conjures images of people in hard hats and carts filled with ore under a hot dimly-lit mine. It’s a cumbersome, yet rewarding process that involves a lot of digging and sifting, and digging, and extracting and dig… – you get the picture. Data mining shares a lot of similarities, but instead of tunneling through a mountain or burrowing beneath the ground, analysts excavate and examine heaps of information stored in data warehouses and process it into meaningful reports for the end-user, like sales teams and managers.

The Process: A Quick Run-Through

Data mining starts with identifying data source. Let’s take your CRM database as an example. The information is housed in a data warehouse, which could be a local server or a cloud-based solution. This raw data is then organized and run for analysis to search for different patterns like purchasing behavior, web searches, social media interactions and so forth. The output is refined through a series of rule revisions and data queries – similar to processing ore in traditional mining – until the data analyst uncovers the outcome he is looking for. From there, the sales team or managers can formulate a plan of action based on the interpretation of the findings.

Get with the program

So you want to integrate data mining into your system. It’s not as difficult as it may seem. You need not look any further than your current CRM. Most popular CRM packages offer data mining add-ons or may have third party solutions already developed for it. You may be pleasantly surprised that the feature you’ve been wanting to add existed all along, right under the hood of your current system.  Hubspot is a perfect CRM system that enables you to perform data mining capabilities with partners that are quite experienced and capable. With evolving  This ensures that your system grows in tandem with the needs of your business, so you wouldn’t need to make a drastic shift to another platform.

Conclusion

When implemented correctly, data mining capabilities can effectively move your business forward to the next level. That is why covering all the bases with a solid program is the next logical step to any growing operation. The insight gained from data mining can be tremendous for your business. It can save you money on costs, increase your ROI, and most importantly, keep those smiles stuck on your customers faces. After all, a deep knowledge of your client base will do your business no harm – you just have to keep on digging.