The best way to reinvent the business is to predict it.
American computer scientist Alan Kay coined the phrase “The best way to predict the future is to invent it”. Quite powerful quote isn’t it? We see this play out successfully in many cases. But many businesses today also have a hard time inventing the future, even with large R&D budgets. This provokes a thought that is worth exploring. Can the opposite be true? Can companies today reinvent their business by analyzing their data to predict trends and find patterns? What if businesses have the ability to look at the crystal ball to explore what is in store for future ? Let us explore this and understand this better.
Many companies have made it a business priority to analyze the vast amount of data being generated at high velocity, inside and outside the enterprise, to get the predictive insights that will help them make adjustments to their business. You find many examples in many different industries. Telco companies are looking at data patterns to see how they can effectively manage their pre-paid and post-paid customers. Using churn analysis, we can predict today when customers will choose to pick one service or the other based on their usage patterns. Retailers are now able to predict what assortment mix that will sell fast from stores based on historic data and demographics of the store location. Banks can prevent customer attrition by understanding the circumstances that lead to the attrition. Forecasting revenue and sales numbers from CRM data helps companies to adjust their product development, placement and marketing activities to improve top-line growth. The list goes on and on.
Software solutions like SAP Predictive Analytics and SAP InfiniteInsight with SAP HANA helps many successful companies crunch through vast amount of data that is present inside and outside the enterprise systems. These solutions have helped many customers predict the trends in their business much earlier so that they can prevent any trends that could have an adverse impact to the bottom-line savings or top-line growth. The question then becomes, why are some businesses still struggling to take advantage of the capabilities in these solutions to reinvent their business? We see some challenges that reoccur in many data science implementation projects. Here are some :
- Getting to the right data – predictive insights are as good as the quality of the data. It is not just volume of data that matters; what matters is how relevant is the information trapped in the data. Predictive models need to be trained with right data set that can help in proper predictions and finding data patterns.
- Extracting data can become complex – collecting and collating enterprise data with external data can be painful. Especially when business run on multiple enterprise systems and data is distributed inside and outside the enterprise.
- Not everyone is a Data Scientist – projecting the future and discovering data patterns requires understanding of technical terms like antecedent, consequent, confidence, lift, alpha and beta values. Not every business user has a degree in statistics to interpret the results of complex algorithm. Benefits and consumption of data science does not reach to every business user to make insightful decisions.
We embarked on a path to solve some of these issues in SAP Predictive Analytics Content Adoption rapid-deployment solution(RDS). Today, this RDS has over 24 solutions that help our customers in different Industries get a jumpstart on their data science needs. The goal was to build relevant content with in SAP HANA, SAP Predictive Analytics and SAP InfiniteInsight so that business users are able to consume the results from sophisticated data analysis, at the same time data scientists are able to get a good starting point to fine-tune and train the predictive models.
What do customers and partners get from this RDS ? It includes :
- Pre-configured software content – data models, predictive models and visualizations
- Data Integration content – direct integrations to SAP sources, data collection guides for other data sources
- Configuration Guides and Best Practices that provide guidance on how best to configure and build predictive models
And wait, there is more – customers who have license for SAP HANA and SAP Predictive Analytics can download the content for free from the SAP Service Marketplace.
So what jumpstarts do we have here? Here are some examples of pre-packaged solutions:
- Store Clustering,Market
- Basket analysis
- Customer Loyalty
- Short-term checkout prediction
- Churn modeling and offer recommendation
For Consumer Products
- Product Fulfillment and Optimization
- Brand Sentiment and Sales Analysis
- Analyze Demand data
- Asset Breakdown Analysis
- Predictive Overall Equipment Effectiveness
- Maintenance Cost Analysis
- Profitability Analysis with Financial Trends
- Late Payment Management
We continue to enhance the set of pre-packaged solutions to help our customers reinvent their business . You can easily download the content for free from the SAP Service Marketplace and try these scenarios. Some of these pre-configured solutions will be available as a trial very soon on popular clouds environments like AWS.
What are some of your challenges ? What problems do you see in your quest to scientifically analyze your data? Do you have some success stories that you can share with this community ? We would be happy to hear from you. As we continue to build these scenarios, your feedback will help us build more content that could directly benefit your business.