What is Business Analytics: discover this new ecosystem
Business Analytics is a concept that describes the practice of iteratively and methodically exploring an organization’s data, with an emphasis on statistical analysis. It involves the use of advanced technologies and methods of information analysis of the most varied formats, especially in large volumes.
Do you know what benefits your company can get with this approach?
Continue reading this article. Here, in addition to showing in depth what Business Analytics is, we’ll help you think of ways to adapt to it!
Key Business Analytics Techniques
One way to further understand what Business Analytics is to get to know the techniques employed in this approach. They can be divided into two major large areas – one more basic and one deeper.
Basic Business Intelligence
It involves examining historical data to get a sense of how a department, team, or team member got results over a certain period of time, for example. This is a mature practice that most companies already make use of.
Deeper statistical analysis
It can mean making predictive analyzes by applying statistical algorithms to historical data to make a prediction about the future performance of a product, service or site design change, for example.
It may also mean using other advanced analytical techniques, such as cluster analysis, to group clients based on similarities across multiple data points. This practice is often very useful in targeted marketing campaigns, among others.
Key Business Analytics Methods
With regard to the methods of analysis under the umbrella of a Business Analytics strategy, we have three highlights:
- Descriptive analysis, which tracks key performance indicators to understand the current state of a business;
- Predictive analysis, which analyzes trend data to evaluate the probability of future results;
- Prescriptive analysis, which uses past performance to generate recommendations on how to deal with similar situations in the future.
Key Business Analytics Tools
Several technologies are employed in a Business Analytics strategy. They can be grouped into four groups:
- Data visualization tools;
- Business Intelligence software;
- Self-service analysis platforms;
- Statistical analysis tools;
- Big Data Platforms.
The seven steps of Business Analytics
Here are the seven basic steps to a good Business Analytics strategy.
1. Survey and definition of business needs
The first stage of the Business Analytics process involves understanding what the company would like to improve or the problem you want to solve.
Sometimes the goal is broken down into smaller goals. Relevant data needed to resolve these goals are decided by the stakeholders, by the users with the knowledge of the processes and by the analyst(s).
At this stage, key questions like “what data is available”, “how can we use it”, “we have enough data” should be answered.
2. Exploiting macro data
This stage involves data cleansing, calculating lost data, removing outliers, and transforming combinations of variables to form new variables.
This is where a specific tool can already be used (as mentioned above).
Time series graphs are plotted as they may indicate discrepant patterns or values.
In this step, removing discrepant values from the data set is a very important task because discrepant values often affect the accuracy of the model if they can remain in the data set.
Once the data is cleared, the analyst will try to understand them better. He will plot the data using scatter plots (to identify possible correlations or nonlinearities). It will visually check all possible data slices and summarize the data using appropriate visualization and descriptive statistics (such as mean, standard deviation, range, mode, median) that will help you get a basic understanding.
At this stage, the analyst is already looking for general patterns and actionable insights that can be obtained to achieve the business goal.
3. Data analysis
At this stage, using statistical analysis methods such as correlation analysis and hypothesis testing, the analyst will find all factors related to the target variable.
The analyst will also perform a simple regression analysis to see if simple forecasts can be made.
In addition, different groups are compared using different assumptions and these are tested using hypothesis testing.
Often, it is at this stage that data is “cut into cubes” and different comparisons are made when trying to get actionable information.
4. Forecasting what is likely to happen
Business Analytics is about being proactive in making decisions. At this stage, the analyst will model the data using predictive techniques that include decision trees, neural networks, and logistic regression.
These techniques will reveal insights and patterns that highlight relationships and ‘hidden evidence’ of the most influential variables. The analyst then compares the predictive values with the actual values and calculates the predictive errors.
Generally, several predictive models are run and the best-performing model is selected based on the accuracy and results of the model.
5. Search for the best solution
At this stage, the analyst will apply the coefficients and results of the predictive model to perform hypothetical scenarios, using goals set by managers to determine the best solution with the constraints and limitations provided.
The analyst will select the ideal solution and model based on the least error, the management goals and the intuitive recognition of the model coefficients more aligned with the strategic objective of the organization.
6. Decision making and outcome measurement
The analyst will then make decisions and measures based on insights derived from the model and organizational goals.
An appropriate time period after this action has been taken, the result of the action is then measured.
7. Updating the system with the results of the decision
Finally, the decision and action results and the new insights derived from the model are recorded and updated in the database.
Information such as “was the decision and action effective?”, “How does the treatment group compare to the control group?” and “what was the return on investment?” are loaded into the database. The result is an evolving database that is continually updated as new insights and insights are obtained.
Where to start to create a Business Analytics strategy in your company – conclusion
The most innovative companies today have a Business Analytics strategy for insights that inform business decisions and can be used to automate and optimize processes.
For a Business Analytics strategy to succeed, you need to ensure data quality, rely on qualified analysts who understand technology and business, and establish an organizational commitment to informed decision-making.
Previously to this, however, one must develop a “Data-Drive culture”, or a DNA, as the experts in the subject like to assert.
We can describe the Data-Driven culture as the practice of using data in a wide variety of business processes in a systematic and continuous way – also referred to as data-driven management.
Companies with a strong Data-Drive culture establish processes and operations to facilitate the acquisition of information needed by employees. They are also transparent about access restrictions and methods of governance. And, therefore, they are better prepared to create and execute a Business Analytics strategy.
How about, are you prepared to implement a Business Analytics strategy in your company? Contact us and see how we can help you in this endeavor!