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The Case for Data Science Modernization: 6 Shortcomings of Legacy Solutions

In a recent white paper, The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution, Maven Wave detailed how recent developments in data science modernization (DSM) are setting the foundation to support the transformative power of artificial intelligence (AI) and machine learning (ML). One stumbling block identified in the white paper is the many ways in which existing data solutions such as SAS are incompatible with the needs of the modern enterprise.

6 Shortcomings of Legacy Data Science Solutions

Specifically, we identify six areas in which traditional solutions come up short. Examining these six deficiencies can illuminate the ways in which DSM is essential for the future success of a business. The six shortcomings of legacy data science solutions are:

1. Talent: New breakthroughs generally require new talent, and this cohort often isn’t conversant in traditional, proprietary languages like those associated with SAS and others. New talent wants — and needs — new technologies and languages like R and Python.

2. Portability: Traditional solutions too tightly bind data, software, and processes together, making it difficult to work with the flexibility that is required for maximized outcomes. Innovative and intuitive outcomes are more readily achieved with modern solutions.

3. Scalability: The traditional approach to scaling is more hardware and more software licenses. Cloud-based solutions allow for scale-up/scale-down approaches that encourage the experimentation that leads to insights.

4. Performance: Traditional solutions are usually noted for their reliability, but that comes with a pretty steep price in terms of flexibility and cost. New alternatives have made great strides in performance and offer solutions for both known and unknown contingencies.

5. Maintainability: Time is running out on legacy approaches and systems. Not only is talent hard to find, as discussed above, but the very model of installed, proprietary software is in decline. Meanwhile, open-source, cloud-based alternatives have nothing but upside: the future is theirs.

6. Cost: Users have noted the high cost and inflexible pricing of existing solutions have also been shattered by cloud-based, dynamic alternatives. Already, the advent of new solutions is changing the pricing dynamic and further examination of hidden costs and further lowering of costs for new approaches bode well for customers.

Taken together, the shortcomings of existing data science platforms and solutions are clear. Their limitations are insurmountable — particularly when it comes to taking full advantage of the rapidly emerging power of AI and ML. Enterprises need new and comprehensive approaches in order to achieve the full benefits of DSM.

In our white paper, “The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution,” we explore what it will take to succeed in a future that is dominated by AI and ML. This includes real-world road maps to guide the journey and challenges that may occur along the way. Download the full white paper here.

September 29th, 2021

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