What is data analytics?
Data analytics is the process of assessing raw data sets to gather valuable insights to help organizations solve complex business problems. Analyzing big data can create a clear picture of customer habits and behaviors to derive trends and patterns which can streamline operations, improve processes, and create efficient marketing strategies.
Data analytics has been used for business intelligence for a long time. Technology has changed the way organizations work with information imperative to the way they function. For centuries, governing powers have used census data to plan cities and maneuver populations. In 1890,to help the US census crunch data fast, Herman Hollerith (the founding father of IBM) invented the tabulating machine which processed data records onto punch cards. A century later, Relational Databases (RDBM)s emerged which provided users the ability to write Structured Query Language (SQL) and analyze data on demand. Next came the Internet and the development of data warehouses, which stores presently used data and historical data.
Organizations uses data analytics in four different ways:
- Descriptive analytics scrutinizes historical data, key metrics and benchmarks to create a summary that describes the changes and shifts during business performance, explaining the “what”.
- Diagnostic analytics digs deeper into data to explain “why” something happened using data mining.
- Predictive analytics forecasts future outcomes so organizations can make better decisions.
- Prescriptive analytics tells organizations how to take action and solve business problems or capitalize on a trend.
Organizations can use data analytics to:
- Discover business opportunities.Data analysis provides insights on advertising, budget, operations and other business elements that discovers holes and boosts value.
- Make better marketing decisions. Data-driven decision making improves outcomes. A McKinsey & Company study found that using data to make better marketing decisions can increase marketing productivity by 15-20%.
- Understand audiences and customers better.Organization can use analytics to create campaigns that target customers directly.
- Centralize data. Funneling data into a central location and allowing organization wide access, streamlines cross-functional teams.
- Budgeting.Big data analytics allows organizations to spend money where it is most needed.