In the past, it was hard for big data to explain and answer some key and fundamental questions such as: what happens? Why it happens? And predict what would happen next? Today, big data scientist have understood and created a system that answers those questions. Therefore, predictive analytics that answers the most sophisticated questions and predicts the future is no longer the rocket sciences. An algorithm enables us to not only answer but ask questions where necessary in near real time.
Data scientists that can help.
Data scientists with statistical algorithm and deep machine learning techniques and extensive experience knows well how to identify the likelihood of future outcomes based on near real time data. This is because they correlate such data with a historical data by writing a sophisticated algorithms capable of understanding not only the relevant data but also historical data insight. This enables them to predict the likelihood of what would happen next and explain why it has to be so.
Big data Hadoop fuels the fire.
As the amount of data continues to explode and new methods like the Internet of Things (IoT) emerge, both the public and enterprise sectors are looking for ways to more effectively manage and analyse their data for competitive advantage. Predictive analytics has been cited as a key form of analytics for big data. This has helped to drive the popularity of the technology like Hadoop ecosystems and far reaching deep machine learning methodology. For example, 70% of corporations surveyed are utilizing predictive analytics on a big data initiative.
Hadoop Ecosystem is gaining momentum among those using predictive analytics. Perhaps not surprisingly, 75% of the active group has plans for utilizing Hadoop over the next two years as part of their analytics struggles.
Analytic Ecosystems are the next wave, especially for those adopting big data solutions. There is no one-size-fits-all approach to gathering the data infrastructure. An ecosystem approach might include a business intelligent modernization along with Hadoop as the data processing and data management infrastructure supporting analytics. Most organizations will want to make use of their existing infrastructure, so an ecosystem approach makes sense.
The benefits of predictive analytic.
There are a myriad of benefits that can be drawn from predictive analytic. Such benefits comprise of, but are not limited to:
- Avoid unexpected downtime by predicating failures in systems.
- Timely detect machine failures consistently
- Predictive and prescriptive analytics
- Enhance productivity while reducing avoidable costs
- Improve product quality by knowing the unknowns
- Drive strategic decision making
- Identify new business opportunities
- Faster response to business change
- Predictive analytics in telecom companies.
In modern days, the telecom companies benefit from predictive analytics by applying it to avoid network outage and create higher customer satisfactions. They use open source and non-open source technologies such as Hadoop ecosystems that comes with a deep machine learning algorithm capabilities needed to create a cutting age and predictive analytics tools for future data analytics obtained via relevant streaming and historical data.
How does the predictive analytics algorithm in telecom works?
This algorithm works perfectly with any industry when used to avoid unexpected operational downtime and improve efficiency. Particularly, it works best in telecommunications by easily predicting fault within the network and helping avoid network downtime by analysing network logs, service records and their Communications Data Model (CDM) software. The algorithm can also identify failures before they occur by analysing data in both real time and near real time. The benefit of predicting future data failure is enormous for telecom companies because it can reduce the service cost at least by 40%, improve productivity through business continuity as well as reducing customer churn.
Predictive analytics solutions.
There are handful out of the box technologies out there that can help you with predictive analytics solutions. However, when it comes to specific industry, you need a team of data scientists and data experts to develop predictive analytics applications for your specific needs.
The competition between big companies in prediction analytics is enormous but HPE’s London discovery in December 2015 show unmatched custom build algorithm for almost all industries, including the automotive, telecommunication, pharmaceutical, financial and internet of things (IoT).
HPE has shown the capabilities of deep machine learning for predictive analytics and predictive maintenance powered by HPE’s out of the box technologies in Haven platform among others such as HPE IDOL with predictive analytics and sentiment analytics.