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Predictive Analytics Tools by SageData
Benefits of using tools for data modeling for your business
Everyone in the business world understands the need always to be one step ahead of the competitors. It may seem complex, especially if you are not well briefed on the specifics of the predictive analytics tools and how they work. It doesn’t matter whether you are managing a startup, a small business, or a big multinational organization, accurate and insightful analysis is a must.
Predictive analytics tools help you explore current data and past events. They allow you to dive deeper into analyzing topics, such as customer behaviour and best-loved products/services among users, and identify potential opportunities or risks for your business.
How do any tools for predictive analytics work?
Good Predictive Analytics Tools use various modeling techniques to determine possible outcomes:
- Descriptive Analytics – Gives a real-time insight as to what is happening at the exact moment;
- Diagnostic Analytics – Explains the reasons behind previously occurring events, as well as the reasons behind events that are currently happening;
- Predictive Analytics – Predicts future possible outcomes, based on the descriptive and diagnostic data;
- Prescriptive Analytics – Provides you with information on how to create a given possible outcome.
Get a Complete Business Intelligence Solution
To achieve good results and to get great performance indicators, you are going to need more than a separate tool. What you have to consider is getting a fully developed service that will not only correctly pull and analyze the data from your data sources, but will also give you the means to organize the information the right way.
SageData has been creating successful Business Intelligence Solutions for years now. During all this time, we have gathered a team of professionals that always succeed in helping businesses make smart decisions, by creating specifically tailored tools. This means that SageData’s solutions can be applied to many industries, including Finance, Media, Healthcare, HR, Banking, and the Energy Industry.
Frequently Asked Questions
You can use Predictive Analytics for many different purposes, depending on your business’ needs:
- Predicting the likeliness of certain people to buy;
- Predicting the ROI of targeted marketing campaigns;
- Giving you insight on when to post your social media content;
- Identifying fraudulent activities – when used in Banking and Insurance;
- Monitoring and looking for early warning signs of people’s health problems – when used in Healthcare;
Predictive Analytics is strongly associated with Business Intelligence. They are not interchangeable terms in any case, as Predictive Analytics Tools are usually considered a part of the Business Intelligence world.
The main difference between the two is that Business Intelligence tools analyze past events to make a good business plan for the present moment, while predictive analytics tools focus more on what the future possibilities for your business may be.
Yes! Our professionally developed technology works in such a way with your infrastructure that it enables you to keep complete ownership of your Data.
Two of the most commonly used techniques are:
- Machine learning – The algorithm receives input data and uses statistical analysis to predict outputs. Examples of machine learning are Geospatial Predictive Modeling, Multilayer Perception, Neural Networks, and Radial Basis Functions.
Regression – This technique establishes a mathematical equation, used to represent the interaction between the different variables. Examples include Linear
- Regression Models, Logistic Regression, Decision Tree Learning, and Discrete Choice Models.
An outlier is a single data point that lies an abnormal distance outside the average value in a random sample from a population. Outliers are usually a result of many possible errors – inaccurate methods of sample gathering and measurement errors being just two of them. To identify an outlier, you can use our Data Visualisation Tools and create a scatter plot. Then you can either change or remove the outlier, or perform a separate analysis with all outliers.