7 steps of a good Business Analytics strategy
Now let's see what the seven basic steps of a good Business Analytics strategy are. Actually, many business analytics tools and solutions will help the professional to determine the best approach and prepare the best recommendations based on his analysis.
1. Study and define business needs
The first step in the business analytics process involves understanding what the business would like to enhance or the problem it wants to fix.
The relevant data to solve these objectives is decided by the stakeholders, the users with knowledge of the processes and the analyst(s).
In this phase, key questions such as "what is the available data", "how can we use it", "do we have enough data" must be answered.
2. Macro Data Mining
This step is about cleaning up the data and doing calculations for lost data, removing outliers, and transforming combinations of variables to create brand new variables.
This is where a specific tool can already be used.
Time series graphs are plotted, showing norms or disparate values.
In this step, the removal of disparate values from the dataset is a crucial task, because disparate values usually affect the accuracy of the model if they can remain in the dataset.
With clean data, the analyst will understand it better. He or she will trace the data using scatter diagrams to detect possible interrelationships or misalignments. He or she will visually check all potential data ranges and synthesize the data using proper visualization and descriptive statistics that will help stakeholders gain a core comprehension.
3. Data Analytics
Using statistical analysis techniques, such as correlation analysis and hypothesis testing, the analyst will identify all factors associated with a target dynamic.
He or she will also conduct a simple regression type analysis to find out if straightforward predictions can be made.
In addition, various groupings are being compared with different scenarios and these are further tested with hypothesis testing.
4. Predict what is likely to happen
Business analysis is about being proactive in making decisions, this is called predictive analytics. The analyst moderates data using predictive techniques, such as decision trees, neural networks and logistic regression.
These techniques reveal new ideas and models that uncover relationships and "hidden evidence" of the most influencing variables. The analyst then proceeds to compare the predictive values to the actual values and computes the predictive errors.
Typically, multiple predictive models are executed, and the best scoring model is selected based on its precision and performance.
5. Search for the best solution
The analyst will apply the coefficients and results of the predictive model to run hypothetical scenarios. The analyst will use manager-defined objectives to determine the best solution, with restrictions and limitations provided.
The analyst will select the ideal solution and model based on least error, business objects, and intuitive recognition of the model coefficients most aligned with the organization's strategic objective.
6. Decision Making and Outcome Measurement
The analyst will make decisions based on insights derived from the model and organizational objectives.
The action taken will be measured after a pre-determined period of time.
7. Update the system with the results of the decision
Ultimately, the decision and action results, as well as new learnings from the model are stored in the database.
Information such as " did the decision and action work?", " did the treatment group compare to the control group?" and " what was the ROI?" are provided. The outcome is an ever-evolving database that is continually updated with new insights and knowledge.
