In the land of the phone, Big Data’s bigger than big and Artificial Intelligence is real

#Big Data & Highload
#Machine Learning
#High Tech
September 21, 2017 4 min read

In America alone, people are looking at their mobile devices more than 9 billion times a day – up 13 percent from last year, according to Deloitte.
Telecom companies can make mammoth business gains with Big Data and Artificial Intelligence — if they can harness and use Big Data expertise and machine learning expertise.

Now we have the power to get things done

For decades, providers have delivered and collected vast measures of information about calling patterns, location data, wireless-data usage and more. Little if anything was done with all that information, though, largely due to costs and efficiency issues.


“Until recently, much of that data was discarded as there was no efficient way to mine value from it and storing it was expensive,” says MapR, which provides a converged data platform.
The firm is among those that anticipate big, fast strides in Big Data application development and ever-increasing improvements in telecom software.

And it’s not just talk

More concretely, globally respected consulting firm PWC believes the development of Artificial Intelligence will deliver serious gains in efficiency and profitability through:

  • Optimizing routing and quality of service by analyzing network traffic in real-time;
  • Analyzing call data records in real-time to identify fraudulent behavior immediately;
  • Allowing call center reps to flexibly and profitably modify subscriber calling plans immediately;
  • Tailoring marketing campaigns to individual customers using location-based and social networking technologies;
  • Using insights into customer behavior and usage to develop new products and services.
  • The emerging pattern of Big Data adoption is focused on delivering measurable business value, according to IBM, a leader in Artificial Intelligence.

“The overall challenge that inhibits Big Data adoption as (telecom companies) move through the Big Data adoption stages – from education, building a base of knowledge, through exploration and engagement, to execution (implementing Big Data at scale) – is understanding the potential that Big Data presents, i.e. the ability to articulate a compelling business case.”


You need to deliver results – and you can

There’s an ongoing need for other major investments, which makes it that much more critical for Big Data investments to provide real, clear results, IBM notes.

“The current telecommunications industry climate – especially slowing or flat revenue growth and the increasing demand for capital expenditures to build 4G/LTE networks – has left (companies) with little appetite for new technology investments without measurable benefits – a requirement that is of course, not exclusive to Big Data initiatives.”


There’s gold in the data

The prospects and gains to be made are staggering, though, and they’ll keep getting bigger, Infosys says.

“Scale, complexity and continued growth make the telecommunications industry a great candidate for automation,” the Indian firm says in a 2017 report.

“It is expected that telecoms networks will expand 10,000 times or more in size in the digital age, hugely outstripping the capacity of human beings to manage them. So, the emergence of highly sophisticated, intelligent automation technologies couldn’t have come at a better time.”

Get it better, get it faster, get it cheaper

Using automation and Artificial Intelligence, telecom companies can slash operating costs and at the same time improve service speed and customer-experience quality, Infosys believes.

“AI is already enabling the core of the telecoms business, with machines doing intelligent human tasks like reading network content to decide how to route traffic.”

Advancements in telecom software are essential – and they’re the way, according to the consulting firm.

“But this is only the beginning. The next stop is the self-optimizing network, where once the designer sets goals and limits, the network control software will structure the network based on existing conditions. The impact will be no less dramatic at the front end. Today, analytics is still doing relatively simple things; the next goalpost is to read obscure and not very visible data patterns to usher a change in customer service, security and every other function.”

Developments in the works are both diverse and expansive, according to Infosys.

“By abstracting the software layer from the physical network infrastructure, AI is well placed to take over many mundane and repetitive operational tasks. The impact of this is that valuable and skilled engineers can be freed up from low-level monitoring and configuration tasks to work on more critical and higher-value activities including building new and expanded network infrastructure, physical repairs, research and development and more.”

Clearly, this is no fad or trendy toy. As far back as 2102 global organizations were creating 2.5 Exabyte (2.5×1018) of data every day.

big data

Big Data + Artificial Intelligence = business results

And Big Data is essential to the success of Artificial Intelligence, Datameer, a data analytics provider, points out.

“AI itself doesn’t reason and deduce the way human minds do. Instead, it learns through trial and error. That’s why having large amounts of data is more important than ever. The more data AI has, the more accurate it will become. They are true partners, and one would not be good without the other,” Datameer says.

The challenges, though include effective Big Data application development, according to IBM.

“Big Data does not create value, however, until it is put to use to solve important business challenges. This requires access to strong analytics capabilities that include both software tools and the requisite skills to use them. Examining those CSPs already engaged in Big Data activities reveals that they start with a strong core of analytics capabilities designed to address structured data. Next, they add capabilities to take advantage of the wealth of data coming into the organization that is both semi-structured (data that can be converted to standard data forms) and unstructured (data in non-standard forms).”


Oh, by the way: We know what we’re talking about

Innovecs, a leader in Artificial Intelligence, will help you take full advantage of machine learning as you move forward with telecom software development. Our professionals leverage the latest technology and statistical methods for you through our AI & ML services. We know what it takes to succeed in the telecom world, we have machine learning expertise, we have Big Data expertise and we are here to help.

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