Business intelligence (BI) has been evolving for decades as data has become cheaper, easier to access, and easier to share. BI analysts take historical data, perform queries, and summarize findings in static reports that often include charts. The outputs of business intelligence are “known knowns” that are manifested in stand-alone reports examined by a single business analyst or shared among a few managers. For example, who are the probable high-net-worth clients to sell them a premium bank account. There can be some consideration like the average account balance etc.
Predictive analytics has been unfolding on a parallel track to business intelligence. With predictive analytics, numerous tools allow analysts to gain insight into “known unknowns”. These tools track trends and make predictions, but are often limited to specialized programs. In the previous example, the probable high-net-worth client could also be the spouse of an existing high-net-worth client that can be figured out using predictive analytics. Here is another set of questions that predictive analytics can answer:
* How can the web experience be transformed to entice a customer to buy frequently?
* How do you predict how a stock or a portfolio will perform based on international news and internal financial factors?
* Which combination of drugs will provide the best outcome for this cancer patient based on the specific characteristics of the tumor and genetic sequencing?
Data Science on the other hand is an interdisciplinary field that combines machine learning, statistics, advanced analysis, high-performance computing and visualizations. It is a new form of art that draws out hidden insights and puts data to work in the cognitive era. The tools of data science originated in the scientific community, where researchers used them to test and verify hypotheses that include “unknown unknowns”. Here are some of the examples:
- Uncover totally unanticipated relationships and changes in markets or other patterns. For example the price of a house based on nearness to high voltage power lines or based on brick exterior.
- Handle streams of data—in fact, some embedded intelligent services make decisions and carry out those decisions automatically in microseconds. For example analyzing the users click pattern to dynamically propose a product or promotion to attract the customer.
As discussed, Data Science different from from traditional business intelligence and predictive analytics in the following way.
- It brings in data that is orders of magnitude larger than what previous generations of data warehouses could store, and it even works on streaming data sources.
- The analytical tools used in data science are also increasingly powerful, using artificial intelligence techniques to identify hidden patterns in data and pull new insights out of it.
- The visualization tools used in data science leverage modern web technologies to deliver interactive browser-based applications. Not only are these applications visually stunning, they also provide rich context and relevance to their consumers.
Data science enriches the value of data, going beyond what the data says to what it means for your organization—in other words, it turns raw data into intelligence that empowers everyone in your organization to discover new innovations, increase sales, and become more cost-efficient. Data science is not just about the algorithm, but about deriving value.