Companies are increasingly relying on data to learn more about their customers. Thus, data analysts have a bigger responsibility to explore and analyze large blocks of raw data and glean meaningful customer trends and patterns out of it. This is known as data mining. Data analysts use data mining techniques, advanced statistical analysis, and data visualization technologies to gain new insights.
These can help a business develop effective marketing strategies to improve business performance, scale-up sales, and reduce overhead costs. Although there are tools and algorithms for data mining, it is not a cakewalk, as real-world data is heterogeneous. Thus, there are quite a few challenges when it comes to data mining.
One of the common challenges is that, usually, databases contain attributes of different units, range, and scales. Applying algorithms to such drastically ranging data may not deliver accurate results. This calls for data normalization in data mining.
It is a necessary process required to normalize heterogeneous data. Data can be put into a smaller range, such as 0.0 to 1.0 or -1.0 to 1.0. In simple words, data normalization makes data easier to classify and understand.
#data science #big data #data mining #normalization
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Data mining is a world itself, which is why it can easily get very confusing. There is an incredible number of data mining tools available in the market. However, while some might be more suitable for handling data mining in Big Data, others stand out for their data visualization features.
As is explained in this article, data mining is about discovering patterns in data and predicting trends and behaviors. Simply put, it is the process of converting vasts sets of data into relevant information. There is not much use in having massive amounts of data if we do not actually know what it means.
This process encompasses other fields such as machine learning, database systems, and statistics. Additionally, data mining functions can vary greatly from data cleansing to artificial intelligence, data analytics, regression, clustering, etc. Consequently, many tools are being developed and updated to fulfill these functions and ensure the quality of large data sets (since poor data quality results in poor and irrelevant insights). This article seeks to explain the best options for each function and context. Keep reading to find out our 21 top mining tools!
#data science #data #data mining #python data science #data mining tools #r for data science
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
In this article, you will learn about the main use cases of data mining and how it has opened up a world of possibilities for businesses.
In the last decade, advances in processing power and speed have allowed us to move from tedious and time-consuming manual practices to fast and easy automated data analysis. The more complex the data sets collected, the greater the potential to uncover relevant information. Retailers, banks, manufacturers, healthcare companies, etc., are using data mining to uncover the relationships between everything from price optimization, promotions, and demographics to how economics, risk, competition, and online presence affect their business models, revenues, operations, and customer relationships. Today, data scientists have become indispensable to organizations around the world as companies seek to achieve bigger goals than ever before with data science. In this article, you will learn about the main use cases of data mining and how it has opened up a world of possibilities for businesses.
Today, organizations have access to more data than ever before. However, making sense of the huge volumes of structured and unstructured data to implement improvements across the organization can be extremely difficult due to the sheer volume of information.
#big data #data visualization #data mining #data warehouse #data modeling #big data analysis
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt