In the age of digital transformation, businesses are looking for tools that provide accurate, data-driven insights to stay ahead of the competition. One such tool is SAP Analytics Cloud (SAC), a comprehensive solution that integrates business intelligence (BI), augmented analytics, predictive analytics, and enterprise planning in a single platform. Among its many capabilities, predictive analytics is a powerful feature that enables companies to make informed decisions based on future trends and outcomes.
In this blog post, we dive into the importance of SAC’s predictive analytics in business and how it can help organisations across industries become more proactive, efficient, and successful.
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What is SAC and Why Predictive Analytics Matter
SAC allows businesses to analyse and visualise data in real-time, creating a unified view of their organisation. With its integrated capabilities, users can easily build interactive dashboards, perform financial planning, and make data-driven predictions—all in a single platform.
Predictive analytics is a key feature of SAC, enabling businesses to anticipate trends, understand customer behaviour, and mitigate risks. By using advanced statistical techniques and ML algorithms, it can transform historical data into actionable forecasts of future trends and make better decisions. Seamlessly integrating with Microsoft Office 365 and MS Excel, it ensures smooth incorporation into existing tools and workflows. Although it requires an additional license, the significant benefits to business planning make it a worthwhile investment. Businesses can leverage its insights to:
- Improve decision-making by relying on data-driven insights rather than intuition.
- Enhance operational efficiency by predicting and preventing potential bottlenecks.
- Drive revenue growth by understanding market trends and customer needs.
How It Works
The basic principle is that the more historical data a business has, the more accurate the forecasts will be. If a user wants to forecast in the six to 18-month range, having four to five years of actual historical data is recommended. It uses many techniques, such as data mining, statistics, ML, and AI. As with all forecasting, the accuracy of the prediction decreases the farther in the future a user pushes the model.
This AI-driven forecasting helps users understand past data trends and predict any metric in the future. The algorithm classifies existing information, identifies outliers, and surfaces relationships within your data to help you see, understand, and identify your business’s key influencers, improving the model’s accuracy.
Key Business Challenges and Predictive Analytics Benefits
Human Errors in Data Input
Manual data entry often leads to errors, resulting in inaccurate analyses and poor decisions. Predictive analytics automates data processing, reducing human error, and ensuring consistent data handling through standardised processes, providing reliable results.
Integrated Data and Analytics
Predictive analytics can seamlessly integrate with other SAP systems and external data sources. SAC can unify these datasets, whether the business data resides in SAP S/4HANA, SAP BW, or non-SAP sources. This holistic approach gives businesses a 360-degree view of their operations, improving the accuracy of predictions and enabling more comprehensive decision-making.
ML-Powered Predictions
To automatically detect patterns in historical data, learn from them, and apply those insights to make more accurate forecasts, the system leverages the power granted by ML. This capability allows businesses to refine their predictive models over time, ensuring continuous improvement in forecasting and planning processes.
Predictive Planning Integration
This feature isn’t limited to forecasting—it can also integrate with SAC’s planning capabilities. This means businesses can create dynamic, data-driven plans that automatically adjust based on predicted outcomes. For example, a company could develop a financial plan based on expected sales performance or a hiring plan based on projected business growth.
Scalability Issues
As businesses grow, they face increasing data volumes and complexity, requiring more sophisticated tools for management and analysis. SAC leverages cloud infrastructure to handle growing data volumes without additional hardware, allowing predictive analytics to scale according to their needs and ensuring cost-efficiency and optimal performance.
Real-Time Data Analysis and Forecasting
SAC operates in real-time, allowing businesses to analyse up-to-the-minute data and generate forecasts immediately. In industries where conditions change rapidly, like retail or financial services, real-time predictive analytics ensures that decisions are based on the latest information. For example, a retail business can use SAC to predict stock shortages during peak seasons and take corrective actions before they impact sales.
Predictive Scenarios and Business Cases
Predictive Scenarios offer businesses tools to gain insights from data without manually creating complex algorithms. Users can input historical data to predict future outcomes, supporting both flat-file and live (HANA) connectivity. These scenarios, known as Smart Predict, provide a workspace for building predictive models to answer business questions requiring forecasting or predictions. The insights gleaned from these scenarios can be leveraged to assist in business planning.
There are three main types of predictive scenarios, each one tailored to address a specific predictive challenge.
Classifications
This setting involves predicting a Boolean value (true or false), such as whether a customer will make a purchase. This helps categorise data into distinct groups based on patterns and characteristics, useful for segmenting customers, identifying high-risk transactions, and classifying products. For example, businesses can determine the likelihood of email addresses being active for marketing or identify store locations with higher sales potential. SAC automates these insights, generating equations to predict the probability of events and eliminating the need to sort through vast amounts of data manually.
Let’s look at the “Customer Churn Prediction” scenario using the Classification setting as an example. The goal is to use historical data to predict whether customers will churn or stay with the company. This prediction helps us gain valuable insights into customer behaviour and implement proactive measures to reduce churn and improve customer retention. For instance, in the retail industry, by analysing historical sales data, customer preferences, seasonal trends, and external factors such as weather, predictive models can forecast demand for specific products. This helps retailers adjust inventory levels, optimise supply chain processes, and plan marketing campaigns around expected demand spikes. By understanding the factors leading to churn, the company can implement targeted retention strategies, such as personalised offers or improved customer service, thus increasing loyalty.
Regressions
This setting identifies which variables impact a dataset by determining relationships between them to predict outcomes. Traditionally, this involves plotting data on a scatter plot and finding an average line. In SAC, users select one variable, and the software automatically generates regressions with relevant statistical data for all variables. This method is essential for understanding how different factors influence business metrics, such as sales drivers or cost impacts. For example, regression analysis can help businesses forecast changes in sales based on a competitor’s new product release, optimise pricing strategies by determining price elasticity, and improve financial planning by predicting future revenue based on current market conditions.
Using the Regression method, the objective of the “Sales Forecasting and Strategy Optimisation” scenario would be to forecast sales revenue changes based on a competitor’s new product release, optimise pricing strategies by determining the price elasticity of demand, and improve financial planning by predicting future revenue based on current market conditions. These models can also forecast future sales volumes by analysing data on historical sales, customer demographics, market trends, and competitor activities. Businesses can leverage these insights to plan marketing campaigns, set realistic sales targets, allocate resources such as staffing and inventory, and create accurate revenue projections.
Time Series Forecasting
This function is a popular predictive analytics application that allows organisations to forecast future values based on historical data. This capability can be applied to various business functions, such as expenses, sales, revenue, workforce, and liquidity planning. The Time Series Forecast function in SAC identifies key trends influencing future performance by generating insights from time-related data. It works seamlessly with both imported datasets and data models that include a date dimension. During forecast training, SAC automatically calculates relevant variables and statistics. Users can apply predictions to new datasets or private versions, with export options based on the data model. Use cases include predicting product revenue, forecasting cost changes, measuring cash flow, and optimising inventory levels. This feature integrates with SAC Planning, automating time series forecasts and allowing analysts to focus on strategic tasks, enabling businesses to make data-driven decisions and enhance their planning strategies.
For the time series method, let’s consider the “Forecasting monthly oil production” scenario. The objective here is to forecast the monthly oil production for the next 12 months to optimise production planning and resource allocation for the projected monthly volume. The models will analyse historical production data and market demand to assist in planning. This can help reduce logistics costs, improve delivery times, and optimise resource allocation for smoother operations.
As Part of a Cloud Strategy
SAC Predictive Analytics integrates seamlessly into a business’s larger cloud implementation and planning strategy by providing advanced data insights and forecasting capabilities. This integration allows businesses to leverage their existing cloud infrastructure while enhancing data-driven decision-making. Predictive Analytics in SAC can centralise data from various sources, creating a unified view that improves collaboration and strategic planning. By anticipating future trends and outcomes, businesses can optimise resource allocation, enhance operational efficiency, and quickly adapt to market changes, making predictive analytics a critical component of a comprehensive cloud strategy.
Predictive Analytics Workflow
To fully grasp how businesses can implement predictive analytics with SAC, it’s important to understand the workflow. Here is a step-by-step breakdown of one of these workflows:
Step 1: Data Integration
- SAP HANA
- SAP BW/4HANA
- Third-party databases (e.g., Google BigQuery, Microsoft SQL)
- Excel files and CSVs
Connecting multiple data sources lets businesses aggregate it all into a centralised hub. The more comprehensive the data, the better the predictive models SAC can generate.
Step 2: Data Preparation and Cleansing
- Data transformation (normalising, aggregating, or restructuring data fields)
- Handling missing or incomplete data (either by imputing values or removing outliers)
- Ensuring consistency across datasets
Clean data leads to more reliable and accurate predictions, so this step is crucial for building effective predictive models.
Step 3: Model Building
- Smart Predict: A user-friendly feature that simplifies the model-building process. Smart Predict helps users create predictive models for classification, regression, and time-series forecasting without requiring deep data science knowledge.
- Custom Model Creation: For users with more technical expertise, SAC allows the creation of custom predictive models using advanced ML algorithms.
In this phase, the system automatically analyses historical data and identifies patterns or trends. It uses these insights to predict future outcomes, including customer behaviour, sales forecasts, inventory needs, or operational risks.
Step 4: Training and Testing the Model
Once the model is built, it needs to be trained and tested to ensure accuracy. SAC allows users to split their data into training datasets (for building the model) and testing datasets (for evaluating its performance).
During this process, the system applies ML algorithms to the training data, enabling the model to learn from past behaviours and patterns. Once trained, the model is tested on the remaining data to evaluate how well it can predict new, unseen data.
Step 5: Generating Predictions
- The likelihood of a customer churning
- The quantity of product inventory needed for the next quarter
- The revenue forecasts based on current trends
The results are displayed in easy-to-interpret visualisations (graphs, charts, dashboards), allowing users to dive deeper into the predictive insights.
Step 6: Scenario Planning and Simulation
- What will happen if we increase marketing spend by 10%?
- How will sales forecasts change if we adjust pricing strategies?
By running “what-if” simulations, businesses can make informed decisions, align strategies, and proactively plan for future success.
Step 7: Ongoing Monitoring and Optimisation
The models in this system are not static; they improve over time. As new data becomes available, the models can be retrained to incorporate updated information, ensuring the predictions remain accurate and relevant.
SAC also offers real-time data visualisation, enabling businesses to monitor predictions and outcomes continuously. This allows decision-makers to adjust strategies in real time, optimising operations and resource allocation based on the latest insights.
Challenges and Best Practices
1. Challenges of Implementing Predictive Analytics
While SAC’s predictive analytics offers significant benefits, businesses may encounter some challenges when implementing the solution:
- Data Quality
The effectiveness of predictive analytics depends heavily on the quality of data. Incomplete, outdated, or inconsistent data can lead to inaccurate forecasts. Businesses must invest in proper data governance and cleansing processes to ensure high-quality data inputs.
- Change Management
For companies accustomed to traditional decision-making approaches, adopting predictive analytics may require a cultural shift. Business leaders must promote a data-driven mindset and ensure employees are comfortable using new tools like SAC.
- Integration with Legacy Systems
Although SAC is designed to integrate with various data sources, businesses with outdated or fragmented IT infrastructure may face challenges in connecting all necessary data points. Upgrading systems and ensuring proper integration is essential for maximising the benefits of predictive analytics.
2. Best Practices
- Provide High-Quality and Precise Data
An AI Amazon implemented as part of its hiring process infamously fell short because its training data was biased. To avoid such pitfalls, always ensure the data is balanced, diverse, and representative of the desired outcomes. This way, the models can make more accurate and fair predictions.
- Understand Your Business Needs
Understanding your business goals clearly is crucial. For instance, if you’re forecasting sales, include relevant factors like market trends and historical sales data. Aligning business objectives with Predictive Analytics ensures meaningful insights.
- Regularly Update and Validate Data
Imagine if Amazon had regularly updated their training data and validated the AI’s recommendations against real-world outcomes. Keeping your data current and periodically checking the AI’s performance ensures it remains accurate and relevant.
- Continuous Monitoring and Feedback Loop
Setting up a continuous monitoring system allows you to monitor the AI’s recommendations and outcomes. If Amazon had a feedback loop, they could have identified the gender bias early and adjusted the model accordingly.
- Transparency and Accountability
Transparency in AI decision-making is vital. Document how the AI makes decisions and ensure there is human oversight. This way, you can hold the AI accountable and maintain trust in its recommendations.
Conclusion
Predictive analytics offers immense value by turning historical data into actionable insights. In a world where precision, time, and data are crucial, predictive models allow businesses to navigate complex challenges with greater confidence. From improving HR operations to optimising marketing campaigns and streamlining supply chains, predictive analytics empowers businesses to make smarter, data-driven decisions. The importance of predictive analytics in business cannot be overstated: it ensures companies stay agile, reduce risks, and uncover hidden opportunities in a competitive market.
