--- title: How Business and Data Science Collide description: How data science can be used to reform business forecasting models and help businesses better anticipate future needs. image: https://blog.avenuecode.com/hubfs/igor-ovsyannykov-329196.jpg --- [AvenueCode.com](https://avenuecode.com) [News](https://avenuecode.com/news) [Contact](https://avenuecode.com/contact) [![Avenue Code Snippets Logo](https://blog.avenuecode.com/hubfs/Avenue%20Code%20New%20Logos%20-%202023/AC-Snippets---Black.png)](https://blog.avenuecode.com/?hsLang=en-us) *menu* - Technology [Cloud](https://blog.avenuecode.com/blog/topic/cloud?hsLang=en-us) [Delivery Infrastructure](https://blog.avenuecode.com/blog/topic/delivery-infrastructure?hsLang=en-us) [Web Experience](https://blog.avenuecode.com/blog/topic/web-experience?hsLang=en-us) [Agile Mindset](https://blog.avenuecode.com/blog/topic/agile-mindset?hsLang=en-us) [Quality First](https://blog.avenuecode.com/blog/topic/quality-first?hsLang=en-us) [Design](https://blog.avenuecode.com/blog/topic/design?hsLang=en-us) [Solution Architecture](https://blog.avenuecode.com/blog/topic/solution-architecture?hsLang=en-us) [Data & ML](https://blog.avenuecode.com/blog/topic/data-and-machine-learning?hsLang=en-us) [Mobile Experience](https://blog.avenuecode.com/blog/topic/mobile-experience?hsLang=en-us) - [Whitepapers](https://blog.avenuecode.com/blog/topic/whitepapers?hsLang=en-us) - [Spotlight](https://blog.avenuecode.com/blog/topic/spotlight?hsLang=en-us) - [Extraordinary Women in Tech](https://blog.avenuecode.com/blog/topic/extraordinary-women-in-tech?hsLang=en-us) - [Avenue Code Culture](https://blog.avenuecode.com/blog/topic/avenue-code-culture?hsLang=en-us) - [Cloud](https://blog.avenuecode.com/blog/topic/cloud?hsLang=en-us) - [Design](https://blog.avenuecode.com/blog/topic/design?hsLang=en-us) - [Delivery Infrastructure](https://blog.avenuecode.com/blog/topic/delivery-infrastructure?hsLang=en-us) - [Solution Architecture](https://blog.avenuecode.com/blog/topic/solution-architecture?hsLang=en-us) - [Web Experience](https://blog.avenuecode.com/blog/topic/web-experience?hsLang=en-us) - [Data & ML](https://blog.avenuecode.com/blog/topic/data-and-machine-learning?hsLang=en-us) - [Agile Mindset](https://blog.avenuecode.com/blog/topic/agile-mindset?hsLang=en-us) - [Mobile Experience](https://blog.avenuecode.com/blog/topic/mobile-experience?hsLang=en-us) - [Quality First](https://blog.avenuecode.com/blog/topic/quality-first?hsLang=en-us) - [Whitepapers](https://blog.avenuecode.com/blog/topic/whitepapers?hsLang=en-us) - [Spotlight](https://blog.avenuecode.com/blog/topic/spotlight?hsLang=en-us) - [Extraordinary Women in Tech](https://blog.avenuecode.com/blog/topic/extraordinary-women-in-tech?hsLang=en-us) - [Avenue Code Culture](https://blog.avenuecode.com/blog/topic/avenue-code-culture?hsLang=en-us) # [How Business and Data Science Collide](https://blog.avenuecode.com/how-business-and-data-science-collide) - [Tweet](https://twitter.com/share) *perm\_identity* [Jimmy Mayal](https://blog.avenuecode.com/how-business-and-data-science-collide/author/jimmy-mayal?hsLang=en-us) *schedule* 9/12/17 4:00 PM **So, you’ve probably heard about Data Science and now you can’t stop thinking about how it can help you improve sales, right? Well, let’s delve into this tech topic from a business perspective.** **The Difficulty** Not long ago, companies used to scramble over various spreadsheets trying to make sense of immense volumes of sales data in an effort to plan for the future. They essentially made their decisions based on last year’s financial results, past marketing actions, or, in the worst case scenarios, their market competitor’s actions. The difficulty lay in having to create a plan or make a prediction based on one-dimensional data from a very small slice of the market. **Where Are You, Data?** Companies began to pursue methods to achieve better sales forecast accuracy, and began to invest in technology used to analyze data as well as reduce cost and human effort. This prompted companies to begin storing all the data generated, such as transactions, purchase orders, sales, etc. After enough data was stored, companies believed they could finally attempt to make predictions and plan sales. However, things were not as easy as they seemed to be! Even with the addition of technology, companies soon realized there was still a missing piece. So, what was it? Essentially, the data that they were collecting had two main problems: it was extremely polluted, and the volume was unfeasible for the technology of that time to process. Simply having data was not enough. **Science Over Data** The genesis of data science occurred in 1962 when mathematician John W. Tukey forecasted the consequences of modern-day electronic computing on data analysis. In the 80’s, IBM and Apple released the first personal computers, causing computing to evolve at a much faster pace and giving businesses the ability to collect data with less effort. In 2000, numerous academic journals began identifying data science as an evolving discipline, and in 2005, the National Science Board advocated for data science as a career path in order to ensure that there would be specialists who could effectively accomplish digital data analyzing. When companies began looking beyond their frontiers and started collecting external data such as social media, alternative payment systems, and client support, the volume of data exploded into an astronomic amount that was impossible for any computer to analyze without an initial cleaning process to organize the data. Therefore, almost 80% of the time spent on a forecasting project has historically been spent on data preprocessing - required in order to determine which infrastructure should be used for big data. As a matter of fact, the definition of big data is data sets that are so large or complex that traditional methods of data processing are inadequate. ![markus-spiske-221494.jpg](https://blog.avenuecode.com/hs-fs/hubfs/markus-spiske-221494.jpg?width=640&name=markus-spiske-221494.jpg) **Data Science: The Real Deal** Now that you know a bit about data science, let’s go back to the question of how data science can help you improve sales forecasting. Here are some of the many ways: - *Sales Planning* - -A personalized sales forecast, which means you can plan what you’re going to offer based on the purchasing behavior of your customers - *Customer Experience* - *Marketing Operations* **Case Studies** H&M ( $20.3 billion yearly sales): H&M has a clear goal for their product and utilizes good demand forecasting to stick to their bottom line Zara ($14.4 billion yearly): Zara’s achievement follows the theory that if a retailer can predict demand precisely, it can implement mass production which leads to well-managed inventories, higher profitability, and more profitability for shareholders. American Express: American Express started looking for ways to predict customer loyalty by analyzing internal and external data. The company is now able to identify 24% of the accounts that will close within 4 months. **A Promising Future** Although Data Science is not a brand new term, its application has become more and more popular due to technological advances that resulted in the explosion of data available on the web. We can consider data science as a “work in progress” that has been taken the business world by storm. When executed properly, data science has the ability to return infinite benefits to your company. --- [![LinkedIn Icon](https://blog.avenuecode.com/hubfs/Images/Blog/linkedin.png)](https://www.linkedin.com/in/jimmy-mayal-8615047/) ### Author # Jimmy Mayal Jimmy Mayal is a Product Manager at Avenue Code. He is a dedicated and creative professional. He also loves being with his family, studying computers and software, and writing fiction. --- ### Related Posts ### Avenue Code Opens Its Doors in Portugal [READ MORE](https://blog.avenuecode.com/avenue-code-opens-its-doors-in-portugal?hsLang=en-us) ### Avenue Code Celebrates 15 Years as a Leading Software Consultancy [READ MORE](https://blog.avenuecode.com/avenue-code-celebrates-15-years-as-a-leading-software-consultancy?hsLang=en-us) ### EWiT Inspire & Be Inspired with Adrienne McDowell & Ulyana Zilbermints [READ MORE](https://blog.avenuecode.com/ewit-inspire-be-inspired-with-adrienne-mcdowell-ulyana-zilbermints?hsLang=en-us) ### Technology & Innovation with Melissa Austria & Holly Camponez [READ MORE](https://blog.avenuecode.com/technology-innovation-with-melissa-austria-holly-camponez?hsLang=en-us) ### Leave a Comment! ### Avenue Code Social [![Facebook Icon](https://blog.avenuecode.com/hubfs/Images/Blog/facebook.png?t=1486470796564)](https://www.facebook.com/avenuecode) [![Twitter Icon](https://blog.avenuecode.com/hubfs/Images/Blog/twitter.png?t=1486470796842)](https://twitter.com/AvenueCode) [![LinkedIn Icon](https://blog.avenuecode.com/hubfs/Images/Blog/linkedin.png?t=1486470796556)](https://www.linkedin.com/company/avenue-code) ### Newsletter Want to stay on top of all tips and news from Avenue Code? ### Popular Snippets ![Primary Logo AvenueCode Inverted.png](https://blog.avenuecode.com/hs-fs/hubfs/Images/Logos/Primary%20Logo%20AvenueCode%20Inverted.png?width=1104&name=Primary%20Logo%20AvenueCode%20Inverted.png "Primary Logo AvenueCode Inverted.png") ### About Us - [Who We Are](https://www.avenuecode.com/who-we-are) - [What We Do](https://www.avenuecode.com/what-we-do) - [Portfolio](https://www.avenuecode.com/portfolio) - [Partners](https://www.avenuecode.com/partners) - [News](https://www.avenuecode.com/news) - [Events](https://www.avenuecode.com/events) - [Blog](https://blog.avenuecode.com/) - [Contact](https://www.avenuecode.com/contact) ### Our Offices San Francisco [+1 415 766 4178](tel:+553125161448) [ac.inquiries@avenuecode.com](mailto:brazil.info@avenuecode.com) Belo Horizonte [+55 31 2516 1448](tel:+553125161448) [brazil.info@avenuecode.com](mailto:brazil.info@avenuecode.com) São Paulo [+55 11 3205 3232](tel:+553125161448) [brazil.info@avenuecode.com](mailto:brazil.info@avenuecode.com) ### We're Hiring! - [Belo Horizonte](https://www.avenuecode.com/who-we-are) - [New York](https://www.avenuecode.com/what-we-do) - [San Francisco](https://www.avenuecode.com/portfolio) - [São Paulo](https://www.avenuecode.com/partners) --- ©2015 - 2017 Avenue Code [![Facebook Icon](https://blog.avenuecode.com/hubfs/Images/Icons/facebook-2.png)](https://www.facebook.com/avenuecode) [![Twitter Icon](https://blog.avenuecode.com/hubfs/Images/Icons/twitter-2.png)](https://twitter.com/AvenueCode) [![LinkedIn Icon](https://blog.avenuecode.com/hubfs/Images/Icons/linkedin-2.png)](https://www.linkedin.com/company/avenue-code) [![Glassdoor Icon](https://blog.avenuecode.com/hubfs/Images/Icons/glassdoor-icon-1.png)](https://www.glassdoor.com/Overview/Working-at-Avenue-Code-EI_IE456173.11,22.htm) [![YouTube Icon](https://blog.avenuecode.com/hubfs/Images/Icons/youtube-2.png)](https://www.youtube.com/user/AvenueCodePlay) Please enable JavaScript to view the [comments powered by Disqus.](http://disqus.com/?ref_noscript) © 2026 Avenue Code ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Jimmy Mayal", "url" : "https://blog.avenuecode.com/author/jimmy-mayal" }, "datePublished" : "2017-09-12T19:00:00.000Z", "headline" : "How Business and Data Science Collide", "image" : [ "https://blog.avenuecode.com/hubfs/igor-ovsyannykov-329196.jpg" ], "mainEntityOfPage" : { "@id" : "https://blog.avenuecode.com/how-business-and-data-science-collide", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://blog.avenuecode.com/hubfs/Avenue%20Code%20New%20Logos%20-%202023/Avenue%20Code-primary%20versions_LOGO%20HORIZONTAL%20group%201-7.png" }, "name" : "Avenue Code" } } ```