Machine learning is rapidly becoming one of the most important trends in the enterprise software ecosystem. A combination of Moore’s law about GPU performance, the raise of big data and the evolution of technology stacks, have finally made the promise of machine learning a reality for many enterprises. However, the promise of machine learning extends beyond a standalone discipline and has the opportunity to power the next wave of innovation in the enterprise.
The last decade has seen a renaissance of innovation in enterprise software powered by movements like cloud, mobile and big data. With those technologies established as mainstream capabilities, the market is turning its attention to technologies that can become the next big thing in enterprise software. From the current fast growing technology trends in the market, machine learning seems to offer a seamless path to power the next wave of innovation in the enterprise.
There are many factors contributing to the adoption of machine learning technologies in enterprise environments. The rapid evolution of the machine learning stacks, the adoption of big data technologies as well as the explosion in the volumes of data processed by organizations are just some of the elements that are conspiring to embrace more advance data analysis techniques. For a technology movement to become transformational trend in the enterprise, it has to combine a strong technical value proposition with elements such as distribution, market maturity or cost of adoption. From everything we can see, machine learning has all the ingredients to become the next power the next wave of innovation in the enterprise.
5 Reasons Why Machine Learning Will Transform the Enterprise
Machine Learning Stacks are Finally Useful
After almost 20 years of evolution of machine learning stacks, they have finally reach a point in which they are being widely adopted by developers and incorporated into third party applications. In that sense, some of the machine learning platforms and frameworks based on technologies like R and Python enjoy large and active developer communities that accelerating the level of innovation in the data science space.
Data Volume Challenge
The explosion in the volumes of data stored by organizations has drastically improve the viability and efficiency of machine learning models. As you can imagine, machine learning supervised and unsupervised algorithms tend to be more effective as they process larger data sets.
It’s not About Internal Data Anymore
For the last 30 years, enterprise applications have been built using relational database models optimized to store internal data. The explosion of social and mobile technologies have completely changed that scenario requiring applications to process diverse set of unstructured and semi-structured data. In that context, machine learning algorithms tend to be very effective to analyze large volumes of external data and correlate it with internal data sources.
Complementing Information with Knowledge
As the volume of information processed by organizations increase, machine learning models will become essential to extract intelligence from those data sources using techniques like statistical regression, classification or clustering. From that perspective, machine learning solutions are becoming the channel to transform raw information into knowledge that can be leveraged in enterprise business processes.
One of the greatest things about machine learning solutions in the enterprise is that they can leverage existing distribution channels as part of SaaS systems or other line of business applications. In that context, SaaS systems can be extended with specific machine learning models to provide better insights and predictions about its data.
The Machine Learning for X Model
Replace X with your favorite industry and you have a solid business model ^^
The rapid evolution machine learning stacks is taking place not only on the platform side but in the form of industry-specific solutions that are leveraging data science to enable additional levels of data insights and intelligence. In this model, machine learning is serving as an enabler to advanced domain specific capabilities. Below we can find some examples of sectors that are being transform by the use of machine learning and data science.
One of my good friends always jokes that some of the best minds of our generation are spending their days finding better ways to place advertisement. That’s’ just one of the best examples of how machine learning is transforming marketing. Predictive lead scoring or intelligent ad and content placement are some of the new and popular marketing techniques that are actively rely on machine learning models.
Forecasting analysis, customer sentiment analysis, customer churn predictions are some of the examples of machine learning disrupting traditional sales processes. These techniques are starting to be included in traditional sales tools such as CRMs and ERPs to create more intelligent sales processes.
Machine learning is powering the next generation of innovation in the enterprise security space. Techniques like security Threat analysis, malicious pattern recognition are actively used in modern security platforms to provide more intelligence about potential security risks in enterprise operations.
Financial technology is being completely disrupted by the emergence of data science and machine learning. Equity investment, high frequency trading, financial planning etc are some of the most innovative use cases that are leveraging machine learning in the financial industry.
The lead platforms in the application performance and operational monitoring space are starting to leverage machine learning to obtain additional insights about system logs or application activities. Additionally, many of these platforms are starting to leverage machine learning to proactively predict failures about specific business processes and adapt accordingly.
The Next Decade of Innovation in the Enterprise Will be Powered by Machine Learning
Machine learning has all the characteristics of becoming one of the most transformational forces in the next generation of enterprise software solutions. Even though the space is still in very early stages, we are already witnessing the transformational impact that machine learning is having in several enterprise sectors. As the space continues evolving, we should starting to see many of the traditional enterprise software systems being completely architected to leverage machine learning techniques. There is certainly a decade-long opportunity ahead.