Data Mining

Data mining is the process of analyzing existing data to predict future outcomes. Businesses can use Data Mining to identify patterns and correlations, thereby improving success in making business decisions. 

What is Data Mining

Understanding your requirements, developing custom solutions 

Data Mining, a subset of Data Science, is a process that is used to understand patterns, correlations and anomalies from large data sets. With a robust data mining strategy businesses can make successful business decisions. 

Data mining allows businesses to better understand their customers. The patterns derived from Data mining helps business to create concrete marketing strategies. 

Our Data Mining Services

Impeccable service, meeting deadlines

We provide you the cutting-edge Data mining solutions for the following services:

 Text Mining from word processing software and text databases:

This service specializes in mining the massive amount of data in different word processing software, spreadsheets, and text databases to convert them into another, more usable form of data. The business rules for data transformation are aligned, followed by in-depth research so that that appropriate tools can be applied or developed for specific business requirements for Data Mining.

Relational and Object-oriented database mining:

Over a period of time, the databases used for various business applications are overloaded with different types of information in their existing database regularly updated through business transactions. In today’s competitive edge, every business needs to be cognizant of their existing data and assess if they can use the data to redefine their strategies for business growth. We support you in providing a complete business consultancy by first analyzing the business model, having a deep-dive analysis of the existing data available in different types of databases, and arranging data in various categories and sequences to provide you the strategy to grow your business.

Image data mining

There is a plethora of information sitting inside images, which several businesses would like to utilize for their business purpose. Image data mining involves using manual processes or tools to slice and dice the data to determine the required pattern. Various attributes like shape, color, and texture of images play a key role in data extraction and grouping. Manual processing is quite resources intensive though required for the poor-quality images. We take care of any type of your image data mining needs.

Legacy databases

Several businesses move towards new technologies to maintain pace with the business environment. During this process, they are not able to utilize their valuable data from the past in their business and hence loses huge opportunities of utilizing the existing legacy databases for business growth. Our service focuses on analyzing and mining data from any legacy database and provide you with strategies for business innovation. 

Multimedia and streaming databases

In today’s world, an abundance of data is available in the form of multimedia, which could be text, images, drawings, sketches, illustrations, animation sequences, audio, and video. Besides, data is also streamed by continuously generating from different sources. Such data is processed incrementally using Stream Processing techniques. We support you in varied mining information from multimedia and streaming databases based on your business requirements.

Web and social data mining:

Every business needs to be aware of the latest trend and strategies used by its competitors or similar businesses in different locations of the world. Besides, your business may require specific information from various web sources to strengthen your business, expand your product offerings, find the suppliers, or new customer segments for your business. We team of experts support you in all these areas.

Need for Data Mining

We understand every business is different.

In today’s era, data is generated at an exceptional speed and volume from multiple sources. However, having abundant data is of no value unless data is analyzed to uncover useful insights to make wise decisions. Data mining is used to explore the hidden, valid, and potentially useful patterns in data to establish relationships and future predictions.

Organizations are using this concept for decades, but earlier, the data was limited. With a massive amount of data, modern tools, technologies, and strategies are required for immediate business decisions, improve organizational’ s performance, and stay ahead of the competition. Some of the vital business needs for data mining involve forecasting resources, market segmentation, customer behavior study, customized customer offerings based on the demand, customer retention, detecting frauds, managing risks, and identifying the effects of clinical trials.

Data Mining Techniques

1. Classification to retrieve the essential and relevant information as per business goals

2. Clustering to group the data and understand the differences and similarity between the data

3. Association to establish the relationship between data and uncover hidden patterns

4. Pattern Study to discover or identify similar patterns or trends in transaction data for a certain period.

5. Outer detection to observe data items in the dataset does not match an expected behavior pattern.

6. Regression to identify and analyze the relationship between variables.

7. Prediction to analyzes past events for predicting a future event by combining multiple data mining techniques.

Data Mining Service Classifications

Data Mining for various type of Research

Research is vital in building a successful business that can fall into various categories like Market Research, Correlational Research, Competition analysis, Concept validation, etc.

Data Mining incorporates cutting edge technologies to specialize in research and provide a concrete output that could result in substantial customer value. We use machine learning algorithms, and Artificial Intelligence makes sense of large datasets. The information derived from the mining will be used to create effective marketing, branding, and business strategies.

Data cleansing from large datasets

Often businesses have large data sets but they couldn’t use it effectively since they don’t have right tools to analyze and clean the data as per the business goals. This is primarily because data mining requires specialization. With Diligent Specialist Services, we use state of the art technologies to process and analyze data.

We are fully equipped to handle a massive influx of data and process them with high efficiency. With the modern methods for data extraction and processing, our clients are assured that their data is transformed into useful and precise information.

Get actionable data metrics for your business

 

Data is precious. With successful analytics, you are getting useful information, not just for your company but also for your audience and the market.

We are always striving to bring the best results to our customers to add business values. Our data mining strategies would enable you to get detailed insights into your business through various metrics and take necessary actions.

Data Mining Life-cycle

The following life-cycle model is followed to bring you the best value through Data mining.

  1. Business alignment: We ensure the business goals for data mining are well understood, and existing data mining processes are studied (if any). The end result of this stage is a detailed data mining plan.
  1. Data Analysis: Data is collected from multiple data sources and validated to ensure relevance with the business goals. Multiple visualization tools are used for analysis, and all the data gaps from the business goals are determined.
  1. Data preparation: The data collected from different sources are cleaned, transformed, formatted, and constructed after updating the missing values, as required. Any inconsistency or data outliers are identified and updated accordingly.
  1. Data transformation: Data is transformed as per the business requirement using multiple approaches like Smoothing (remove noise from the data), Aggregation, Generalization, and Normalization. 
  1. Modelling: Based on the business objectives, suitable modeling techniques are selected to test the model’s quality and validity. Results are assessed by relevant stakeholders to ensure the model can meet data mining objectives.
  1. Review: Modeled data are assessed against the business goals, and accordingly, the deployment is planned.
  1. Deployment: In this phase, the final reviewed model is deployed in business operation.
  2. Control: A detailed plan for monitoring the data mining processes is created, and lessons learned are recorded on a regular basis.