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The three challenges you must figure out to effectively use data science in your business

It seems that at this point, everybody knows – the future is here. Data Science (DS) technologies are no longer science fiction. Advanced technologies where machines learn, communicate, predict, recommend, and sometimes even decide do not exist only in Hollywood movies.


Top-notch companies make wide use of data science and deep technologies. They leverage the results of these efforts to achieve a significant advantage in all aspects of product and business. In fact, a significant part of current-days market leaders has become such due to their ability to foresee the future and understand they need to stay ahead of the game. They did that by adopting deep technologies back in the days when they were pre-mature and struggling to provide actual results. It is their ability to believe in the power of technology and their resilience through the exhausting iterations of retrying to make it work that brought companies like Tesla, Amazon, Google, and many more to become market leaders. Eventually, they are the true winners in this technology race that we are all a part of in the last decade or two.

Now, after the early adaptors have proven their superiority, we are moving into the second phase of the technology-market evolution: it is becoming clearer by the day that those who will not adopt new and deep technologies are doomed to lose their business literally.

CEOs, CTOs, and other executives are looking to actively pursue after adding data science power to their organization but also often find it challenging for three main reasons:


  1. Data Science is deep and complex Data Science as a domain holds many subdomains such as Natural Language Processing, statistics, mathematics, Computer Vision, Predictive Modeling, and many more. To master each of these subdomains and provide high-quality results, one needs years of learning, experience, and practical expertise. Since each subdomain is deep and complex by itself and since many solutions may come from a combination of several strategies that originate from different subdomains (e.g., Natural Language Processing, unsupervised learning, and predictive modeling), it is no surprise that no single data scientist can hold all the knowledge of all sub-domains. In fact, most data scientists do not have the experience and the expertise to produce high-quality deliverables in more than 2-3 sub-domains. The depth and diversity of the field impede the path of success of many technology groups.

  2. Relying so heavily on theoretical, explorative paradigms, data scientists often ‘behave’ like different species in R&D divisions; hence many CTO, CIOs struggle to effectively manage them.

  3. Heterogenous groups are challenging to build, manage and orchestrate, often resulting in compromising in homogeneous or mediocre teams, which limits the potential of the solutions they could provide a suboptimal, narrow, often superficial approach.

  4. The teams are often busy improving the efficiency of algorithms and models. At the same time most of the benefit is already gained instead of developing their knowledge into more product/business practical areas of expertise. And so, even if one managed to build a solid DS team, the expansion of the team capabilities and; as a result, the organization’s product/business capabilities might still be a challenge.

  5. Human Resources Top talent is hard to find across the board. Nowadays, with the financial climate, there is an evident market shortage in technology expertise in many roles. However, finding a good data scientist is on the verge of mission impossible. There are many reasons for this difficulty, but the main one is implied in the previous section: only a very few individuals hold the required academic background (usually MSc., or even PhD. in scientific fields from top universities). Fewer of them hold applied approach (vs. theoretic), professional experience, and ability to deliver effectively in commercial environments. The long training period of those top DS makes the drought a long-lasting problem. In the rare cases that such an individual is identified, in many cases, a fair evaluation of her capabilities and fit to the organization is tough. Usually, one needs deep domain knowledge or external help. The problem is threefold harder when the company needs to establish a new team since there is a challenge of recruiting and effectively managing those individuals in an effective manner Many organizations are mistaken to take the first talent they come across without understanding the first talent at the door is probably the one that will dictate the level of professionalism in the org and without understanding how profound the level of expertise in their ability is to perform.

  6. Data Science is a multidimensional challenge The most common mistake we see in the market is that organizations tend to think implementing data science is solved by bringing talent onboard and building data science team/s. Keeping up to date on the professional level is hard: A good data scientist should constantly learn. In a rapidly changing and evolving environment, the knowledge he/she must maintain keeps a good data scientist always on their heels. Doing it while learning and knowing the product and business in depth is much harder The utilization of data science in any organization is a shared challenge and not the sole responsibility of those tech professionals: Product and business professionals often don’t understand deep technology's different capabilities or the data requirements, processes, methodologies, and many other aspects needed to utilize data science effectively. Realistic expectations are often not set. As a result, the central knowledge gap lies in the following questions.

  7. “How can deep technologies impact my product/business most effectively?”

  8. How much time (and resources) will it take until we see some concrete outcomes?

  9. How will other organizational divisions be affected by those solutions (e.g., will it change the operation? Marketing?)

The answer to this question is almost always multidimensional. Advanced technology implementation and use can be a thing of beauty but requires profound changes in how you run your product, marketing, data, analytics, business model, and strategy If not thinking strategically, the DS team will work on irrelevant tasks, will focus on, e.g., improving the efficiency of algorithms and models while most of the benefit is already gained.


To summaries:

Nowadays, Data Science is a challenge any competitive business needs to figure out and implement. Since this is a complex domain with a shortage of experts in the market and such a comprehensive impact on many different parts of the organization, this is something you definitely do not want to take the risk of failing.

We have established Fusion Power AI once we realized that 85% of Data Science projects were failing. We have built the team, work processes, and methodologies to maximize your chances of success, and with a success ratio of 92%, we are confident we can help organizations build and expand Data Science and AI capabilities.




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