The exponential rise in data, easy access to information, proliferation of smart devices and rise of smart technologies together present a huge opportunity and challenge for enterprises. The importance of data science as a service is now evident as the demand for data science consulting continues to grow over the past few years. By taking advantage of business analytics solutions and data science consulting, enterprises can increase their share-of-wallet and positively impact their bottom lines by reducing revenue leakages and identifying fraud. Data science consulting can also impact the top line by increasing revenue, improving sales conversions, and enhancing customer experience.
- Smaller businesses who cannot afford a Data Scientist no longer need one. While Data Scientists are important to provide stakeholders with the answers to important business-related questions, DSaaS has made them not necessary for small, budget-tight businesses.
- Particularly for larger businesses, Data Scientists can become empowered by DSaaS. Just like providing a hero with a sword, DSaaS allows Data Scientists to save the day and show their business executives where revenue can get generated.
- Data Science as a Service provides the missing link between real Big Data from real Big Users and business goals. These programs are trained to both process current business problems and identify new ones, as well as provide the correlations needed to evaluate your business and make decisions.
ADVANTAGES
What we offer
Today, data comes in various flavors, from different sources and in different formats, which can be structured or unstructured. Keeping this in mind, Our data science services offers businesses the capability to analyze and gain insights from big data through its own innovative methods of obtaining, importing, and processing, storing for later use. Our data science consulting experts use proprietary tools for data preparation, management and harmonization. Our data science as a service ensure that the entire lifecycle of data preparation is taken care of:
- Data exploration
- Data cleaning
- Data changing
- Data shaping
- Data publishing for analysis
We employ statistical methodologies, machine learning and a combination of both to build analytical models both on big data as well as conventional data. Our data science as a service offering helps improve data quality, which in return answers business queries much faster. The statistical modelling service comprises:
- Data analysis
- Data interpretation
- Data explanation
- Data presentation
While data preparation and statistical modelling are considered to be the foundation of analytics, insight generation is what businesses look for when using data science services. The insight generation process consists of collecting, organizing, retaining data, generating various analytics models from the data, evaluating the analytics models for actionable business strategies. Insight generation extensively uses:
- Data mining
- Model building
- Business insights
For data science services, using the insights gained for making informed decisions and taking corrective actions are crucial. Our insight deployment process helps translate the analytical results in the everyday decision making process and automating it, thus writing back the results of the analytical models to implement systems and deploying in a big data environment.
- Write back of insights to existing processes and systems
- Feedback loop to collect results of analytics implementation
- Improvement of analytics processes based on feedback loop
- Automation of predictive processes and systems using insights generated from analytics
Example
Imagine that an eCommerce clothing site recently unveiled a new line of designer jeans. A week after the launch, the company noticed a decline in web conversions and saw an increase in shopping cart abandonment throughout the site. What could have caused this? Market research clearly indicated that customers loved the new line of jeans. Data Science as a Service can explain what happened. A DSaaS solution may tell them that their page speed on their site is slow and this is directly impacting conversions. By monitoring real users and providing easily accessible graphs representing data, the dSaaS might indicate in real time that whenever a user waits 3 seconds to load a page on this eCommerce site, their likelihood of conversion decreases by 20 percent. Being able to correlate data among many sources (provide meaning) is vital, and this ability is the highest interest driver for those considering to conduct Big Data analysis.