A Look at Different Data Analytics Services and Their Popularity Today
Data analytics is a process of examining and transforming large amounts of data to uncover patterns, behaviours, and other useful information to help support decision-making. This process relies on specialized systems, software, and analysts to draw actionable conclusions from data. Although scientists and researchers rely heavily on data analytics, this process is also widely used in other industries, including the commercial industry.
With the help of data analytics services, businesses can make informed decisions to improve business operations both behind the scenes and for customers. The services that are readily available today make it easier for businesses to improve operations, marketing strategies, customer service, and product quality, while ultimately boosting revenue.
The type of data analyzed will depend on the application used. Some services look at historical data, while others look at new information processed in real-time. The data can also come from both internal and external data sources.
Data Analytics Methods
Data analytics services use a variety of methods to draw conclusions from sets of data. These methods include:
- Exploratory Data Analysis (EDA)—searches for patterns and correlations in data;
- Confirmatory Data Analysis (CDA)—uses statistical methods for data to conclude whether a hypothesis is true or false;
- Quantitative Data Analysis—analyzes numerical data with quantifiable variables that are compared and measured using statistical methods; and,
- Qualitative Data Analysis—interprets non-numerical data, such as audio, video, text, images, themes, phrases, and viewpoints.
Data Analytics Services
There are a variety of analytics services and software available today to improve sales, business operations, and customer satisfaction. Many of these services are used together to meet various business goals.
Business Intelligence (BI) provides reports on key performance indicators, business operations, customer satisfaction, and other important business operations. Businesses are also now using BI platforms as front-end interfaces for big data systems.
Data mining analyzes large data sets to find relationships, trends, and patterns. Data mining services allow businesses to predict future trends and improve leads by mining customer data. Web mining is a form of data mining that attempts to understand customer behaviour and the effectiveness of websites so you can strategize accordinly.
Predictive analytics attempts to predict future customer behaviour and events. This analytics service uses both historical and new data to predict future trends, activity, and behaviour with analytical queries, statistical analysis methods, and automated machine learning algorithms.
Predictive analytics is very useful for online marketing campaigns. Businesses use predictive analytics tools and services to find trends in visitors’ browsing history data in order to personalize advertisements. These tools also help businesses determine which products customers are more likely to purchase, aiding in their analysis of inventory needs.
Machine Learning uses automated algorithms to organize large sets of data quickly. Machine learning relies on artificial intelligence (AI) that makes software applications more accurate at predicting outcomes without explicit programming.
Examples of machine learning include:
- Showing ads on webpages based on users’ previous searches; and,
- Personalization of user’s news feeds on Facebook. The behaviours of users—i.e. liking certain friends’ posts, and pauses in page scrolling to read a friend’s post—will lead to the news feed showing more from those specific friends earlier in the feed.
Big Data Analytics
Examines large, varied data sets to reveal patterns, correlations, customer preferences, market trends, and other useful information. Big data analytics services combine machine learning, data mining, and predictive analytics tools to analyze big data sets that usually contain unstructured and semi-structured data.
Applications usually include data from both internal and external sources. Examples of external sources include weather data, or demographic data about customers from a third-party service provider.
Also known as text analytics, text mining analyzes data from natural language text. It can provide businesses with valuable insights from text-based content, such as e-mails, Word documents, and social media posts.
Real-time analytics uses data and resources as soon as they are entered into the system. For example, in customer relations management (CRM), real-time analytics provides up-to-the-minute information about the customers from a specific company to help make fast and improved business decisions.
With the variety of data analytics services available today, businesses can address multiple needs and goals while gaining a competitive edge in the market. Consider how these services can help improve your business and consult with expert analysts to make the most of your data.