Business Analytics

Business Analytics is a data-driven field that powers practical changes. This is done by creating statistical analysis models on why things happened and what will happen, and giving concrete recommendations on what should be done. The goal of Codex Ipsum business analytics is to increase your knowledge of your business environment and boost the value of your time and investments.

Discover Business Analytics

Scale your business by using explanatory and predictive statistical models

As Business Intelligence helps understand how an organisation has arrived to the point where it is now, Business Analytics takes things further by giving insights into future business outcomes. Business analytics seeks to answer predictive questions of interdepence, such as “How much change can we expect in Y given change in X?”, putting statistical analysis methods into practice.

How it works

Business Analytics enhances the value received from your investments and scales effortlessly.

Companies increasingly invest in innovative ways to use big data in order to improve customer and employee experiences and maximize profits. To achieve this is not a simple task. Given the great diversity in investment targets and overlap between their desired outcomes, it is difficult to determine which investment programs produce the greatest value and which programs fare poorly in cost-effectiveness.

Fortunately, by using statistical analysis methods these issues can be managed and robust decision making information can be produced. Business analytics has applications in a wide array of different businesses, which help to explain and predict outcomes of plans and strategies in relation to the amount of investment and other business environment factors such as employee wellbeing measures.

In other words, a statistical analysis would be able to determine which of your marketing strategies produces the best return on your investment and what is the expected ROI. Business analytics can also discover which employees have the greatest sales impact per hour of work in different marketing strategies, or which employee wellbeing strategies are most influential for employee retention, illustrating how flexibly analytics can produce insights and reveal hidden strengths.

Inspect the following figure to learn about the business analytics procedure

The analysis design is determined during the planning stage. This involves planning out what data is used and what statistical analysis methods are suitable for the data in question. In the planning stage test hypotheses and causational chains are established in the explanatory models.

Data is provided to the analyst in the planned formats. Access-sensitive data, such as in the case of databases, is to be database queried by the client.

Data is transformed to facilitate the analysis procedure, and the statistical analyses are performed to answer design hypotheses. Post-hoc analyses may be performed to investigate any notable factors and to determine their influence in the statistical model.

The analysis process and results are reported. Result significance is laid out in practical terms and demonstrated visually to explain the real-world relationships between investigated factors. Actionable business decision suggestions are derived from analysis results.

As data accumulates during business operations, the explanatory and predictive power of statistical analyses increases; furthermore, the content and results of statistical analyses fundamentally develop and change as the business environment evolves and business decisions are made. It is highly recommended that business analytics is performed on a regular basis to ensure that the statistical models stay up to date. We always design reusable scripts and programs for each client, which facilitates efficient analysis redeployment.

Analytics toolkit

We vouch for use of open source software.

By using open source software it is possible to know with certainty that your data belongs only to you, and is not handled by any third parties. Currently, our services support GNU Octave and IBM® SPSS® software, with more analysis, scripting and programming options coming soon.

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