Data prediction is the fourth critical first step in finding the business opportunities in big data.
It’s the step that can explain why those quarterly sales are going up and down. It can also help predict whether those fluctuations will continue.
In short, prediction is where the rubber meets the road.
Data prediction takes information revealed in data and uses it to forecast events or trends in the future. It can also look at the present and past, too.
Data prediction can be applied to any type of unknown in any time frame. It can also be used to forecast outcomes in any time frame, and make real-time forecasts based on historical patterns of activity.
Data scientists design and apply algorithms to sets of data they have already found, cleaned, and categorized. In the example we’ve been following throughout this series of a data analyst identifying and cleaning data for a presentation, this is the step where we discover why the sales results have been rising and falling over previous quarters.
In our example, let's say that the data scientists find out that quarterly sales are fluctuating due to a price hike for the web service. This has caused some customers not to renew their subscriptions.
That insight goes to the sales department to develop incentives that include a price promotion and a month’s free service on top of a year’s renewed subscription. Data scientists can then test multiple incentive offers to predict which one will be most effective.
It’s possible for data scientists to run these tests one at a time. But that takes a lot of effort and results in long and complex analytical projects.
When data stays mostly the same and there are only a few variables to analyze over and over, a manual predictive analysis isn’t the best approach.
Automated Data Prediction Offers Advantages
A better approach is to use software that automates data prediction through machine learning. Automating the immense power of machine learning algorithms makes data prediction faster, better, and more powerful.
Predictive analysis software scans the organization’s proprietary data. It also looks at open sources of relevant data. Automated data prediction can find rare and non-obvious connection patterns faster than human beings can.
Organizations use data prediction to uncover the top drivers of outcomes and to do root cause analysis that can lead to better outcomes. They want to know what is producing unwanted outcomes so they can fix it. Process improvement is done much faster with automated data prediction.
Automated data prediction is a new field. It will become far more sophisticated very quickly due to surging demand for even deeper data analysis.
New Tools Call for Updated Skills
IT professionals who build and manage networks or work with data must keep their skills current.
The Internet of Things (IoT) is transforming manufacturing and IT. Operations are merging with networks. This development has created huge demand for professionals with the skills to understand and manage interactions among IT, networking, and traditional control systems.
A new IoT-focused Cisco Industrial Networking Specialist certification consists of lab-based training. It is for plant administrators, control engineers, and IT/network engineers in manufacturing. It gives them the knowledge and skills to build, manage, and operate converged industrial networks.
In addition, a new CCNA Industrial certification enables administrators to handle digital networks that are analytics-driven and always adapting. It also helps them keep pace with the demands of their profession, and improve their relationships with end users in their organizations by taking more of an advisory role.
Administrators of fully digital networks must do more than keep them operating. They must manage constantly varying traffic flows that are used for analytics.
More devices are creating data than ever before. It’s now up to us to do something amazing with it.
I welcome your thoughts and opinions on how best to unlock the potential of big data. Please join the conversation in the comments below.
For more information, visit the Cisco Data and Analytics page.
Learning@Cisco product manager Neeraj Chadha has more than 20 years of experience in the networking industry. Over that time, he has functioned as a software developer and network engineer, and in various aspects of product management. Currently, he guides the overall product strategy and evolution of Cisco courseware and certifications around Wireless, Collaboration, and Big Data and Analytics. Neeraj's primary areas of focus include technology trends, digital transformation, continuing education, and product strategy.