Predictive forecasting is all around us — Google’s autocomplete, weather forecasts, your GPS knowing when you’ll arrive at your destination — they’re all forms of predictive analytics.
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What is predictive analytics?
Predictive analytics uses multiple techniques such as data mining, statistics, machine learning, and artificial intelligence to predict the possibility of something happening based on historical data.
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Predictive forecasting may seem mysterious, but the logic is actually quite easy to understand.
The way many machines work is by following a strict set of commands outlined in computer code. An alarm clock, for instance, is programmed to perform a few relatively simple tasks — display the time and turn on the radio at a pre-programmed time.
But what if instead of programming the alarm clock to turn on the radio at a certain time, you wanted it to wake you up at the optimal time based on your sleep cycle? For that, you would need a more sophisticated alarm clock, more data, and more coding.
Assume for a moment that your alarm clock is a more sophisticated machine that can track all of your sleep data such as:
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- When you go to bed
- Your sleep cycles
- All the times you toss and turn
- When you get up to use the washroom
- What position you sleep
- Your body temperature
After a while, you would have a large volume of your data of your sleep cycles and nighttime habits. Your sophisticated alarm clock could then use mathematical algorithms to look for patterns within the dataset. For instance, it notices that you hit snooze twice on Mondays, you wake up at 5:30 a.m. on Wednesdays, tend to sleep in on Saturdays, and so on.
But what if we fed the machine more data such as your productivity levels at work, your mood, your caloric consumption, your metabolic rate, your fitness routines, your social habits, and so on. It’s likely to find even more patterns and relationships within the data.
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It can tell that you’re more productive on the days you get more rest. You’re in a better mood on Fridays and Saturdays. You tend to go out for drinks on paydays, and you tend to order particular kinds of foods following days where you were most active, etc.
Using all this data, it seems as though the machine would know your habits quite well, right? It also seems likely to be able to make intelligent guesses, or predictions, with a high degree of accuracy as to your future behavior based on what you did in the past.
![Sap predictive analytics software download Sap predictive analytics software download](/uploads/1/3/3/9/133936574/775859968.jpg)
This is precisely how predictive forecasting works. SAP Analytics Cloud predictive forecasting looks at historical data to find patterns, and then uses those patterns to make predictions about future outcomes.
Predictive forecasting for business
As we saw in the previous example, predictive forecasting uses historical data to predict future outcomes. For example, a sales manager may use predictive forecasting to project sales revenue for the upcoming season.
Predictive forecasting takes into account different values, trends, cycles and / or fluctuations in your data to make predictions. This is a powerful data-driven approach that can be leveraged to aid your planning process.
SAP Analytics Cloud predictive forecasting gives you an unbiased understanding of your key business influencers and allows you to dive deeper into your data with interactive visuals. You can then use these findings to influence business decisions.
How to use predictive forecasting in SAP Analytics Cloud
To use predictive forecasting in SAP Analytics Cloud, upload a dataset and make sure there’s historical data so the system can use this to look for trends. Once you create a chart, you can apply predictive forecasting.
First, select a chart you want to apply predictive analysis to and change it from a trend chart to a Time Series chart. Note, it must be enriched with dynamic hierarchies to show different levels of granularity.
Next, select one of the forecast options:
- Automatic Forecast — forecasts future values based on historical data within the chart
- Advanced Options — offers the ability to include additional data as a potential forecast influence
The forecast is added and is indicated by the highlighted area with the dotted line. When you select a data point, you can see the upper and lower confidence bounds (i.e. probability of accuracy in the prediction). You can also edit the forecast if you like such as change the color or add another variable.
SAP Analytics Cloud predictive forecasting helps you understand past data trends to predict any metric in the future. The predictive algorithm classifies existing information, identifies outliers, and surfaces relationships within your data to help you see and understand your business’ key influencers.
You can use SAP Analytics Cloud predictive analytics to:
- Discover key influencers of your KPIs such as revenue, churn, and productivity
- Explore interactive charts and graphs, automatically generated based on your query
- Create predictive forecasts to predict future results based on historical data
To see how SAP Analytics Cloud predictive forecasting can benefit your business, try it today for free.
Please note, you will need planning rights and a planning license to run a predictive time series forecast.
Please note, you will need planning rights and a planning license to run a predictive time series forecast.
Additional Resources
- Video: Add a Predictive Forecast to a Trend Time Series Chart
- Video: Create a Predictive Time-Series Forecast for Planning Data in a table
In the United States, the cliché “Christmas comes early” is often applied to the American Thanksgiving holiday, which occurs each year on the last Thursday of November. However in the case of SAP Predictive Analytics, the development teams (they don’t seem to like being called “elves”…) are in Ireland and France, so things are clockwork-like “business as usual”. That means the delivery of SAP PA 2.4 really *is* an early Christmas present for us all!
We are proud to announce that SAP Predictive Analytics 2.4 has been formally released and is now available on SMP for licensed customers. For those of you who are not already using it, you can download the 30-day trial here.
What’s New in PA 2.4?
You can learn more about everything in SAP Predictive Analytics 2.4 in the What’s New document, but here are some highlights of this release:
Even Bigger, Better Support for Predictive in SAP HANA:
- HANA SPS10: Certified support for SAP HANA SPS10 using both Automated and Expert interfaces as well as the SAP HANA Automated Predictive Library (APL).
- APL Training Delegation: Automated Analytics now supports model training delegation to the SAP HANA Automated Predictive Library (find out more about the APL here: What is the SAP Automated Predictive Library (APL) for SAP HANA? )
- APL Stored Procedures: The APL now comes with SQLScript stored procedures to take care of signature tables, table types, and wrappers – dramatically reducing the time and code required to develop with the APL.
New Predictive Features:
Expert Analytics has been enhanced to include additional native-to-HANA components:
- SAP HANA Demand Forecasting Component: Perform high volume, near real-time processing of algorithms to create sales predictions, future demand, data on price elasticity, natively in HANA.
- SAP HANA Optimization Function Component: Create reusable objective functions with linear constraints to solve complex optimization functions like how to maximize profits on a product.
- SAP HANA Sentiment Analysis Component: Analyze complex streams of text to determine the opinions and influencing factors of discovered entities and transform your unstructured data into easily understandable categories.
Updates and Refinements:
![Sap Predictive Analytics Download For Mac Sap Predictive Analytics Download For Mac](/uploads/1/3/3/9/133936574/204499531.jpg)
- Updated database support: Automated Analytics now supports IBM PureData System for Analytics 7.1.x.
- Improved installation: Now apply licenses automatically during silent installations.
- Improved support process: Automated Analytics can now generate log files from errors to simplify obtaining support from SAP.
Wanna See More?
Our product management team has also prepared videos to show you more about what’s awesome in PA 2.4:
How To Get Started?
- Download the trial!
- Check out: Frequently Asked Questions – Downloading, Installing and Activating SAP BusinessObjects Predictive Analytics
- Check out: Frequently Asked Questions – Downloading, Installing and Activating SAP BusinessObjects Predictive Analytics
- Check out the online materials and tutorials:
- See the high level messaging and use cases of how other customers are using PA at www.sap.com/predictive
- Check out: Official Product Tutorials – SAP Predictive Analytics, SAP Predictive Analysis and SAP InfiniteInsight
- Participate in the SCN Community: SAP BusinessObjects Predictive Analytics
- Learn, ask questions, get answers!
Looking Back on 2015 and Forward to 2016!
This has been a banner year for us on the predictive team. We released *five* versions of SAP Predictive Analytics,
unleashed our Automated Predictive Library for HANA-native predictive workloads, and we put our final touches on SAP HCP predictive services (which will launch early next year).
But we are not resting. Our development team has already started work on the next version within our PA 2.x line and internal development on our next generation SAP Predictive Analytics 3.x platform is ongoing. We “predict” that 2016 will be an even bigger year for us with a new server platform, new cloud capabilities, and of course continuing innovation on the core components that make SAP Predictive Analytics one of the best predictive tools available on the market.
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Stay tuned to SCN @ SAP BusinessObjects Predictive Analytics for more news, webinars, and whitepapers. Better yet, sign up for “Email Notifications” on the top right of this page!
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