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Machine Learning Dynamics: The Advent of the Modern Enterprise Software

Enterprise software is on its way to witness a major makeover, which is destined to make the transition to the software as a service (SaaS) model a smoother and simpler process. This makeover is most likely to be induced by machine learning. Machine learning will allow computers to undertake real-time data mining and processing tasks and to create predictive models that will help companies in processing meaningful insights and identifying patterns. Simply put, machine learning has the potential to trigger endless possibilities.

With machine learning solutions, you can source data from both within and outside of your enterprise without having to employ human resources to fill out web forms. The machine learning software is effective enough to source data from unstructured systems, such as calendars and emails, and also from voice mail systems and call centers. The data collated and organized by the software will allow managers to get insights and predictions and to make critical business decisions.

Issues that we have been facing with traditional Enterprise Software

With machine learning, you can get rid of many issues that are associated with traditional enterprise software. First off, the quality of data captured and stored by the traditional software is similar to the quality of data acquired through human resources. Given the fact that most sales executives do not really bother to update the CRM information on time, the process tends to become prolonged and unnecessarily complicated with numerous sales calls and spreadsheets that have to be updated with pipeline-related information.

Secondly, traditional enterprise software, built on relational databases, fail to present better longitudinal perspectives of information, which is why they are less capable of generating insights. This is the reason why many companies choose to depend on large data warehouses that obtain large amounts of data from enterprise applications. With such an approach, managers have to wait for weeks before they can use the data in an appropriate manner to create insights. It is important to note that static, human-defined rules dictate the functioning of traditional enterprise systems and such rules become obsolete as businesses continue to undergo transformations with time.

The machine learning revolution is most likely to be brought by next-gen specialists

It is quite surprising to note that the existing software leaders are not expected to fuel this revolution. Enterprise software veterans Dave Duffield, Marc Benioff, and Aneel Bhusri were associated with the SaaS wave, but this new wave is created by creative thinkers who do not belong to the traditional software development genre. Instead, this new wave is more likely to be created by professionals from Google, Facebook, and Twitter. This is mainly because of the fact that the development of machine learning software requires expertise and skill sets that are entirely different from the ones required to develop traditional enterprise software. As a matter of fact, consumer machine techniques have been used for years by consumer Internet players for data analysis purpose.

New Leaders in the Machine Learning Landscape

The business landscape is on its way to be transformed by the new wave of enterprise applications that are fuelled by machine learning. The revolution will have a tremendous impact on key business areas including:

  • Human Resources: New machine learning-powered enterprise solutions have the potential to transform applicant tracking systems into back-end solutions and to support the actual task of recruitment. Gild, Entelo, and Concept Node has deployed machine learning models to screen and recruit appropriate candidates and to promote effectiveness in the functioning of the internal teams.
  • Sales: The new enterprise solutions allow sales managers and account managers to receive alerts about risk situations so that they can take timely actions. These alerts are insights that allow managers to plan ahead of time. Clari, Inside sales, Gainsight, and Lattice have been using machine learning and data science-based solutions to identify sales opportunities and risks and to generate sales forecasts.
  • Finance: The machine learning powered solutions provide insights, allowing managers to identify opportunities to drive profit and growth and to promote efficiencies. Trufa, Adaptive Planning, and Anaplan support their financial planning function through solutions that promote predictive analysis.
  • Marketing: Persado and Captora are two companies that have deployed data science to customize their content to suit the needs of their prospects.

So what’s next?

The introduction of the new enterprise models brings mammoth opportunities to the entrepreneurs and the investors to capitalize on the innovations to exploit growth and return-on-investments. According to BCC Research, the machine learning market is predicted to grow to $15.3 billion by 2019, with the predictive analysis software being listed in the early-growth category.

To sum up, the machine-learning powered enterprise models are on their way to replace the legacy systems, thereby instilling a modern makeover to how businesses operate in the present times.

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