The Art of Data Mining Explained From Data to Discovery

Every day across the globe, business leaders rely on immense reams of data for intelligent insights and analysis to inform many critical decisions. However, just possessing the data isn’t enough.

Businesses need to proactively discover and uncover the valuable patterns, themes, and trends that are buried within their data. This is known as data discovery.

What is Data Mining?

Data mining is a technique used by software programs to automatically look for patterns and relationships within large data sets. It’s a multistep process that uses algorithms from statistics, artificial intelligence and machine learning to find correlations and patterns that may not be visible to human eyes. The results of a data mining model can describe current data, predict future trends or help in finding data anomalies.

In a basic example, a grocery store might use data mining to determine how best to market its packaged food items to customers. The program might examine past buying behavior by classifying customers based on their ages and then search the new database for patterns that match those groups. The software could discover that customers 51 to 65 tend to shop twice a week and purchase mostly fresh foods, while customers 21 to 50 purchase more packaged goods when they shop. The software might then refocus its marketing efforts to this group, and a company might reap sales gains.

As more public data becomes available and data mining technologies become easier to use and less expensive, the number of potential business applications is expanding. Data mining is already in use in many industries, from banks looking for credit card fraud patterns to insurance companies predicting the likelihood and cost of future natural disasters.

As a result, businesses can better assess risks and develop strategies to avoid costly mistakes. This can lead to savings in the long run by avoiding wasteful investments and focusing on areas that are more likely to yield a return. For example, a data mining analysis might identify that a company’s employees aren’t responding to its customer service surveys as well as it should, allowing the company to create training courses and other initiatives to improve employee performance.

What is Data Analysis?

Data analysis is the process of converting raw information into useful knowledge that supports decision-making. Typically, it involves a five-step approach that includes collecting data from multiple sources, cleaning and organizing the data, performing descriptive statistical analyses, applying inferential analyses, and identifying patterns and trends.

Data collection can occur in many ways, including surveys, interviews, direct observation, and more. The first step in the data analysis process is to identify what information you need. Once you’ve done that, you’ll need to collect the relevant data from these sources. The next step in the data analysis process is to clean the data, which means removing extraneous or incorrect information. This step is important because it allows you to focus on analyzing the data, not trying to figure out why something went wrong.

Descriptive statistical analysis helps you explain quantitative data by presenting statistics, such as mean, median, and mode. It also identifies key findings and provides an overview of the data set. Inferential statistical analysis uses sample data to make predictions about a larger population. It includes techniques like confidence intervals, regression analysis, and hypothesis testing.

Qualitative research analysis focuses on non-numerical data, such as text, images, and audio. Techniques such as content analysis, narrative analysis, and grounded theory can help you interpret this type of data. Quantitative research analysis focuses on data that can be counted, such as sales, market research, or survey results. Techniques include time series analysis, which identifies patterns in data collected over time, and cluster analysis, which groups similar data points into relative groups, according to StatisticsSolutions.

Finally, diagnostic analysis identifies the cause of an outcome or trend. For example, if descriptive analysis shows that a hospital had an unusual influx of patients, diagnostic analysis could show that the influx was caused by a virus.

What is Data Cleaning?

Data cleaning is a process of correcting errors, inconsistencies and duplicates in data sets to improve their quality and integrity. It also involves removing irrelevant observations. Data cleansing removes invalid, missing or corrupted data; it may also involve transforming raw data into more usable formats (for example, formatting phone numbers into formatted strings with dashes and parentheses). It is a common part of the data preparation process and a vital component of business intelligence initiatives.

Dirty data affects the ability to make informed decisions, and can ultimately erode trust in your company’s information. As a result, businesses must invest in cleaning and integrating their data more regularly to get the most value from their investment.

Today, organizations collect vast amounts of raw information from clients, products and more. This information is used for everything from predicting customer behavior to creating targeted marketing campaigns. However, inconsistencies in data entry, incorrect or missing values, and extraneous observations can skew the results of any analysis. Without a clear, well-defined data strategy, your team will struggle to find insights and make confident decisions that drive business success.

Clean data allows you to separate the signal from the noise and unearth valuable insights that will improve your operations, customer experience and more. Getting your data into the right shape will help you deliver more accurate results and free up time for your team to focus on other analytics projects.

Many different tools are available to automate the data cleansing process. They often work on batch processing, meaning that they can run on large data sets without any user interaction. They can also include a wide variety of functions for correcting data errors and inconsistencies, removing unnecessary observations and matching data sets. They also offer a range of features for standardizing fields, adding missing values, fixing punctuation and combining duplicate records.

What is Data Integration?

Data integration is a process that combines different data sources and assets to create unified sets of information for operational and analytical use. It’s a key component of data management, used in applications such as enterprise reporting, business intelligence, and advanced analytics. Georgia tech masters in analytics which has been getting a lot of buzz might pique your interest.

There are a number of ways to perform data integration, depending on the requirements of your business and the type of data you have in various systems. For example, you may use automated, scheduled processes to ingest data from one system and then move it to another. This is known as ETL (extract, transform, load). Or, you might rely on a more agile approach by using middleware or software to connect applications directly and synchronize data with each other on a real-time basis.

Regardless of how you implement your data integration, the goal is to ensure that data is accessible to all users across departments and divisions. This helps your team work together, and it also prevents the development of departmental silos that keep potentially useful data locked away from other areas of the organization.

Having access to the right information at the right time is critical for your business to remain competitive. This means that you must have a robust strategy in place to integrate all of your data and assets in order to maximize the value of your information. It’s important to tie your data integration work into your existing data governance programs, master data management (MDM) initiatives, and other core information management activities. Doing so will ensure that your data is clean and consistent, and that you have complete visibility into the lineage of your data sets. This will help you identify and resolve any inconsistencies before they become costly problems for your organization.

What is Data Visualization?

Data visualization is a technique used to convey insights and findings via data-based graphics. It helps business users quickly uncover fresh insights and focus on areas that require more attention. According to a study by the Wharton School of Business, visualization reduces decision-making meeting time by up to 24%.

There are many types of data visualizations, ranging from simple charts and graphs to infographics and advanced data models. The type of visualization you choose depends on the complexity of your data and the insights you wish to convey. For example, a line graph is perfect for observing trends over time (e.g. Bitcoin value over time) whereas a scatterplot is more suitable for exploring correlations between variables.

It’s also important to consider your audience when creating visualizations, since they may have differing levels of familiarity with the data you’re presenting. As such, it’s essential to keep cognitive load to a minimum by removing unnecessary elements and presenting key information succinctly.

Data visualization tools can be used in a wide range of applications, from creating automatic dashboards to monitor company performance to analyzing unstructured text data for keywords and hidden relationships. However, they’re perhaps most common in business intelligence (BI) and predictive analytics, where they can be used to create interactive visualizations for end-users. They’re also increasingly being integrated with business intelligence applications, providing users with a single, easy-to-understand view of all data. These visuals can also help teams communicate key metrics to colleagues and stakeholders, making them a vital component of any data-driven organization.