Analytics, also known as “business analytics” or “data analysis”, is the use of data, statistical analysis, explanatory and predictive models to gain insights and act on complex issues. Analytics can provide insights to a wide variety of uncertainties. For example, analytics can be helpful for an institution to answer the following questions: How to increase student retention? How to maximize alumni donations? How to increase research productivity? What does this IT initiative really cost? For this reason, analytics must start with a question or hypothesis.
Once the data is collected and put into a data warehouse, it’s time to decide what suitable analysis suits the questions asked and the data collected. At companies today, different types of information reside in so many different environments and are stored in so many different formats. Quickly extracting meaning from almost what we have is becoming quite impossible. Today, information is flowing like mighty rivers and soon the amount of digital information will grow equivalent to a stack of books from the Sun to Pluto and back again. And with 80% unstructured data, from music files to 3D images, medical records and email records, the challenge is to pull it altogether and make it sensible.
Everyday companies make better business decisions about their customers, competitors and new products. Decision making today is an art based on incomplete and conflicting information. Analytics can prove to be very advantageous for a company:
- To look at all of its information at once
- Spot hidden trends before they occur
- Predict outcomes and everything from weather forecasting to transportation scheduling to financial performance
- Keep it’s information safe and secure by providing access to the right people while keeping out the trouble makers and determining all the different variations of a persons’ name, that’s analytics
The three major types of analytics are:
Descriptive analytics is about the present. Descriptive analytics helps organisations understand what happened around the past. It can be from one minute ago to a few years back. Descriptive analytics help to understand the relationship between customers and products and the objective is to gain an understanding of what approach to take in the future: learn from past behaviour to influence future outcomes. Common examples of descriptive analytics are management reports providing information regarding sales, customers, operations, finance and to find correlations between the various variables.
It includes comparative analysis. Comparative analytics solutions leverage the concepts of business intelligence but contextually compare that data against peers and other benchmarks – a key differentiator. For example, comparative analytics solutions enable organizations to access salary and compensation statistics, measure reimbursement rates against benchmarked data and compare actual clinical treatments against published pathways.
Predictive analytics is about the future. Jeho současný styl hry vychází z hráčů typu dave weckl, jojo mayer, benny greb nebo johnny rabb. Mým cílem je objektivně vyzkoušet tento výživový doplněk a vlastnilekarna.com předat vám své zkušenosti z praxe is doporučením. Predictive analytics converts data into actionable information. The three basic tenets of predictive analytics include: Predictive modeling, Decision Analysis and Optimization, and Transaction Profiling. This type of analytics helps to determine the likely future outcome of an event or situation occurring. Predictive analytics includes the use of various statistical techniques from data mining, modeling, machine learning, and game theory which analyze historical and current facts to make predictions about future events. This type of model helps to identify relationships among numerous factors to permit the analysis of risk or potential connected with a certain set of conditions in order to make better decisions. As well, it can be used for a business offering more than one product. This model can be used to assess customers spending and other behavior to help with cross-sales or selling other products to customers.
Prescriptive analytics provides advice based on predictions. Prescriptive analytics brings together a large amount of data, device learning, rules of business (constraints, preferences, policies, best practices, and boundaries), and mathematical, to predict future outcomes, and then suggest decision options to benefit from the predictions. This type of analytics shows the decision-maker the implications of each decision option, and not only anticipates what will happen and when, but also why it will happen. The result is acquiring new data to improve prediction accuracy, taking advantage of a future opportunity, or mitigating a future risk.
The best part of analytics is its limitless horizons. It is changing the conventional way of business. Studies show that organizations that apply analytics outperform their peers. Further, those with a broad-based, analytics-driven culture perform, on average, three times better. Not only do they drive more top-line growth and control costs, they take timely corrective action to reduce risks that derail their plans.
Authored by Shreesti Ghosh, Student, FIIB
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