Your Big Data is speaking to you. So, why don't you actually listen?

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These days, companies generate and collect enormous amounts of data which has become known as Big Data. Big Data can be analyzed for insights which lead to smarter and more strategic business decisions. Businesses realize the huge potential of their data repositories, particularly with machine learning (ML), however, that does not always translate to actionable information due to various issues. Some of those issues are related to a lack of efficient integration between data scientists, the ML experts and the business experts in the company.

Big Data Analytics Challenges Business Face Today

Translate the data into actionable information.
Being a data-driven organization:

Being a data-driven organization means integrating data into their strategic core vision. Data should be highly regarded in the company, because it reflects the beliefs, attitudes and behaviours of the stakeholders and more. 

"The true recipe for data success is the perception of data elsewhere in the organization, not in the data team itself" (Newman, 2015).

The Relationship Between the CEO and Big Data

Instead of separating data from the CEO, and thinking that a department in the company should know all the answers regarding data, the CEO should be the one leading the way, especially with big data being so important in today's business decisions.

The partnership between big data and the CEO is essential in creating a competitive big data strategy. 

CEOs need to get involved with artificial intelligence (AI) 

CEOs must get involved with AI in order to stay relevant in a world where business decisions are increasingly data driven and automated. Just as CEOs need to deeply understand human intelligence to manage enterprise teams, it only makes sense that in today's world where machines and algorithms are increasingly becoming part of these very teams, understanding human intelligence alone is no longer enough and therefore they need to understand more of the data that their company produces.

CEOs see data scientists as a key skill for the future

Data scientists have been identified as the most important workforce capability that CEOs are looking for to support future business growth, according to a KPMG survey of 150 UK leaders and a further 1,150 CEOs from across the world, who were asked about their future investment plans and the challenges and opportunities facing their companies. (Bobby Hellard, 2018). 

The important relationship between Data Scientists and the Business Experts in the company.

"Where Is This Going?” : Reasons To Have The Relationship Talk Now

Basically, data scientists' role is about supporting and automating business decision-making. Hence, data scientists need to get involved in the core of business processes. When the company's goal is being an efficient data driven organization, the root of the problem starts when there is a lack of integration between the data scientists and the business experts in the company. There is no room for argument about the importance of data scientists in the company, and as mentioned earlier, CEOs realize it. However, CEOs must also understand that hiring data scientists and investing enormous amounts of money in their salaries won't guarantee the company will immediately reap the benefits. A data scientist alone can only be effective within a fertile corporate environment. Lack of good communication and fertile corporate environment happens due to many matters., which will now point out.

Commitment for being data-driven

You might not be surprised that without the support and a strong commitment to actually making those data-driven decisions, everyone will just waste their money and time. Though, today’s state of AI/ML across industries includes the following two main issues: 

  1. Business executives still need to be convinced that reasonable ROI of ML investments exists.
  2. Even if they are convinced and understanding of the value proposition and market demand, they may lack technology skills and resources to make data-driven decisions into actions. 

Introducing a machine learning initiative should be supported and understood on all organizational levels. Using ML as a support to decision-making, and especially for making important decisions, is likely to face resistance. Therefore, the role of the CEO and other C-level executives is to educate employees and foster innovation. 


Explain Technical Concepts To Non-Technical Audiences

Another issue to point out is related to the technical-language barrier between the Data Scientists and the stakeholders. This barrier implicates the many consequences and problems that data scientists face when wanting to assist in predicting future business decisions. Defining the problem to predict is one of the most important parts of Data Scientists pipeline and often ends up occupying a lot of the data scientist’s time. One of the obstacles of getting to the stage where the problem is agreed on, is that DS need to know how to make the less technical people, the stakeholders, understand what can actually be done with data science, more than just a fuzzy idea. These people often have real problems that they care about, so DS should explore the core issues and questions with them. The ability to understand the strategy behind the need, will obtain better results. This is why communication and presentation skills are necessary qualities for a data scientist.

Becoming a Data-Driven Enterprise Takes Dedication

Good communication and correct integration between CEO, business experts and the data scientists will lead the company into a data-driven enterprise, and only a real commitment and collaboration will leverage the business revenues. The modern era of business decisions will put those on top of competition who can make use of data they collect. 

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Author Note: The following articles served as both source and inspiration for this post

Bradford. (2018, September 6). 8 Real Challenges Data Scientists Face. Retrieved from: https://www.forbes.com/sites/laurencebradford/2018/09/06/8-real-challenges-data-scientists-face/#648393796d99

Seroussi. (2015, November 23). THE HARDEST PARTS OF DATA SCIENCE. Retrieved from: https://yanirseroussi.com/2015/11/23/the-hardest-parts-of-data-science/

U. Recio. (2018, September 14). Machine Learning for CEOs. Retrieved from: https://www.datasciencecentral.com/profiles/blogs/machine-learning-for-ceos

Swaney. (2018, February 13). Big Data and the CEO: A New, Important Relationship. Retrieved from: 

 https://hortonworks.com/article/big-data-and-the-ceo-a-new-important-relationship/

Hellard.(2018, 8 August). UK CEOs see data scientists as a key skill for the future. Retrieved from: https://www.itpro.co.uk/business-strategy/31649/uk-ceos-see-data-scientists-as-a-key-skill-for-the-future

Neubauer. (2019, January 28). Why Is It so Hard to Put Data Science in Production? Retrieved from: 

https://www.datascience.com/blog/why-is-it-so-hard-to-put-data-science-in-production
Wheeler. (2017, October 31). Becoming a Data-Driven Enterprise Takes Dedication.
Retrieved from: https://hortonworks.com/article/becoming-a-data-driven-enterprise-takes-dedication/?utm_campaign=be-first&utm_medium=article&utm_source=hortonworks

https://www.altexsoft.com/blog/datascience/how-to-structure-data-science-team-key-models-and-roles/

https://www.altexsoft.com/whitepapers/machine-learning-bridging-between-business-and-data-science/


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