Data Science across Industries

Data Science

Till now Data Science has been playing a back office and supportive role in the business. This is already transitioning into pushing the Data Science professionals to the front seats as algorithms have started taking active part in business decision making. With industries ready to access data and feedback loops, algorithm teams are now getting engaged in every aspect of the business. Welcome to the 4th Industrial Revolution! From marketing to managing inventory to operations and customer behaviour, data science not only provides insights to human behaviour but also integrates algorithmic products and decision making into business processes.

Why Data Science cannot be ignored?
  • 90% of data in existence has been created in last 2 years: We produce 2.5 quintillion bytes of data every day. With IoT the pace is already picking up with no looking back. We are now more dependent on search engines than ever. Google alone processes 40,000 searches every second.
  • Data Science Is Smart Entrepreneurship: More startups are now involved in data crunching to develop robust data driven models to formulate smart solutions. In sectors like Insurance companies are already attempting to analyse drivers behaviour to formulate customised insurance premiums.
  • It Powers Up Business Value: Math, Statistics, Computer Science coupled with Human domain knowledge and intuitiveness power up the business value.
Challenges
  • Pace of Innovation Faster than Learning: Data science is innovating at a very fast pace and each new technology has its own learning curve. Codes are written by computer engineers in many different programming languages so the learning curve is very steep.
  • Engineers & Data Scientist: Who does what? Organisations are still trying to identify the right composite for the team to do this work. Data scientists have strong backgrounds w.r.t machine learning but are poor on business and its products. Engineers are good with products but weak on machine learning.
  • Hiring New Talent with Required Skills: Most companies and industries are grappling with shortage of right Data Science talent.

Data Science: Linking & Solving the Riddle of Enterprise Data & Big Data

Current data management landscapes often fail to create a link between the Enterprise Data and Big Data. This makes it difficult to operationalise Data Science and derive valuable insights. Users struggle to massive haystacks of data to find hidden needles of worthy insights.

Enterprise Data:

  • High quality, structured data.
  • Contracts, customer information and financial transactions.
  • Clear governance, security concepts and life cycle management practices.

Big Data:

  • Characterised by high volume of semi-structured or unstructured data.
  • Captured in Data Lakes- Hadoop.
  • Lack comparable enterprise governance, security and lifecycle management.

Advantages of an upskilled Workforce

  • The current resources are largely working in Siloes and handling limited aspects of data. An upskilled resource is effectively able to implement best practices of Data Science and provide worthy insights.
  • Data Analysts, Big Data Resources with cross departmental experience within the organisation have an excellent chance of upskilling into an effective Data Scientist.

How Can SkillUp Technologies help you leverage Data Science?

(Leave us a line, if you have not done so already. We shall get in touch with you)