What is the reason that a few organizations are so effective with data science and AI while others face difficulties to deliver the expected ROI? For what reason do a few organizations appear to easily adopt data science while others develop solutions that either never make it to the market or are seldom utilized once deployed? Why is it that instead of data science, only analytics and reporting is delivered?

We're sure you must have faced these questions too! As indicated by PwC 's global study, AI will be responsible to give a 26% lift in GDP for local economies by 2030. However, for some organizations, deploying data science into different business functions can be troublesome if not daunting.

As indicated by Gartner expert Nick Heudecker, over 85% of data science projects fail. Isn't that huge? A report from Dimensional Research showed that only 4% of organizations have effectively deployed ML models. Now, that's too less!

Significantly more critical, the financial decline brought about by the COVID-19 pandemic has put increased pressure on data science and BI teams to provide more with less. In this down market, companies are rethinking which AI/ML models they should build, how to optimize resources and how to best utilize significant budget dollars for the desired impact. In this kind of environment, AI/ML project disappointment is essentially not acceptable.

Having a data scientist(s) isn't the primary step in building data science capabilities. It's a complex buildout that begins with a complete procedure. That is the difference between organizations seeing achievement and those encountering poor or conflicting outcomes.

Like any unpredictable capability to build out, certain factors that determine the success of a data science project.

Deciding Goals in Data Science

Before you even begin planning, defining goals and expectations greatly affects the result both from a technical and business perspective.

A challenging aspect of this cycle is uniting business objectives and mathematical objectives. From one perspective, business decisions are still impacted by vision and business intuition, while on the other, data scientists settle on choices dependent on rigorous performance metrics that don't in every case straightforwardly convert into business value.

A typical error that companies make is to come up with a quantitative goal, for example, 95% precision for a classification model, with no earlier idea of whether this is a sensible measure: is precision the correct performance metric? Is 95% too simple or too hard with regards to this particular area? Will accomplishing that degree of precision convert into a competitive advantage for the business?

The only fix for this is to invest energy understanding the space of the issue before defining goals and setting expectations. The constructive outcomes of having clearly defined goals stream down to all resulting phases of the cycle, setting guidelines for both project managers and the technical team

Lack of Skills

For almost two years, there has been a widespread talent shortage in the data science domain. LinkedIn revealed in 2018 that there was a lack of more than 150,000 people with data science aptitudes. While the complex interdisciplinary methodology of data science projects includes subject matter experts such as mathematicians, data engineers, and numerous others, data scientists are often the most critical and also the most challenging to select. This implies that organizations are struggling to implement and scale their projects, which thus, is easing back an ideal opportunity to production. Also, numerous organizations can't manage the cost of the huge teams needed to run various activities all the while.

C-Suite Level Sponsorship

The most significant acknowledgment for an organization utilizing or intending to utilize data science is that this will change how you work together. This implies that most teams and operations will be affected. Data science isn't something an individual or a single team can make successful. Overall the business needs to buy in and bolster initiatives.

A clear vision, defined by individuals from the C-Suite helps effectively express that idea. When you see a data science initiative with C-Suite visibility, it's probably going to fail. Visibility isn't sufficient to drive the sort of progress expected to facilitate data science capabilities.

Data science will surely fail in a siloed organization. CxOs and board members are the only individuals with the power to make enterprise-wide changes. Despite the fact that execution happens at lower levels, buy-in begins at the top.

How to Avoid Such Pitfalls

Data Engineering

You can't do data science without data– explicitly, "great" data that feeds the data model or algorithm you are utilizing to pick up insights or make forecasts. The efforts required to put in to get data in a structure that is helpful for analysis is frequently underestimated by enterprises.

Your product team needs to make sense of what data you really need, which requires building up a data strategy that guides your product and business strategy. Second, make sure that your team scopes the data engineering effort appropriately. It's always disparaged when kickstarting a data science initiative. Lastly, it is important to understand that a data engineer has diverse ranges of abilities than a data scientist. You need the former to appropriately comprehend the engineering required, not the latter.

Automation

To help alleviate the actors that cause data science projects to fall flat, the business has seen an increased interest among companies adopting end-to-end automation of the full data science process.

With automating data science, organizations are not just ready to flop quicker (which is something to be thankful for on account of data science), however, to improve their transparency efforts, ensure minimum value pipelines (MVPs), and ceaselessly improve through emphasis.

However, you must be wondering why failing fast is positive? While maybe strange, failing fast can give a huge advantage. Data science automation permits technical and business groups to test speculations and complete the whole data science workflow in days. Originally, this cycle is very extensive that takes months, and is very costly. Automation permits failing hypotheses to be tried and killed quicker. Fast failure of poor projects gives savings as well as increased productivity. This rapid try-fail-repeat process likewise permits organizations to find helpful theories in an all the more ideal way.