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Power Your Business Decisions with Predictive Insights

Data-driven approach is now considered as one of the critical success factors. After all, your data is your primary asset and by analysing it, you can make better decisions to increase your revenue margins.

Having a great business sense and supporting it with smart data governance is important to get a prescriptive idea of what steps need to be taken for business growth. With business intelligence and predictive insights, businesses can perform predictions and planning simultaneously.

Predictive insights are empowering modern-day businesses with early problem detection in the IT infrastructure, applications and middleware. Based on results, a proactive approach to fault management in logical and physical infrastructure can be taken.

Predictive analytics has become a norm, and along with early problem detection, it also helps with real-time analysis and dynamic configuration.

However, to perform statistical analysis efficiently, businesses need to rely on fast and powerful solutions like IBM SPSS Statistics.

What’s IBM SPSS Statistics?

It’s a comprehensive set of statistical tools, which helps organizations run regression, advanced and descriptive statistics. Users can benefit significantly from publication-ready tables, charts and decision trees.

With a simple drag and drop interface of IBM SPSS Statistics, businesses can analyse multiple data sources and get access to the diverse range of capabilities. To perform advanced statistical analysis, IBM SPSS Statistics is backed by machine learning algorithms, big data integration and open-source extensibility.

The flexibility and scalability of IBM SPSS Statistics help users undertake projects of diverse complexity with ease. At the same time, this statistical analysis solution minimizes risk and improves efficiency of businesses in grabbing new growth opportunities.

Predictive Analysis with IBM SPSS Statistics

IBM SPSS for predictive analytics uses advanced analytics capabilities to discover patterns in the data. With a single solution, organizations can anticipate the outcome and take actions to optimize operational decisions.

You can apply the best strategies to optimize human capital management with real-time scoring and machine learning algorithms. Further, the power of analytical projects is amplified with open-source integration across R, Python and more.

  • Statistical Analysis and Visualization

Advanced statistical techniques and predictive analysis tools will help organizations with planning, data collection, analysis, reporting and deployment. With graphical representation of data, users can analyse trends and patterns, which might go unrecognized in traditional data analysis.

With data visualization, businesses can understand big data better and find out factors which influence their customer behaviour. Also, they can identify areas of improvement and take measures to improve their sales performance and revenue.

  • Predictive Modelling and Data Mining

With the migration to digital business models, a vast amount of real-time data is available with organizations across cloud computing platforms and the internet browsing history. Further, a significant amount of information can be tracked through social media and other mobile devices used by employees in the corporate network.

With the powerful model-building and automation capabilities of IBM SPSS statistics, businesses can forecast the result of upcoming events. IBM SPSS statistical software collects historical data and processes it on the basis of past happenings to predict future outcomes.

  • Linear Regression

With the linear regression feature of IBM SPSS Statistics, you can predict the value of a variable by finding its relation with another variable. You can find the relation between two continuous variables, such as, the ‘number of hours worked’ and the ‘productivity’. Here, first one is the independent and the latter is the dependent variable.

IBM SPSS also allows users to perform multiple linear regression, involving more than one explanatory variables. This type of linear regression is more frequently used for understanding the impact of market changes on the price of a product.

  • Logistic Regression

Logistic regression (also known as logit model) is often used for predictive analytics and modelling, extending to applications in machine learning. More classification problems like predicting whether an email is spam or not requires logistic regression.

It provides predictive analysis and modelling based on machine learning to explain the relation between dependent binary variable and independent variables of nominal, interval and ordinal nature.

IBM SPSS statistical analysis software has it all to transform your data into meaningful insights and recommended actions. It’s your chance to collaborate across teams with data and take actions to speed up your business processes.

Kalpana Arya

Kalpana Arya is a passionately vociferous writer who eats, drinks and sleeps words. A self-confessed tea-lover, she loves to travel and explore new vistas. Her vices include online shopping, wearing fashionable clothing at the risk of being frozen half to death and chatting about random stuff. She’s a published writer whose articles have found home at Tweak Your Biz and iamwire.

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