BUDGETS ARE SHIFTING TOWARD A “CLOUD-FIRST” AND “CLOUD-ONLY APPROACH


Gartner has recenty predicted that by 2020, a corporate “no-cloud” policy will be as rare as a “no-internet” policy is today. CIOs will increasingly leverage a multitude of cloud computing providers across the entire IT stack to enable a huge variety of use cases and meet the requirements of their business unit peers. Indeed, the tides are shifting toward a “cloud-first” or even “cloud-only” policy... 

Marc Wilczek

How to Start Incorporating Machine Learning in Enterprises

Incorporating Machine Learning in Enterprises

The world is long past the Industrial Revolution, and now we are experiencing an era of Digital Revolution. Machine Learning, Artificial Intelligence, and Big Data Analysis are the reality of today’s world.

I recently had a chance to talk to Ciaran Dynes, Senior Vice President of Products at Talend and Justin Mullen, Managing Director at Datalytyx. Talend is a software integration vendor that provides Big Data solutions to enterprises, and Datalytyx is a leading provider of big data engineering, data analytics, and cloud solutions, enabling faster, more effective, and more profitable decision-making throughout an enterprise.

The Evolution of Big Data Operations

To understand more about the evolution of big data operations, I asked Justin Mullen about the challenges his company faced five years ago and why they were looking for modern integration platforms. He responded with, “We faced similar challenges to what our customers were facing. Before Big Data analytics, it was what I call ‘Difficult Data analytics.’ There was a lot of manual aggregation and crunching of data from largely on premise systems. And then the biggest challenge that we probably faced was centralizing and trusting the data before applying the different analytical algorithms available to analyze the raw data and visualize the results in meaningful ways for the business to understand.”

He further added that, “Our clients not only wanted this analysis once, but they wanted continuous refreshes of updates on KPI performance across months and years. With manual data engineering practices, it was very difficult for us to meet the requirements of our clients, and that is when we decided we needed a robust and trustworthy data management platform that solves these challenges.”

The Automation and Data Science

Most of the economists and social scientists are concerned about the automation that is taking over the manufacturing and commercial processes. If the digitalization and automation continues to grow at the same pace it is currently happening, there is a high probability of machines partly replacing humans in the workforce. We are seeing some examples of the phenomena in our world today, but it is predicted to be far more prominent in the future.

However, Dynes says, “Data scientists are providing solutions to intricate and complex problems confronted by various sectors today. They are utilizing useful information from data analysis to understand and fix things. Data science is an input and the output is yielded in the form of automation. Machines automate, but humans provide the necessary input to get the desired output.

This creates a balance in the demand for human and machine services. Both, automation and data science go parallel. One process is incomplete without the other. Raw data is worth nothing if it cannot be manipulated to produce meaningful results and similarly, machine learning cannot happen without sufficient and relevant data.

Start Incorporating Big Data and Machine Learning Solutions into Business Models

Dynes says, “Enterprises are realizing the importance of data, and are incorporating Big Data and Machine Learning solutions into their business models.” He further adds that, “We see automation happening all around us. It is evident in the ecommerce and manufacturing sectors, and has vast applications in the mobile banking and finance.”

When I asked him about his opinion regarding the transformation in the demand of machine learning processes and platforms, he added that, “The demand has always been there. Data analysis was equally useful five years ago as it is now. The only difference is that five years ago there was entrepreneurial monopoly and data was stored secretively. Whoever had the data, had the power, and there were only a few prominent market players who had the access to data.

Justin has worked with different companies. Some of his most prominent clients were Calor Gas, Jaeger and Wejo. When talking about the challenges those companies faced before implementing advanced analytics or machine learning he said, “The biggest challenges most of my clients face was the accumulation of the essential data at one place so that the complex algorithms can be run simultaneously but the results can be viewed in one place for better analysis. The data plumbing and data pipelines were critical to enable data insights to become continuous rather than one-off.”

The Reasons for Rapid Digitalization

Dynes says, “We are experiencing rapid digitalization because of two major reasons. The technology has evolved at an exponential rate in the last couple of years and secondly, organization culture has evolved massively.” He adds, “With the advent of open source technologies and cloud platforms, data is now more accessible. More people have now access to information, and they are using this information to their benefits.”

In addition to the advancements and developments in the technology, “the new generation entering the workforce is also tech dependent. They rely heavily on the technology for their everyday mundane tasks. They are more open to transparent communication. Therefore, it is easier to gather data from this generation, because they are ready to talk about their opinions and preferences. They are ready to ask and answer impossible questions,” says Dynes.

Integrating a New World with the Old World

When talking about the challenges that companies face while opting for Big Data analytics solutions Mullen adds, “The challenges currently faced by industry while employing machine learning are twofold. The first challenge they face is related to data collection, data ingestion, data curation (quality) and then data aggregation. The second challenge is to combat the lack of human skills in data-engineering, advanced analytics, and machine learning

You need to integrate a new world with the old world. The old world relied heavily on data collection in big batches while the new world focuses mainly on the real-time data solutions

Dynes says, “You need to integrate a new world with the old world. The old world relied heavily on data collection while the new world focuses mainly on the data solutions. There are limited solutions in the industry today that deliver on both these requirements at once right now.”

He concludes by saying that, “The importance of data engineering cannot be neglected, and machine learning is like Pandora’s Box. Its applications are widely seen in many sectors, and once you establish yourself as a quality provider, businesses will come to you for your services. Which is a good thing.”

By Ronald van Loon

Ronald van Loon

Ronald has been recognized as one of the top 10 Global Big Data, IoT, Data Science, Predictive Analytics, Business Intelligence Influencer by Onalytica, Data Science Central, Klout, Dataconomy, is author for leading Big Data sites like The Economist, Datafloq and Data Science Central.

Ronald has recently joined the CloudTweaks syndication influencer program. You will now be able to read many of Ronald's syndicated articles here.