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Predictive Analytics: The Key for Business’ Success

Predictive Analytics the key for business success

The popularity of predictive analytics in today's digital and big data era is driven by the opportunities it presents for businesses to harness the vast amounts of data at their disposal. Predictive analytics is no longer a luxury; it has become a necessity for businesses looking to stay ahead and adapt to rapidly changing market conditions..

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What Is Predictive Analytics?

Cited from IBM, predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. It uses input data, statistical combinations and machine learning statistics on predicting the probability of a particular event happening, forecast future trends or outcomes utilizing on-hand data with the final objective of improving the organizational (Kumar & Garg, 2018; Davenport et al., 2020; Espadinha-Cruz et al., 2021; Izagirre et al., 2021). Through this machine learning prediction, businesses can change where they allocate their resources to profit from possible future events.

The Process of Predictive Analytics

Unquestionably, predictive analysis gives businesses tremendous advantages. Through its accurate prediction, businesses can spot early warning signals of future churn. Not only for business continuity and success, predictive analysis can also be utilized to transform your IT GRC and improve your ITSM’s incident response. In addition, there are 6 steps in implementing predictive analytics.

  1. Define Problem: clearly define the problem you want to solve with predictive analytics. You need a well-defined problem statement, objectives, and scope to guide the entire process..

  2. Collect Data: : gather the relevant data which can be structured or unstructured. Your data quality and quantity play a crucial role in the accuracy of predictions.

  3. Preprocess Data: clean and transform the data, including handling missing values, removing outliers, and etc. This to ensure that the data is in the right format and can be used by predictive models..

  4. Evaluate Model: evaluate the model’s performance by using appropriate metrics once the model is trained. Perform cross-validation to ensure the model’s generalizability.

  5. Deploy and Monitor: deploy your predictive model and put it to work on new data. Then, regularly monitor your model to ensure it's performing as intended and to review its performance. .

4 Types of Predictive Analytics

The framework advocates for a holistic approach, combining technology, processes, and people, ensuring organizations are poised to tackle the ever-shifting cyber threat landscape. With that understanding in place, here are the practical steps to bring this framework to life in your organization:

  1. Decision Trees

    A decision tree is essentially a kind of flowchart that illustrates a clear pathway to decisions. It is a kind of algorithm used in data analytics that uses conditional "control" statements to categorize data. A decision tree begins at a single node (or root node) and branches out in two or more ways from there. Every branch presents a range of potential results (internal/decision node), combining choices and random events until a desired result is achieved (leaf node).

  2. Decision Trees

    A neural network is a type of machine learning technique that uses networked nodes or neurons arranged in a layered pattern to mimic the organization of the human brain. Computers can utilize this adaptive approach to learn from their errors and keep getting better. As a result, neural networks will try to more accurately solve challenging problems.

  3. Text Analytics

    Text analytics processes large volumes of unstructured text to find patterns and insights by combining a set of machine learning, statistical, and linguistic techniques. It makes it possible for businesses to use it for making important decisions. Sentiment analysis, topic modeling, named entity recognition, term frequency, and event extraction are some methods used in text analytics.

  4. Regression Model

    Regression analysis is a statistical technique for determining the relationship between dependent (target) and independent (predictor) variables. The influence of the independent variable(s) is presumed to be the dependent variable. The target variable's likely value is estimated or predicted using the predictors' values.

In a highly competitive business landscape, businesses are constantly seeking advantages for their success. Through accurate prediction from predictive analytics, businesses that can accurately anticipate market trends, customer preferences, and potential issues are more likely to succeed.


Hillier, W. (2023). What is a decision tree and how is it used? CareerFoundry. https://careerfoundry.com/en/blog/data-analytics/what-is-a-decision-tree/

IBM. (n.d.). What is predictive analytics? https://www.ibm.com/topics/predictive-analytics

Kumar, M., Shenbagaraman, V. M., Shaw, R. N., & Ghosh, A. (2021). Predictive data analysis for energy management of a smart factory leading to sustainability. In Innovations in electrical and electronic engineering, 765-773

Panchotia, R. (2021). Predictive Modelling Using Linear Regression - The Startup - Medium. Medium. https://medium.com/swlh/predictive-modelling-using-linear-regression-e0e399dc4745

Spotfire. (n.d.). Unlocking Insights with Text Analytics | Text Mining Techniques. https://www.spotfire.com/glossary/what-is-text-analytics

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