Putting Big Data Analytics in the Life Sciences context

From the time we had computers, we have always had analytics.  Whether we called it MIS (Management Information System) or Business Intelligence, we always wanted the ability to look back into the data that we had collected and get some information out of it.

We went from Business Warehouse to Business Analytics to Business Intelligence.

We were able to tell:

We used canned reports from the system, downloaded data into spread-sheets and even built our own reporting.

We even did some regression analysis to predict sales (or a different variable), depending on a few other pieces of information.

And we have extremely fast (HANA) databases, immense computing power and business understanding.

However, we were still falling short on answering some key questions such as:

Answers to questions such as these can be expected of Big Data analytics.

Big as in massively big data.  Hundreds of millions of records.  Some clean, mostly not-so-clean.  Some structured, others unstructured.  Some text, some videos, some images, etc.  Some from within the company, some from externally available sources, still some others that we purchased (such as prescription data).

Big Data is not data that you can collect in a few tabs of an excel spread-sheet.

Big data must have an analytical engine (a set of complex algorithms) that can collectively examine this data for patterns.  Such analytical engines would have artificial intelligence built-in, i.e. the more data they crunch, the better they get at crunching the same type of data.

Most importantly, the resulting insights should be predictive and substantial enough for significant business gains.

Here is a definition of big data from Wikipedia.

“Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.