Harvard had dubbed data science as the Sexiest Job of the 21st century way back in 2012. Cut to almost a decade later, its aura still prevails.
Data science has a proven track record and has fast expanded into various industries. These twin advantages make it a much- sought-after success enabler for businesses today.
Organizations deal with a large volume of structured and unstructured data. It emanates from various sources in different formats. Data science helps extract valuable insights from mere numbers behind the data.
It uses a blend of science, mathematics, and statistic (and some routine common sense!). This way, the field is successful in maximizing value from digital transformation.
What is the Typical Data Science Workflow?
The data science life workflow comprises 5-6 continuous and overlapping processes.
Here is a peek into the same –
1 – Gathering Information
All available sources are engaged to extract data. The data will be either unstructured or structured data. They use diverse methodologies to gather data from various sources. These include web scraping and manual data entry.
2 – Transforming Data for Consistency
The collected data may be in different formats. A data scientist will help bring consistency. This way, analyzing it later becomes simpler.
She will use many techniques for generating data in a better format. These include reformatting the data or cleansing the same, and more. This is where the concept of ETL (Extract, Transform, Load) comes in handy.
This step results in a comprehensive data lake or a data warehouse. Due to this effort, the job of analyzing data becomes less cumbersome.
3 – Scrutiny Of Data
The data scientist will scrutinize several aspects like ranges, patterns, outliers, anomalies, and so on. This way, she makes it ready for allied purposes. These next steps include machine learning, deep learning, and predictive analysis.
4 – Data Analyzed For The Eureka Moment!
The data scientist analyzes the data, as per the requirement of a specific case,
They apply diverse techniques such as predictive analytics, deep learning algorithms, and more. With this step, extracting appropriate insights becomes straightforward.
5 – Sharing insights with business users
The data scientist is ready with key findings behind the data. Now, she needs to convert these findings into a language that business leaders and decision-makers can understand.
Obviously, these business users are from a non-technical background. They want a simple answer to every insight – “What does it mean for the business?”
The data scientist will try to answer this question with her expertise. She will employ solutions like charts, reports, heat maps, and other data visualization methods. These methods make outliers, trends, and patterns visible.
At this stage, she presents the data and insights in a language that the stakeholders can comprehend. As a result, they can make strategic decisions and set the path to the company’s growth.
What is the Role Played By Data Science: Today And Future
Irrespective of your business and industry, your organization can gain incredible ROI with data science. Skilled data scientists enable data analysis to unlock gold-standard insights. Such insights can uncover future predictions, trends, personalization, or risk assessments. Doing so helps the company build a roadmap to success.
No longer the company has to grope in the dark about their new offerings. They can rely on a data scientist to collect the freely available unstructured data and make sense of it. Such actions also uncover the preferences, behaviors, and needs of their target customers.
As a result, the company now knows what the customers want. And they can tweak their business model to address their specific needs and wants. This way, the company can grow exponentially. This USP is possible because it aligns the offerings with the customers’ needs.
Businesses operate in various industry verticals. They look for solutions to tide over the difficulties they face. This is where data science comes to their rescue. For instance, it offers them tremendous help in understanding customer personas.
In a way, it guides the businesses to come out with newer tactics. By doing so, they can enhance customer experiences.
What are the Use Cases for Data Science?
Now that we are clear about the potency of data science let’s go one step ahead. We will now check out some practical use cases. Doing so will help us better understand its impact on real-world business situations
Data science can wrangle and analyze super-large chunks of data. No wonder that its practical applications are relevant to many industry verticals.
Let’s check out some use cases to understand the massive impact of data science.
1 – Media and Entertainment
Media and entertainment businesses make use of data related to content usage patterns. For instance, on-demand content is largely generated depending on the medium used to view it, like mobile devices.
Take the case of the hugely popular recommendation system from Netflix. The system uses a machine-learning algorithm to search for content similar to what the viewer watches regularly. It then displays those content that matches the movies or shows that you watched or liked recently.
Netflix delivers this USP via two broad recommendation systems
a. Content-based recommendation
If you have watched horror movies in the past, and have liked a few movies on Netflix, then the OTT channel will recommend you to catch up on unwatched horror movies in the future.
b. Collaborative filtering recommendation
Rather than content-level recommendations, Netflix also offers filtering based on your specific profile. So, imagine that Peter watches horror and sci-fi, and Paula watches horror and thriller. Now, Netflix will recommend thriller to Peter, too (since the horror genre is common to both Peter and Paula).
2 – Banking and Finance
The banking sector finds AI-based data science to be the best medium to enhance their decision-making process. For instance, banks create virtual assistants to guide customers to make smooth transactions.
Take the case of HDFC Bank’s virtual assistant Eva, for example. You can ask Eva about their various products or services. It has emerged as India’s first and largest AI-powered banking chatbot. It holds the distinction of achieving a staggering 85% accuracy in answering more than 5 million queries to date.
With such chatbots, the banks save a lot of costs on staffing customer support executives. It also features 24×7 availability. This helps the bank to amplify customer satisfaction.
Another example of data science in banking is fraud detection. Here, the data scientist brings in her expertise in different data-mining techniques. These cover association, clustering, forecasting, and classification.
Investment banks also carry out risk modeling. Doing so ensures that they regulate financial activities.
Their forte on data science lets them carry out data-driven decision-making for various responsibilities. These include mergers and acquisitions, company capital evaluation, or conducting corporate restructuring.
3 – Travel and Transportation
Did you know that data science can also enable huge success stories in the field of travel and transportation? Yes, it has proven its effectiveness in this industry too.
Let’s look at Uber. It has embraced data since to extract insights from big data collected on its app. It also collects data from third-party sources like social media platforms. They use this data to improve the rider experience.
It uses data science to calculate surge pricing. Such pricing depends on the route followed and the distance covered. It also includes variables like the end destination and the general traffic conditions along the route. With such measures, it aligns the right driver with the specific rider request.
With data science, Uber can calculate pricing according to the demand for its cabs. So, imagine if the demand for a cab in an area at a specific time is low. The pricing, too, would be low. Data science comes in handy to accurately calculate the fares based on these parameters.
4 – Music Streaming
Spotify is a classic example of how data science can revolutionize the music streaming industry. Their hit mood-based recommendation systems speak volumes about the app’s success through data science initiatives.
They have moved beyond custom-curated playlists and personal recommendations systems.
When they adopted data science, they could add phenomenal personalization initiatives like:
a. By context – liveness, acoustics.
b. By mood – danceability, energy, tempo, and valence
c. By custom properties – loudness, speckiness, and use of instrumentation
d. By segments – timbers, pitches, bars, and beats
Their internal machine learning models identify these traits in a song. This helps them bucket a specific song into specific categories, as shown above. Now, if the user is on the road and wants to listen to foot-stomping dance beats, Spotify makes it easy as it presents a list of songs that are of the same mood and tempo.
To sign off
It is evident that data science is well on the way to providing a competitive advantage to companies embracing it. We at Teg Analytics can help you get the necessary assistance needed in this endeavor. Do contact us, and we will be happy to help so that your company is at the forefront in adopting data science.
