How Analytics Answer the Most Challenging Business Questions

As first mentioned in my last blog, I am excited about Teradata’s focus on giving organizations a way to use analytics to answer tough business questions. What type of business questions is your organization able to answer?  

Business questions that are answerable by analytics can be grouped into three categories:
 

• Descriptive – like the rear-view mirror in a car, descriptive analytics is about answering questions through looking back in time to report on history.

• Predictive – like a modern GPS navigation system where multiple factors including current and projected traffic patterns are used to estimate the time to destination, predictive analytics is about understanding the future.

• Prescriptive – like the more advanced features in a car such as collision avoidance and even self-driving cars, prescriptive analytics is about advising on possible outcomes and the specific actions to take.

While answering all types of questions adds value, being able to leverage all your data to answer questions that cannot simply be addressed by looking at aggregated (or siloed) data has tremendous upside. For example:
 

• Volvo monitors the mileage and condition of every car to the kilometer, so they can determine when to send maintenance alerts, track which features drivers use, and stock the most popular configurations at each dealership.
• Carrying 15% of the world’s annual GDP, Maersk Line is changing their culture with data, analytics, and the Internet of Things. Pervasive Data Intelligence provided Maersk Line the parameters they needed to dramatically optimize energy and shipping costs.

Over the last year, I have had my own experience in helping our customers answer the difficult questions that impact their business such as “How many of each size shirt should a retailer request from their manufacturer in a single pack?” In another project, the important question was “How much did the specific marketing spend for each channel influence customer purchases?”

As a matter of fact, I was hosting an internal knowledge sharing session (with other consultants at Teradata) on our engagement when I was asked, ”Why did we start with solving a difficult problem instead of starting with some easier ones?” The simple answer is that we focused on the area that matched the business need of the customer.
In more general terms, think of it as a multi-step process:
 

1. Determining where an enterprise’s pain point lives.
2. Proving the quantifiable business benefit in resolving it. 
3. Providing a dashboard solution that has pre-loaded models for the most common business problems that effect businesses like yours, so you’re able to quickly and efficiently answer those questions.

Descriptive analytics, like the dashboards described above, add value by providing a way to consistently measure performance overtime. These dashboards are the mechanisms that are utilized to both understand what has happened within your business’ past, but also to measure the impacts of any solutions you employ.

However, the bottom line is why stop at descriptive analytics?  Why not tap into the vast amount of rich data available and utilize advance analytic methods for predictive and prescriptive analytics, such as machine learning. Go ahead, rise to the challenge and don’t be afraid to ask hard questions. It’s not only possible to ask these questions, with Teradata Vantage you’ll be able to answer them with concrete quantifiable solutions.