Source: techwireasia.com
DATA has emerged as one of the most valuable assets for companies across many industries, and investing in data science definitely promises lucrative returns.
Businesses have figured out many use cases for data science and data analytics, and address many modern business challenges.
Deployed properly, data could be used for demand forecasting, targeting and generating leads, enhance product quality, and allows for more accurate decision making.
But, despite the apparent benefit of data, business leaders are struggling to derive business value for their expensive data science initiatives and projects.
According to one Gartner estimate, 60 percent of all big data project fails, and experts are arguing that the actual number is closer to 85 percent.
Further, a staggering 96 percent of enterprises struggle with AI and machine learning technology, according to another report.
Why big data science project fails
These high failure rates could be attributed to many factors, but the considerable disconnect between business users and the data science process bears the lion’s share of the blame.
Given the complexity and interdisciplinary nature of data science and data analytics, the objectives and expectations must be aligned from the start.
Without clearly defined business goals or context, data scientists would not only be drowning in a large volume of data, but also producing complex analysis and models that have no business value.
Beyond that, data teams also often struggle to deliver the results required at speed required out of them, as the analytical process itself in interdisciplinary.
In addition to mathematical and statistical skill, data scientists also need to have a certain degree of experience, and expertise on the industry and subject matter, to be able to pick out patterns and trends from raw data, before processing them into particular formats that are compatible with the algorithms deployed.
And to make matters worse, there has always been a global shortage of data scientists, and the heavy dependencies on select few experts may have lead to data project failures.
Automation will unlock true potential
Meanwhile, data science automation that leverages AI and machine learning could immediately address some of the pressing issues that have plagued the data science process.
These new generation automation tools could quickly analyze vast amounts of data, detect thousands of intricate patterns or features, as well as ‘train’ more machine learning algorithms.
With some aspect of the process reliably automated, data scientists would also be able to discover and test newer patterns and possibilities, in addition to speeding up the entire process.
Beyond that, data science automation could also empower a whole host of citizen data scientists, allowing them to experiment and business models while pushing the organization towards being more data-driven.
While it may be still in nascent stages, automation in data science and analytics hold great potential in unlocking real business values of big data.
By speeding the entire process, and addressing the talent gap, businesses may finally be able to see a substantial return on their investments.