--- title: 5 Reasons Over Half of Data Science Projects Never Leave the Lab description: Data science projects have historically been stuck in a research lab type of environment, unable to be deployed across a business at large. The impact? Data science projects are failing to deliver value to businesses, resulting in a massive amount of wasted time, money and human resources. image: https://download.riverlogic.com/hubfs/RL%20Blog%20Data%20Scientist.jpg --- ### Get in Touch [![logo](https://download.riverlogic.com/hs-fs/hubfs/logo.png?width=3153&height=398&name=logo.png "logo")](http://riverlogic.com) [![logo-hover](https://download.riverlogic.com/hs-fs/hubfs/logo-hover.png?width=3153&height=398&name=logo-hover.png "logo-hover")](http://riverlogic.com) - [Solutions](http://www.riverlogic.com/solutions) - [Capacity Planning](https://download.riverlogic.com/solutions/capacity-planning) - [Commodity Trading & Logistics](https://download.riverlogic.com/solutions/commodity-trading-and-logistics-optimization) - [Product Mix & Customer Profitability](https://download.riverlogic.com/solutions/product-mix-customer-profitability) - [Production Planning](https://download.riverlogic.com/solutions/production-planning-and-scheduling) - [Sales & Operations Planning](https://download.riverlogic.com/solutions/sales-and-operations-planning) - [Sourcing & Procurement](https://download.riverlogic.com/solutions/sourcing-and-procurement-optimization) - [Supply Chain & Logistics Optimization](https://download.riverlogic.com/solutions/supply-chain-optimization) - [Supply Chain Network Optimization](https://download.riverlogic.com/solutions/supply-chain-network-optimization) - [Trade Promotion Optimization](https://download.riverlogic.com/solutions/trade-promotion-optimization) - [Industries](https://download.riverlogic.com/industries) - [Building Products](https://download.riverlogic.com/industries/building-products) - [Chemicals](https://download.riverlogic.com/industries/chemicals) - [Consumer Packaged Goods](https://download.riverlogic.com/industries/consumer-packaged-goods) - [Food and Beverage](https://download.riverlogic.com/industries/food-beverage) - [Manufacturing](https://download.riverlogic.com/industries/manufacturing) - [Mining & Metals](https://download.riverlogic.com/industries/mining-and-metals) - [Oil & Gas](https://download.riverlogic.com/industries/oil-and-gas) - [Pharmaceuticals](https://download.riverlogic.com/industries/pharmaceuticals) - [Pulp & Paper](https://download.riverlogic.com/industries/pulp-and-paper) - [Utilities](https://download.riverlogic.com/industries/utilities) - [Technology](https://download.riverlogic.com/technology/prescriptive-analytics-platform) - [Our Platform](https://download.riverlogic.com/technology/prescriptive-analytics-platform) - [Prescriptive Analytics](https://download.riverlogic.com/technology/prescriptive-analytics) - [Resources](https://download.riverlogic.com/resources) - [Blog](https://download.riverlogic.com/blog) - [News](https://download.riverlogic.com/news-2) - [Testimonials](https://download.riverlogic.com/testimonials) - [Partners](https://download.riverlogic.com/partner-program) - [Our Partners](https://download.riverlogic.com/our-partners) - [About Us](https://download.riverlogic.com/about-us) - [Contact Us](https://download.riverlogic.com/contact-us) - [Our Board](https://download.riverlogic.com/board-of-directors) - [Our Leadership](https://download.riverlogic.com/leadership) - [Careers](https://download.riverlogic.com/careers-current) - Get in Touch Get in Touch [![Get in Touch](https://hubspot-no-cache-na2-prod.s3.amazonaws.com/cta/default/484375/8a2efec4-88ad-40a3-bc49-91f4a5e57e38.png)](https://hubspot-cta-redirect-na2-prod.s3.amazonaws.com/cta/redirect/484375/8a2efec4-88ad-40a3-bc49-91f4a5e57e38) Share This [mailto:?subject=5%20Reasons%20Over%20Half%20of%20Data%20Science%20Projects%20Never%20Leave%20the%20Lab&body=https%3A%2F%2Fdownload.riverlogic.com%2Fblog%2F5-reasons-over-half-of-data-science-projects-never-leave-the-lab](mailto:?subject=5%20Reasons%20Over%20Half%20of%20Data%20Science%20Projects%20Never%20Leave%20the%20Lab&body=https%3A%2F%2Fdownload.riverlogic.com%2Fblog%2F5-reasons-over-half-of-data-science-projects-never-leave-the-lab) # 5 Reasons Over Half of Data Science Projects Never Leave the Lab Remember dissecting a frog in middle school? The point of the project was to learn about the internal workings of the amphibian’s organs and get a better picture of ecology as a whole. However, the smell of formaldehyde often made it difficult to think about the purpose. Instead, the tendency was to finish the task at hand — one that usually involved a knife-happy partner paired with a squeamish one who documented results with eyes half-shut. Jump to the corporate workplace and the sentiment of the science lab and the frog can be found today: *forget the corporate ecosystem!* But with no formaldehyde smell or squishy parts to handle, why is it that ***50% or more of data science projects never leave the lab? What’s the deal?*** ## Data Science Project Obstacles To date, big data and data science have primarily benefited companies in two ways: cost-cutting with improved efficiencies and the creation of a data-driven culture.1 However, data science can do so much more. As an interdisciplinary field that squeezes a myriad of data sets into meaningful insights, data science applied across an enterprise can perform at its highest purpose: **disruptive innovation that leads to unmatched value**. While some 64% of company executives see success in creating innovation and disruption utilizing data science, common obstacles remain, making projects hostage to the lab. Five of these obstacles include: - Planning - Leadership - Data Quality - Data Scientists - Analytics ### Planning Planning is an obstacle for companies that lack defined business objectives. This leads to a failure to identify questions that companies need to have answered. Personnel and tools are being put into action without planning, and this leads to the discovery of random insights. The problem is that individually or collectively, these ad hoc findings don’t map to a plan or a return on investment. For the company, it’s like the school classroom that conducts a lesson without first having the curriculum in place. Gartner has noted that large companies executing data science projects are not allocating enough time, staff, and funding.1 ### Leadership By 2019, it’s projected that 90% of large companies will have a Chief Data Officer (CDO). In the next few years, the ethos of this role will shape outcomes with a mission-critical function no different than IT, HR, finance, and business operations.2 The exact scope of the CDO, however, varies greatly and is often not clear. Is the role meant to be the driver of strategies or coordinator of resources? The ambiguity may be one reason CDO and similar roles in the data science arena are walking out the door.3 These reluctant heroes are often missing clarity in the C-suite, and it’s why planning remains a primary obstacle to overcome. Then, there are the CDOs who become protectors of the data, building a barrier for the rest of the organization. By isolating outcomes in the lab, opportunities to evangelize at the enterprise level evaporate.  Leadership without definition impacts other obstacles, too. ### Data Quality When it comes to big data, weak governance and maintenance are operational challenges that increase the risk of compliance and can even result in revenues losses. Additionally, these data issues question the quality of outcomes — no matter the stage of the project. The inability to validate the data often keeps projects from moving forward. ### Data Scientists Similar to the challenges of the CDO, the role of the data scientist has lacked definition in many corporate settings. The work of the data scientists remains a bit of a mystery to most corporate stakeholders. That can be an obstacle when it comes to collaboration with the business as a whole. As identified in an earlier post, data scientists are in high demand and short supply. Because of their expertise, data scientists may build models that rely upon their perspective and are not necessarily aligned with the business. This can make changes to scale beyond the lab more lengthy and reliant on this role. ### Advanced Analytics The technology selection process within corporations relies on many considerations: budget, in-house and management consultant expertise, existing platforms, and time and resources. Implementing a new technology is an obstacle in itself. Add an advanced analytic solution that depends on the high-level skills of data scientists, and it’s easy to understand how getting out of the lab becomes an obstacle. It’s comparable to a frog experiment where one student gets a scalpel, and everyone else in the class receives a toothpick: results are lowered. In summary, obstacles are ever present in the corporate environment. Could it be that overcoming one obstacle holds the key to breaking past the others? In Part 2, each is examined closer to see if this is the case. [![Optimization or Simulation - Inline](https://hubspot-no-cache-na2-prod.s3.amazonaws.com/cta/default/484375/43605e26-328e-4d61-8471-766bbb911844.png)](https://hubspot-cta-redirect-na2-prod.s3.amazonaws.com/cta/redirect/484375/43605e26-328e-4d61-8471-766bbb911844) 1”[NewVantage Partners Big Data Executive Survey 2017](http://newvantage.com/wp-content/uploads/2017/01/Big-Data-Executive-Survey-2017-Executive-Summary.pdf),” White Paper, January 2017 2”[Six Pitfalls to Avoid When Executing Data Science and Machine-Learning Projects](https://www.gartner.com/doc/3834463/pitfalls-avoid-planning-data-science),” Gartner, February 5, 2018 3”[3 Top Take-Aways From the Gartner Chief Data Officer Survey](https://www.gartner.com/smarterwithgartner/3-top-take-aways-from-the-gartner-chief-data-officer-survey/),” Gartner, January 29, 2018 4”[Why Are Data Science Leaders Running for the Exit?](https://www.datascience.com/blog/why-data-science-leaders-fail),” Oracle+DataScience.com, January 18, 2018   You May Also Enjoy Reading [How to Start Using Prescriptive Analytics: Your 12-Step Guide](https://download.riverlogic.com/blog/how-to-start-using-prescriptive-analytics-your-12-step-guide)…[(Read More)](https://download.riverlogic.com/blog/how-to-start-using-prescriptive-analytics-your-12-step-guide) [6 Supply Chain Analytics Examples to Keep You Competitive](https://download.riverlogic.com/blog/6-supply-chain-analytics-examples-to-keep-you-competitive)…[(Read More)](https://download.riverlogic.com/blog/6-supply-chain-analytics-examples-to-keep-you-competitive) #### About River Logic We’re on a mission to ensure companies across the globe drive maximum value with every decision they make, no matter how complex. [![Learn More](https://hubspot-no-cache-na2-prod.s3.amazonaws.com/cta/default/484375/a2bde0d6-69fe-4923-8616-00e630d07900.png)](https://hubspot-cta-redirect-na2-prod.s3.amazonaws.com/cta/redirect/484375/a2bde0d6-69fe-4923-8616-00e630d07900) #### **Search** #### Explore Our Solutions We offer a suite of supply chain planning, network optimization, order allocation, and general planning solutions that are purpose-built for business users rather than data scientists. [![Explore Now](https://hubspot-no-cache-na2-prod.s3.amazonaws.com/cta/default/484375/244b60cb-8886-4e40-a50b-a0ab86c40833.png)](https://hubspot-cta-redirect-na2-prod.s3.amazonaws.com/cta/redirect/484375/244b60cb-8886-4e40-a50b-a0ab86c40833) #### Ready to Chat with Someone? We’re ready to help! Just click the button below, and grab a time slot that works for your schedule. [![Schedule a call now](https://hubspot-no-cache-na2-prod.s3.amazonaws.com/cta/default/484375/798117a2-c8b3-49c5-aca3-cfe9c69c3b12.png)](https://hubspot-cta-redirect-na2-prod.s3.amazonaws.com/cta/redirect/484375/798117a2-c8b3-49c5-aca3-cfe9c69c3b12) #### Solutions - #### [Solutions](https://www.riverlogic.com/solutions) - [Capacity Planning](https://www.riverlogic.com/solutions/capacity-planning) - [Commodity Trading & Logistics](https://www.riverlogic.com/solutions/commodity-trading-and-logistics-optimization) - [Product Mix & Customer Profitability](https://www.riverlogic.com/solutions/product-mix-customer-profitability) - [Production Planning](https://www.riverlogic.com/solutions/production-planning-and-scheduling) - [Sales & Operations Planning](https://www.riverlogic.com/solutions/sales-and-operations-planning) - [Supply Chain & Logistics Optimization](https://www.riverlogic.com/solutions/supply-chain-logistics-optimization) - [Supply Chain Network Optimization](https://www.riverlogic.com/solutions/supply-chain-network-optimization) - [Trade Promotion Optimization](https://www.riverlogic.com/solutions/trade-promotion-optimization) - #### [Technology](https://www.riverlogic.com/technology/prescriptive-analytics-platform) - [Our Platform](https://www.riverlogic.com/technology/prescriptive-analytics-platform) - [Prescriptive Analytics](https://www.riverlogic.com/technology/prescriptive-analytics) - #### [Industries](https://www.riverlogic.com/industries) - [Building Products](https://www.riverlogic.com/industries/building-products) - [Chemicals](https://www.riverlogic.com/industries/chemicals) - [Consumer Packaged Goods](https://www.riverlogic.com/industries/consumer-packaged-goods) - [Manufacturing](https://www.riverlogic.com/industries/manufacturing) - [Mining & Metals](https://www.riverlogic.com/industries/mining-and-metals) - [Oil & Gas](https://www.riverlogic.com/industries/oil-and-gas) - [Pulp & Paper](https://www.riverlogic.com/industries/pulp-and-paper) - [Utilities](https://www.riverlogic.com/industries/utilities) - #### [About Us](https://www.riverlogic.com/about-us) - [Our Board](https://www.riverlogic.com/board-of-directors) - [Our Leadership](https://www.riverlogic.com/leadership) - [Contact Us](https://www.riverlogic.com/contact-us) - #### [Partners](https://www.riverlogic.com/partner-program) - [Our Partners](https://www.riverlogic.com/our-partners) - #### [Resources](https://www.riverlogic.com/resources) - [Blog](https://blog.riverlogic.com/) - [Content Library](https://www.riverlogic.com/resources) - [News](https://www.riverlogic.com/news) - [Testimonials](https://www.riverlogic.com/testimonials) - [Careers](https://download.riverlogic.com/careers-current) [Facebook](https://www.facebook.com/River-Logic-480102222138357/) [LinkedIn](https://www.linkedin.com/company/river-logic/) [Youtube](https://www.youtube.com/channel/UCMJGbIQn9AX2kGojeRHhu-w/) - [Disclaimer](https://www.riverlogic.com/disclaimer) - [Support](mailto:support@riverlogic.com) - [Privacy Policy](https://www.riverlogic.com/privacy-policy) - [Sitemap](https://www.riverlogic.com/site-map) - [Terms & Conditions](https://www.riverlogic.com/terms-and-conditions) © 2022 [River Logic, Inc.](https://www.riverlogic.com) All rights reserved. ![](https://googleads.g.doubleclick.net/pagead/viewthroughconversion/985826217/?value=0&guid=ON&script=0) ![](https://dc.ads.linkedin.com/collect/?pid=173242&fmt=gif) [![Supply Chain Brief](https://widget.aggregage.com/images/supplychainbrief/badge-clear.gif "Supply Chain Brief")](https://www.supplychainbrief.com) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Cheryl Hammer", "url" : "https://download.riverlogic.com/blog/author/cheryl-hammer" }, "dateModified" : "2020-08-17T19:03:40.035Z", "datePublished" : "2018-09-12T22:01:00.000Z", "headline" : "5 Reasons Over Half of Data Science Projects Never Leave the Lab", "image" : [ "https://download.riverlogic.com/hubfs/RL%20Blog%20Data%20Scientist.jpg" ], "mainEntityOfPage" : { "@id" : "https://download.riverlogic.com/blog/5-reasons-over-half-of-data-science-projects-never-leave-the-lab", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://download.riverlogic.com/hubfs/4.28_Logos/logo_mobile-1.png" }, "name" : "River Logic" } } ```