Data is the new gold. If used the right way it can lower your cost and can help you make predictions and help you or your business is a great way. If mined and analyzed correctly, this is great for informed decisions and calculated risks.
Large-scale and even small-scale Businesses have a lot of data at hand. But without proper use, that data is costing you to store it. That Data unused is a liability, but if you use this to make your business better, you can turn it into an asset. With the help of Data Mining, this is possible.
Data Mining manually is not an efficient way. If you want to mine Data for cheaper and want to do it efficiently and error-free, you would need to use Data Mining tools. So, it is time to find the best Data Mining tools to accelerate your workflow and make calculated decisions for your business.
Without further ado let’s get started with which software you should try for Data Mining on your Business.
What Exactly is Data Mining?💁
Before doing Data Mining, we need to know what exactly Data Mining is. No, Data Mining doesn’t need you to dig with a shovel but it has got some similarities with traditional mining, hence the naming similarities.
Data mining is a term used for the operations such as extracting, evaluating, and searching for specific or non-specific Data. Now, this data can be presented in many forms to make it easily understandable for us mere mortals. This can be transformed by turning it into Pie-charts or any type of chart in that case. This can also be used in written or verbal forms.
Using Data mining, we can extract data using computer programs. This uses fancy technology and algorithms. This method understands patterns or minds’ meaningful information in data and formats or presents it in a form we can easily understand.
Nowadays Data Science in Computer Science is the field that deals with Data Mining. This is very relevant and very useful to companies as it can predict various aspects potentially causing more profit when there could have been a huge loss.
Data Mining is not only about finding data and analyzing data from plain text. It also involves the Management of Data and Management of Databases. The process is not a one-step process. Data Mining consists of three steps: Pre-processing, Mining, and Validation.
How To Mine Data?
As discussed, Data Mining consists of three stages. But it should be understood what kind of Data we are dealing with and what is the requirement. Now after understanding the requirements, we have to extract and use the Data accordingly.
So, let’s get to know the steps of Data Mining.
1. Data pre-processing
The steps for Mining Data start before it happens. Various things need to be followed and need to be decided before starting the actual process.
Mining Data is about finding patterns. So, before the process begins, we need to find out if the Dataset is large enough to contain them, and if the Data is useful to mine. But it can’t be too big that it can’t be mined in the timeframe and it requires so many resources.
This means we have to ensure a large volume of target datasets that can be sourced from the website or the database of the company or organization. But we are not good to go yet. We now have to clean the data and make it useful. This step is known as the process of pre-processing.
2. Data Mining
After the assembly and the step for acquiring the Data, the actual mining begins. This also has steps. There are six main steps. So, for Data Mining, we have to follow these steps, these are- Anomaly detection, Dependency modeling, Clustering, Classification, Regression, and Summarization.
Let’s go through these steps one by one:-
Anomaly Detection:- As the name suggests, in this process we have to look at and identify irregularities in datasets. These we have to fix or get rid of to make a good prediction. But sometimes, we have to see if these datasets can be used in some way before discarding them. So, we can ensure efficiency.
Dependency modeling: – This stage finds various relations between and among various variables. Due to this feature, we also know of this step as Association rule learning. It is also known as Market basket analysis.
Clustering:- We discover structures and groups in similar data sets in this stage.
Classification: – In Classification, we simply classify data based on certain parameters.
Regression:- This process discovers the relation between data sets or data to find out a function that is able to model the data with the least amount of error.
Summarization:- Summarization is the process of visualizing data and creating reports that are human-readable and understandable with relative ease. This allows meaningful representation of the data we extracted.
3. Results Validation
After the Data is extracted, it needs to be verified. This would ensure something wrong did not happen during the process. Not all of the results you get are valid. Sometimes the results can be off and it is our responsibility to weed them out and keep the results that are right.
So, this step is not an optional one. You need to verify the Data which comes up, and we need to work accordingly. This can be done by desiring a certain output, then comparing that with the result that comes up.
After this process. If the Data we get meets our standards, we then interpret that, and then we can turn that into a human-readable format. The patterns are turned into knowledge. If that is not the case, we have to reevaluate what we did in the previous stages.
So, we need to revisit the Pre-Processing and Data Mining stage and evaluate the results. We have to find out what changes are required and we have to make them accordingly.
Do you need Data Mining?
Before jumping on to actually mining the Data, we should once revisit if we really need Data Mining. There are various uses for Data Mining, but we should know if it would be meaningful for our business.
Data Mining gives us useful information about our business. It is used for Data Analytics, and it is also important for Business Intelligence as they get more data about their own organization, customers, and even their competitors which helps them to prepare for the future which saves and even gains Businesses money and profits.
Data Mining can be used in various use cases, we are mentioning some of the use cases of Data Mining below-
Optimizing Business Operations:- With the help of Data that businesses get from Data Mining, they could use this to increase the efficiency of their business. They can optimize their workflow by reducing spending in certain divisions and they can overall make more informed decisions.
Sales and marketing:- Acquiring new customers and retaining existing customers is a big part of running a business. So, knowing more about your target customers and making ads and products relevant to them would increase the chances of sales and would help to optimize the Sales and Marketing efforts, and it would also help to optimize the products and the service the company provides.
Fraud Detection:- Fraud Detection is another area where Data Mining shines. Although computers are quite smart, they are not able to anticipate the user’s actions and can’t find out if the user is legit or not. Here is where Data comes in.
If there were Data on what features are more likely to be faulted, we can use this to prevent unwanted transactions for Banks and Organizations. We can also secure systems by observing anomalies, and this could even prevent Cyber Attacks.
Education:- Not only the corporate world but society can also be a better place with the help of Data Mining. Various performances of a student can be extracted which can therefore help to increase the quality of education, making the educational sector a little better.
Now that we know the uses and basics of Data Mining, let’s get to know what is the best Data Mining tools.
Best Data Mining Tools – Our Top Pick👌👌
Teradata is a multi-cloud platform. This is one of the best Data Mining Tools that unifies various pieces of information for enterprise analytics. So, it allows your business to grow by bringing various information in one place.
With Teradata, you get predictive intelligence. You get advanced information, and you get a forecast of the possible future which helps you with making better decisions for your business. It helps to drive your business by predictable intelligence and it delivers actionable answers.
Built with multi-cloud technology, this platform gives you the flexibility to deploy it anywhere you desire.
You can deploy it on Private Servers, and you can even use services like Amazon Web Services (AWS), Microsoft’s Azure, and even Google Cloud as a place of deployment. Teradata also has an expert team who can help you to optimize your business operations, and they help you to achieve great value out of their services.
With Teradata, you won’t need to worry about uptime. You can send a query request in real-time. They are built to serve the business. So, their intelligence helps you to build your own next-generation business.
Worried about what to do when your business scales? You don’t have to worry about it. Teradata’s services are multidimensional, and they offer enterprise-level scalability.
So, you don’t need to tear your hair out thinking about the future. Artificial Intelligence and Machine Learning Models would power the models which ensures a better quality of their services.
You can give your teams the access they need. Teradata has various roles for users, and they also have no coding needed software. So, you are good to go and won’t have to go through extensive training for your staff.
Along with these features, Teradata doesn’t have any additional cost, and their Console or Control Panel is quite intuitive and shows and tracks your usage statistics.
SAS Enterprise Miner is very robust in its mining aspect. You get valuable insights and information for your business with SAS Enterprise Miner. It is possible to create models quickly with the help of their tools which makes them easy to understand and easy to work with.
With SAS, you get equipped with various tools that make it easy to create models, and it also helps you to create better models. They use flow diagrams which make the process very easy and simplify the Mining
Process. Experts and even business users can easily get used to this and can generate models by themselves with the help of the SAS Rapid Predictive Modeler.
SAS gives you an easy-to-use GUI, you also get features like advanced predictions, open-source integration, descriptive modeling, and also batch processing. They also have options for Cloud-Deployment, Scalable Processing, and many more features.
Qlik is an intelligent platform that helps your Data Mining needs. Qlik helps you to bridge the gap between your insights, data, and action. They provide you with Analytic Visualization of real-time, AI-driven, and collaborative data.
Qlik accelerates your workflow. It does so by accelerating the speeds of each key aspect like Ingestion, Data Replication, and Streaming of said data across mainframes.
Qlik is made to reduce your cost, risk, and delivery time. This data can later be consolidated or even be joined with others. This can be done by using push-down or other modern ELT methods.
Qlik’s services are no-code services. Their services are cloud-native which helps you automate and streamline your workflow. Not only that, but you also get the Qlik Sense application. So, you can automate your workflow between it and SaaS applications.
This data mining tool also comes with an intuitive and easy-to-use dashboard. Artificial Intelligence assists the Analytics making it fast and reliable and with their APIs, you can create external applications with the help of this.
If any abnormality in Data is spotted, Qlik prompts a relevant action. Their deployment option is also quite flexible, they protect local governance and offer multiple cloud options.
Weka is yet another one of the best Data Mining tools that provides you with the tools for ML (Machine Learning Algorithms), Data Processing, and Data Visualization.
Machine Learning Techniques can be added to Data Mining Problems quite easily. This can be done by following some simple steps.
These are as follows-
- Clean the parts of the raw data which has null values and which are irrelevant to the current work. Do the cleaning process with the tools from Weka.
- Save the modified and clean data and save it to storage to apply the machine learning algorithms.
- Select the correct option depending on the type of the data. There are three available options, classify, cluster, or associate.
- Now, this can be used to automate the workflow and save time.
You can choose which algorithm you want. You can choose to form various Weka’s. You also have the ability to set the desired parameters as per your liking. After the work is done, you can get visualized information from the machine output and turn it into statistical output.
5. Oracle Data Miner
With the help of Oracle Data Miner, Businesses, Data Analysts, and data scientists can simply work directly inside a Database and can use the intuitive drag and drop workflow editor.
But it has to be kept in mind that Oracle Data Miner is not standalone. It is an extension to the SQL Developer by Oracle, which has a very simple workflow and which is very useful if we want to share insights and execute analytical methodologies.
With Oracle Data Miner, you get graph nodes to view data. These include summary statistics, box plots, and more. This miner helps you to minimize the time between the development of the model and the deployment and saves precious time. It also helps by eliminating the step of Data Movement and preserves the security of the data.
6. RapidMiner Studio
With the help of RapidMiner Studio, you get a comprehensive data mining platform that also has full automation. This helps you to speed up the process of predictive modeling by implementing drag and drop functionality.
Not only that, with RapidMiner, you get connections to warehouses or enterprises. You also get connections to Cloud Storage, social media, various Business Applications, and Databases.
ETL and preparation can be run inside the database. This would help to maintain optimized data to run analysis. The manual hard part can be eliminated by using the tool RapidMiner Turbo Prep. This saves man hours and lessens the room for error somewhat also.
You can run Machine Learning models without even writing even a single line of code using RapidMiner Studio. With the help of RapidMiner Studio, easy-to-explain, easy-to-understand models can be created. If your workflow has Python and R. You can add the community-provided functionality to add various capabilities using extensions.
With KNIME, you get end-to-end Data Science Support. This not only saves your business time but also enhances the productivity of your business. KNIME equips you with various tools when you opt for their services.
You get tools like KNIME Analytics among other tools. KNIME Analytics helps you deploy a commercial KNIME Server which is for Data Science Models.
Besides that, KNIME is an open and intuitive platform. They are also able to integrate new developments into their services as time goes by. So, this is a great tool for collaboration and management.
However, if you are not an expert you don’t need to be sad. KNIME gives you access to their Web Portal. The Extensions of this tool are made by both KNIME themselves as well as by the amazing community. The integration with open-source items is also quite good, which ensures you are not lacking features.
You can get KNIME if you are on Amazon’s AWS or in Microsoft’s Azure Services. But they don’t leave you the hard part of transforming the Data. They help you to access, merge and transform your data and then let you analyze it using your preferred tools.
8. Togaware’s Rattle
If you want to do data science using R. Rattle is the Graphical User Interface that makes the process easy. In its core lies a GUI Toolkit called RGtk2, which you can install from Microsoft’s CRAN repository.
So, let’s get to know what Rattle can do-
- Rattle can show you a visual representation and statistical summary of your data
- Rattle transforms the data for modeling.
- Rattle creates both supervised and unsupervised machine learning models.
- Rattle can graphically present a high-performance model.
The interaction that happens with the data is captured and saved in the R script. Then this is executed in the rattle interface, but the R script is executed independently. To use this tool more efficiently, one can learn R.
Rattle is a platform that is free and open-source. The code for it can be found in the Bitbucket git repository. So, there is full transparency there and you can even add or edit the code you’re liking as well.
9. Apache Mahout
Mahout from Apache is a Scala DSL framework and a distributed linear algebra framework. This is specifically designed for Statisticians, Data Scientists, and Mathematicians. This is to help them implement their algorithms.
This project by Apache is an open-source Data Science project. It helps you to create machine learning algorithms. The algorithms for Apache are written in Hadoop.
Therefore, it can scale in the Cloud with the help of the Hadoop Library. So, you get a ready-to-go framework for your data mining needs which is also quite intuitive.
To sum it up, Data Mining is an effective way to gather information from various fields and take meaningful parts to put it to good use for your business. This not only helps you reduce costs; it also makes good forecasts, helps you prepare, and tells you if there is something to worry about.
So, try out the best data mining tools we mentioned for your business and make informed decisions for betterment and growth overall.