Stories about companies using data to recommend books, movies, discover who is pregnant based on credit card receipts keeps appearing in articles and magazines. But how does one turn data analytics into this type of insight? The answer is data analytics. Data analytics is the process of finding the right data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to have the biggest possible impact. Data Analytics, in its simplest form, is looking at data and creating meaningful insight and understanding. It is used to make decision/s using data and create useful actions based on data – be that of customer, prospect or transactional data that the company has available to them.
The insights revealed through Data Analytics can help you make better, faster decisions and automate processes. It enables you to take advantage of all of your data sources, including structured and unstructured data and thus enabling you to stay ahead of your competition.
It is a more logical and scientific approach to making business decisions and it is based on the use of data to enable this. Analytics uses information from the past to describe and often predict the future. It enables managers to make decisions based on previous data.
Analytics closely resembles statistical analysis and data mining, but it is based on modeling which involves extensive computation. Some fields within the area of analytics are enterprise decision management, marketing analytics, predictive science, strategy science, credit risk analysis and fraud analytics.
Up scaling your business
Understanding your customers and the ways they behave is crucial to the success of any business. How do you extract this maximum insight and value from the given data? One must apply analytical techniques to customer data with clear business goals in mind and deliver findings that are both understandable and actionable. The intelligent analysis and interpretation of customer and prospect data will enable you to positively impact customer behavior, improving financial returns, customer perceptions and standout against the competition.
Data Mining Vs Data Analytics
Data Mining and Data Analytics is distinguished on the basis of scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher. Data mining is exploring data for trends that cannot be “defined” where else Data Analytics is looking at data for trends that can be defined.
Data Analytics Applications
The term “analytics” has been used today to describe different functions that it performs. To name a few:
• In Call centers data analytics is used to describe everything from online analytical processing (OLAP) to CRM analytics.
• Banks and credit cards companies use data analytics to analyze withdrawal and spending patterns to prevent fraud or identity theft.
• Ecommerce companies examine Web site traffic or navigation patterns to determine which customers are more or less likely to buy a product or service based upon prior purchases or viewing trends and influence their behavior with up-selling / cross-selling.
• Modern data analytics use information dashboards supported by real-time data streams to make decisions.
Enterprise’s data is among its most valuable assets. Data Analytics can play an integral role in helping an enterprise unlock the treasures hidden in its massive stores of data. It can help to explain patterns, which in turn can help the enterprise identify what it is doing well, determine how to do it better and recognize problems before they spiral out of control. It can be used to identify areas of key risk, fraud, errors or misuse; improve business efficiencies; verify process effectiveness; and even influence business decisions.