- Go beyond predictions to smarter decisions
- Find cost reductions without capital investment
- Optimize logistics, scheduling, capital budgeting
- Understand and mitigate risks before they happen
- Build analytics expertise within your own team
- Leverage skills of the business analysts you have
- Enable analysts and developers to work together
If You're a Manager Who Wants To...
Get Results Quickly on a First Project
With our powerful but low-cost software and your team's Excel expertise, you can tackle a real problem, leverage the data and analysis skills you have, get results in months not years -- and move easily to the cloud.Build Analytics Expertise In-House
With our software wizards, guides, support, and online courses, your analysts and developers can do the work of "data scientists" and "management scientists," and grow your own analytics team expertise.What you need to know: Data Science (Predictive Analytics and Machine Learning), while not new, are more actionable than ever. They enable you to use all your data to make classifications and predictions, on a case-by-case basis. For example, instead of an overall sales forecast, you can make predictions about each individual customer’s likelihood of buying (or “churning”). Here's the Wikipedia description of Data Science.
With Data Science plus simple business rules (such as “is the churn score > 0.6? Then send a marketing offer”), you can impact business results. But Data Science doesn’t address complex decisions such as “how do I allocate my marketing budget over email, online ads, social media and more?” or “how do I manage inventory and reorders to minimize stock-outs and holding cost?” (Management science does.)
In the past, managers couldn’t answer both kinds of questions in an integrated way. But now you can … if you have the right analytical and model-building tools.
Data Driven, Machine-Generated Models
The models used in data science are standard mathematical forms -- such linear or logistic regression, or neural networks -- 'trained' or fitted to your past data by machine learning algorithms. Human expertise is needed to ask the right questions, and select the right data to be fed into machine learning, but the models are automatically generated. (In contrast, management science models describe the structure of some part of your present or future business -- they require a human modeler with business domain knowledge, model-building expertise and tools.)
