We are experiencing a digital revolution. Machine learning, artificial intelligence, and big data are the buzzwords of the day. Yet most corporations are having a hard time to start implementing machine learning solutions or even see the usefulness of these new technologies. Unfortunately, organizations which don’t get to grips with concepts like deep learning and big data soon will be left behind. (See also: Sayonara, sucker! How digital companies are leaving the rest behind)

 

A History Of Big Data

Truth be told, most companies face challenges similar to what their own clients are facing. Before the advent of modern computing capacities and advanced machine learning concepts, data was always complicated; piling up on hard disks without ever becoming useful.

There was a lot of manual aggregation and crunching of data stored mostly locally. Few people had access, few people knew what to do with it, and management had no idea how to make these data useful for corporate decisions or the optimization of processes.

The biggest challenge was cleaning up the data, and trusting the available data enough to apply the different analytical algorithms, and analyze the raw data, and visualize the results in meaningful ways for the business to understand. Data was always there, but it was always useless.

To be truly useful, data did not just have to be analyzed once, but continuously updated over months and years, in order to discover trends and the effect of strategic business decisions. With manual data engineering practices, it was very difficult to meet the requirements of clients. What was need was trustworthy, easily manageable and affordable data management platforms.

Automation and Data Science 

Most economists and social scientists are concerned about automation taking over manufacturing processes and commercial activities. If digitalization and automation continue to grow at the same pace as is currently happening, there is a high probability of machines partly replacing humans in the workforce. We are seeing some examples in our world today, but this trend will be even more prominent in the future.

There is another form of automation though that is going mostly unnoticed. Data scientists are providing solutions to intricate and complex problems confronted by managers and operators today. They are utilizing useful information from data analysis to understand, fix, and predict correlations, events, and problems. In that sense, data science is a kind of input, and the output is a form of invisible automation.

There is thus a balance between the demand for human knowledge and know-how and the services machines deliver based on structured data. Automation and data science go hand-in-hand; 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.

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Incorporating Machine Learning into Business Models

Enterprises are realizing the importance of data, and are incorporating big data and machine learning into business models. Whether it is e-commerce or manufacturing or even banking and finance, there is a lot of data out there; data which has lain dormant for decades, waiting to be finally utilized. The demand for such data analysis has always been there, but it is only now that we are learning how to use it, visualize it, share it, and benefit from it on a larger scale.

Still today, the biggest challenge for most organization is the selection and verification of essential data in one place so that complex algorithms can be run simultaneously and results can be viewed in easy-to-interpret form.

Rapid Digitalization Is Threatening Laggards

We are experiencing rapid digitalization mostly due to two major reasons. Firstly, technology has evolved at an exponential rate in the last couple of years and secondly, organization culture has evolved to embrace data and data analysis. Herein lie the two biggest challenges for organizations:

  1. Understanding the technological change and selecting the right tools
  2. Changing the corporate culture and creating a data-friendly, open environment

With the advent of open source technologies and cloud platforms, data is now more accessible; it is easier to use interpret, visualize, distribute. More people have access to information, and they are using this information to their benefit. This is a great motivator for everyone in the organization.

In addition to the advancements and developments in technology, there is also the workforce effect: a whole new generation of young people is joining the talent pool, people who are tech-savvy and are used to relying on technology for mundane tasks. They are more open to transparent communication and used to working with data as the basis of business decisions and operational processes. They are also used to sharing data and it is easier to gather data from this generation because they are ready to talk about their opinions and preferences. \

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Integrating The New World And The Old World

Apart from technology and corporate culture, organizations also face challenges related to the data itself. Data collection, data ingestion, data curation (quality) and then data aggregation are hot topics. Legal challenges to the way corporations gather and use data abound (e.g. GDPR). But these are comparatively easy to overcome. Far bigger ist the human resources challenge; there is a massive lack of human skills in data engineering, advanced analytics, and machine learning.

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 satisfy both demands at once.

Data engineering is probably the job most in demand in the coming years. Without data engineers who understand the source, quality, and treatment of data, machine learning, deep learning, and even artificial intelligence, cannot progress. After all, any solution is only as good as the data it is based on.