Data Mining, Data Management And Predictive Analytics

Analytics

Data Mining, Data Management And Predictive Analytics

Analytics is the structured statistical analysis of quantitative data or facts. It is used largely for the exploration, identification, and determination of relevant trends in data. It also involves applying statistical techniques to efficient decision-making. By applying techniques such as mathematical and statistical analysis, analytics find accurate predictions about the future trends.

We can make our business more competitive by using Analytics to predict the future outcomes of our activities. By properly using analytics to analyze data we can predict the demand in a market and therefore decide on how to increase or reduce our activities accordingly. For instance, predicting which car company will win the next Formula One championship will help us to diversify our business, invest in more resources, reduce cost and get involved in a more exciting way!

Statistics and analytics go hand in hand, for good business performance a predictive model has to be developed and continuously tested against the available information. Analytics can be applied at every business level; it is applied to marketing, sales, customer service, planning and development. To apply Analytics at the enterprise level, we need to build predictive models and evaluate them using statistical methods. Data analysis gives insights into areas that need improvement in order to achieve business success. In the past, data analysis used to be performed manually and was time consuming, even worse it used to give wrong results.

Nowadays with sophisticated software tools and analytical techniques we can build predictive models using historical data and make the necessary recommendations to improve our performance. Some of the best Analytics software programs use the R statistical language that is ideal for all aspects of business research. These advanced tools provide actionable insights from large-scale historical data sets and they are easy to use and understand.

Analytics helps us make better decisions by helping us predict and act on the trends that are already happening. We can apply this knowledge to solve problems and make decisions that will make things easier for us. This is how we improve our performance. We start with a small amount of raw data and build up from there.

Prescriptive analytics may also be called prescriptive programming or predictive programming. In this case the purpose of the system is to make better decisions based on statistical methods. Analytics in this case is usually used for forecasting or for providing advice about what is going to happen next. The main advantage of prescriptive analytics is that it is based on real events. Unfortunately though because the data is real it may not accurately forecast what will happen in future.

Descriptive analytics on the other hand provides clear insights from a large set of data without requiring one to make any assumptions. It therefore outperforms prescriptive in the case of forecasting because it does not require the person to make any estimates, predictions or guesses. It provides valuable insights by using both mathematical and non-mathematical techniques and models to analyze large sets of real data.

Both types of analytics provide business intelligence by helping managers make better decisions and achieve their goals. However, the main difference between the two is that one attempts to predict future results while the other looks for trends and patterns in the past performance. This helps improve the current business performance by finding out what worked well and what did not. This is what is known as data mining. Data mining techniques are very popular in business intelligence research and development.

Data mining is commonly used when there is limited or no previous information available about a specific problem or target market. It is also commonly used when there is little direct way to get answers about a problem. Examples include weather forecasts or airline schedules. Other business analytics are predictive in nature, where the main goal is to predict future outcomes based on past performances. Examples include natural language processing, machine learning and behavioral marketing analytics.

Data mining and predictive analytics can be applied to just about any business. Examples include scheduling appointments, identifying customer demographics and predicting behavior. Predictive analytics also help companies predict outcomes in specific situations such as loss prevention and inventory management. This is because if a company can learn about the factors that cause loss or damage it can make its policies and tools more effective. Machine learning uses artificial intelligence to analyze large sets of data and generate results more quickly than traditional analytical methods.

Data mining and predictive analytics are two of the most important pieces of the analytics puzzle. They give analytics technology the ability to make more informed decisions about its projections and help businesses optimize their business. These advances in analytics are likely to continue to improve in sophistication and accuracy. As organizations become more complex, business intelligence analytics will require even more sophisticated technology to be useful. For example, as organizations grow and data volume increases so too will the number of insights and data management techniques being developed to manage these changes.