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6 Best Practices of Successful Enterprise Data Science Projects

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During the last decade, data science projects in the enterprise have developed a reputation for being complex and expensive. However, the last few years have seen an explosion in new machine learning and big data infrastructure technologies that have helped lower the entry point for implementing data science solutions in the enterprise. Despite the technical evolution, enterprise data science projects remain relatively complex compared to traditional areas of investment in enterprise IT.

Similar to other groundbreaking technologies in enterprise IT, implementing successful data science solutions is a combination of strong processes, delivery methodologies and technologies. Our experience implementing dozens of successful enterprise data science and machine learning solutions have allowed us to develop certain perspective about patterns we think help to optimize the success of data science projects in the enterprise. The following list provides a small summary of best practices in enterprise data science projects. Some of them might seem trivial but they can be difficult to enforce in real world implementations.

Build For the Future: Build on Technologies You Can Innovate Upon

Data science platforms is one of the fastest growing areas in the technology ecosystem. As a result, new platforms, machine learning algorithms, data visualization technologies, etc are constantly surging bringing new value propositions to enterprise solutions. Additionally, the requirements for enterprise data science solutions are constantly changing based on new market trends.

Building on a technology stack that facilitates innovation, extensibility and scalability is essential to guarantee the success of enterprise data science projects. In that sense, when selecting a data science platform, organizations should not only evaluate its technical capabilities but also complementary factors such as developer community, open source contributions, talent availability etc.

No Model is Right: Implement Various Models for the Same Scenario

One of the most common mistakes in machine learning projects is deciding on a specific prediction or classification algorithm before implementing the solution. Many times, the optimal algorithm is not discovered until several models are tested and evaluated with the real data. In that sense, is a good practice to implement the first iteration of the solution running several machine learning algorithms concurrently and compare the results over time.

Continuous Data Science: Deliver Results Every Week and the First MVP in a Month

Enterprise data science projects are notorious for taking a long time and being extremely expensive. Also, is not uncommon that stakeholders need to wait months before seeing the first results of a data science solution which, more often than not, need to be improved. To mitigate some of those challenges, we always recommend structuring projects in a way that deliver weekly results to stakeholders.

In addition to deliver weekly results, we always recommend to focus on delivering a minimum viable product (MVP) within the first month of starting the project.  Sometimes, this model requires cutting a few corners on the infrastructure side on the early days but it guarantees the constant feedback from the ultimate users which will help to continuously improve the data science solution.

Test Test Test: Make the Models Testable

Complementing the previous point, it is very important to provide mechanisms to continuously test and validate machine learning algorithms even if the solution is running in production. Building testing models is an often overlooked aspect of enterprise data science projects but one that becomes critical to guarantee the evolution of the solution.

Monitor Everything: Implement Operational Monitoring in Your Data Science Solutions

Monitoring the execution of machine learning models, data inputs and outputs, model failures etc becomes essential for the production readiness of an enterprise data science project. In that sense, IT organizations should considering implementing the correct operational monitoring and instrumentation infrastructure as part of any data science project. While conceptually obvious, incorporating these capabilities in a data science solution is far from trivial as most operational monitoring platforms are still not integrated with machine learning and data science stacks.

Start Small, Fail Fast and Iterate

Machine learning and data science solutions are new initiatives for most enterprises and one that requires new skillsets and practices. In that sense, it is important to approach these projects in a highly iterative manner and allocating room for initial failures. While the limitations of legacy data science technology stacks prevented organizations from applying agile and lean development practices to data science projects, this is no longer the case. Today most of the modern data science and machine learning stacks provide enough capabilities that allow organizations to start delivering results extremely fast with a minimum investment.

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Posted by on August 5, 2015 in Uncategorized

 

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The Half Ass Consumerization of the Enterprise

This is the first of a series of posts that will attempt to provide a pragmatic view of the consumerization of the enterprise phenomena and demystify some of marketing cloud surrounding this term. As an industry, we are identifying the consumerization of the enterprise as a movement within traditional organizations to enable employees with tools and applications similar the ones they use in their personal life. This movement has been triggered by different technology factors such as the raise in the use of connected devices within enterprises or the emergence of new application delivery mechanisms such as cloud computing, application stores, etc.

While, undoubtedly, there is a tendency within organizations to embrace a new type of enterprise software technologies that look closer to traditional consumer applications, I believe the consumerization of the enterprise is far from becoming a relevant phenomenon in the enterprise software industry. From my standpoint, the consumerization of the enterprise is more of a psychological phenomenon accelerated by a marketing frenzy than a technological movement.  As a matter of fact, I firmly believe that, unless software providers and enterprises make some serious changes, the consumerization of the enterprise might never expand beyond being a sexy marketing term.

Let me try to explain;

When we think about the consumerization of the enterprise, we can easily related this movement to a number of emerging disciplines in the software industry that are starting to have an impact in the enterprise:

  • Enterprise mobility
  • Enterprise social networking and collaboration
  • Cloud computing
  • Gamification

While there are others, we can pretty much associate any “consumerized enterprise application” with some the 4 technology areas listed above. When we look deeper into the enterprise technologies   in each one of these areas, we quickly realize that the true enterprise-ready software technologies are far from being consumer friendly and the products that truly leverage consumer technology concepts are far from being enterprise ready. I know, it totally sucks :(.

To put this in the context of an example, let’s look at the enterprise mobility space which, arguably, is leading the charge in the consumerization of the enterprise movement. Currently, SAP and IBM (yes those two) are the dominant players in the space followed by companies such as Antenna Software and Kony. When you deep dive into the technologies offered by those vendors or talk to some of their customers, you realize that the experience is no better or simpler than traditional enterprise packages in the space such as the old Blackberry server stack. The sad thing is that the consumer market is full of mobile technologies that deliver a far superior experience than the enterprise products but, unfortunately, none of those technologies is well suited to be applied to enterprise applications.

In order to become a relevant movement in the software industry, the consumerization of the enterprise needs a deeper commitment and collaboration by the product vendors as well as the enterprise. Here are some of the things I believe are desperately needed:

  • Enterprise tailored software products that leverage consumer technology concepts
  • Deeper understanding of enterprise mechanics by the new generation of enterprise software vendors
  • Flexible pricing models based on economies of scale
  • Simpler software acquisition policies
  • More flexible compliance, procurement and other enterprise regulatory processes.

Each one of the aforementioned areas represent is a major challenge for the new generation of enterprise software technologies. I will cover each one of the areas listed above in more detail in future posts but, for now, I am happy if this post is making you reflect about the realities of the consumerization of the enterprise movement.

In my opinion, without addressing some of the challenges in the areas listed above, the consumerization of the enterprise might be destined to stay as an engaging marketing line with a minimum impact in the enterprise software industry.

 

 
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Posted by on June 8, 2012 in Uncategorized

 

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