Data analytics examines large amounts of data to uncover hidden patterns, correlations, and other insights. With today’s technology, it is possible to analyze your data and get answers from it almost immediately – an effort that is slower and less efficient with more traditional business intelligence solutions.
Companies across all industries can benefit from data analytics. Manufacturing companies may use data analytics to assess workloads to ensure machinery is operating at maximum capacity. Financial companies can use data analytics to help measure market risk. Retail companies can use data analytics to evaluate customer satisfaction and predict retention rates. No matter what you use data analytics to measure, you will incorporate a process that looks something like this:
- Determine the necessary data to collect.
- Collect said data (computers, online sources, personnel, environmental sources, etc.).
- Organize the data so it can be analyzed (this is where software can replace the manual and disparate storage across spreadsheets and store all data in a centralized and secure location).
- Clean the data for gaps or repetition. Once it’s clean, it can be used for analysis.
How does it work?
To function, data analytics requires several technologies that work together. The main components include:
Machine Learning: A subset of artificial intelligence, machine learning allows the software to learn and automate models. It works to analyze mass amounts of data quickly and provide results based on models.
Data mining: Data mining takes large sets of data and finds patterns. It is through this process by which information can be gleaned to answer complex business questions.
Data management: As briefly mentioned, data must be managed and cleansed to be used for analytics. Data comes in and out of organizations at light speed, so there must be a set standard to collect, store and address the quality of data before it is used in practice.
Some key tools used for generating insights from raw data are R programming, Tableau, SAS, Python, Apache Spark, MS Excel, RapidMiner, KNIME, QlikView, Splunk, Power BI, COGNOS, Informatica etc.
Why is it Important?
Data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Businesses around the globe have benefitted from the power of analytics in the following ways:
- Cost reduction. Technologies and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
- Faster, better decision making. With the speed of technologies and in-memory analytics, combined with the ability to analyse new sources of data, businesses can analyse information immediately – and make decisions based on what they have learned.
- New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. With data analytics, more companies are creating new products to meet customers’ needs.
Data analytics helps businesses convert their raw business data into actionable insights. Our experts at ThoughtStorm can help your organization make quick, data-driven decisions in the ever-changing environment by rendering a full set of data analytics services. Contact us at email@example.com and visit our website www.thoughtstorminc.com to know more.