Every business, regardless of its size, collects data. Whether it is financial data, HR data, traffic data, or sales data, modern businesses using digital tools cannot avoid gathering mountains of data.
The problem with business data is that few businesses use it to its fullest potential. Buried in each company’s data vaults are clues to making better decisions, identifying opportunities, and optimizing the outcomes of whatever business they do. To uncover and unravel those clues, businesses must engage in the field of data science.
Applying data science is no longer optional
“Data science is no longer a ‘nice-to-have’ or an expensive experiment for businesses,” says Jan Maly, Data Science Lead at STRV. “It’s vital for gaining a competitive edge today. AI is now attainable, affordable and, most importantly, a necessity for almost all businesses.”
STRV is a software design and engineering team with nearly 20 years of experience in developing digital products that help companies unlock business opportunities. STRV believes that there are four data science myths that can keep companies from embracing the power of data science.
Myth One: Data science is expensive
Obviously, doing the work of data science will cost companies something. At the least, companies will need to make room in the budget to obtain or develop software that can tame data and extract understanding. However, when the impact of applied data science is understood, those expenses can be better seen as investments that lead to increased efficiency, effectiveness, and sales. The understanding gained from data science allows companies to automate processes, increase speed, and mitigate human errors, all of which save companies money.
For most retail businesses, product descriptions provide a wealth of data. Utilizing that data to categorize products can make it easier for customers to find what they want or for businesses to make suggestions about related items.
An AI solution provided by STRV allowed a company to use its available product data to categorize 30,000 types of shoes with 96 percent accuracy and a 20 millisecond per item processing time. The project was completed 500 times faster than it could have been if managed manually. Combining AI and data science decreases the cost while increasing the return on investment.
Myth Two: Data science takes a long time
Because most science deals with natural processes that cannot be rushed or manipulated, it is not wrong to think that good science takes time. Businesses, especially businesses trying to solve problems, typically do not have a lot of time. Addressing problems with data science can seem like a luxury that your business cannot afford.
Data science is different. Data moves at the speed of light and the technology and methods for mining and understanding data, once developed, can be widely applied. STRV approaches data science projects by first developing a Proof of Concept (POC) to validate that the problem can be solved with the data that is available. By committing to get to a POC conclusion quickly, STRV allows for the entire timeline for data science solutions to be greatly reduced.
STRV has undertaken major projects for companies including Songclip, Cinnamon, and AllVoices. Even with projects that involve cutting edge technology and demand a high degree of efficiency and accuracy, the POC phase of the process has rarely taken more than one month.</…….
Source: https://www.influencive.com/debunking-the-four-most-common-data-science-myths/