Data is cheap and ubiquitous. We are collecting new types of data at unprecedented rates from mobile devices, sensors, instruments, and transactions. IBM estimates that 90 percent of the data in the world today has been created in the past two years.
New technologies have emerged to organize and make sense of this data overload. We can now identify patterns and regularities in data of all sorts that allow us to
improve personal health,
detect medical fraud,
recommend a customized insurance plan based on lifestyle,
do image recognition of all types,
create a genome sequence at a fractional cost,
predict the optimized container and routing schedule,
predict that wind gusts will be around 70 mph yesterday here in The Central North Carolina region,
find the right customer to work with,
improve your donor recruitment,
show employee skills /projects to cross-pollinate teams,
send a targeted message at the right time, at the right place to the right consumer
moreover, many more use cases.
The point here is not data but the ability to draw insight and nudge consumers, users, customers, employees, customers, suppliers to take action.
The rise of "big data" has the potential to deepen our understanding of phenomena ranging from physical and biological systems to human social and economic behavior. ~ U.C. Berkeley
Few Challenges Ahead of Us
Every CIO or CMO or CFO that we talk to now has access to Terabytes if not Yottabyte of data. Challenges we hear are as follows:
Legacy issue of multiple systems giving different insights to different data sets
The data accumulation rate is much faster than the ability to extract insights from it
Ability to provide Aggregate Actionable Insight
Ability to connect internal and external data sources – both social and open source data
Ability to make sense of both structured and unstructured data
"This hot new field promises to revolutionize industries from business to government, health care to academia." — The New York Times
If we drill down further, we ascertain that all questions revolve around raw data itself as follows:
Ability to agree upon standard data formats
Ability to derive social and economic value from data – Insights is the key
Lack data talent pool
Inability to leverage the new machine learning tools
Inability to build data and statistical models to test the hypothesis on large data sets
Ability to manage heterogeneous data sets - text, audio, images, videos, machine data from sensors, social data
How to convert Insights into Action?
Data Science teams can convert raw data into insights that could be actionable for different stakeholders by following this simplified methodology:
Understanding past data set
Creating different models, test and put these models to action
Ability to use models to different systems
Ability to push the insight to Data visualization tools for senior executives or BI tools to take action
All the above should happen yesterday in modern business parlance for managers to take Insights and convert them into Action.
Business Intelligence is the rear mirror view mirror looking at the past data while Predictive Analytics is windshield, which will empower you to steer your business in the right direction with Actionable Insights.
In current times, Data is like the crude oil. Converting the crude oil to refined products makes it more valuable (and moments like this when we disagree, and the value of Crude is down). In internet parlance, it is the ability to convert data into insights into action that makes it more valuable.
Photo Credit: tj.blackwell