Data as a Service to Simplify the Complex
A Framework to Use Data for Business
How to turn Data into Profits?
Big Data Analytics
Do you have Data but aren’t sure how to make business with it?
Data-as-a-Service (DaaS) providers are tasked with wrangling big data sets and taking them through heavy analytical rigor:
- There’s science involved in how the data is collected and aggregated.
- There’s strategy to how you run deep analyses while looking at statistical significance and identifying correlations.
- But there’s also a great business sense to pulling out insights from advanced analytics and visualizing them in a way that’s digestible by everyone company-wide, regardless of education or experience.
LIFEdata’ s powerful data science and AI solutions help you get more out of your data investment and get an edge on the competition—no data science degree required. We apply intelligent analytics to your business vision to find quick wins and low-hanging fruit. These insights enable the organization to find growth opportunities, plan execution paths to adopt new initiatives and innovate in a way that will have the greatest impact while being timely and cost-effective. This approach is done quickly, like you typically see done at the startup level, but without the risk that typically comes from running an agile organization.
Big Data or Small Data?
Small data is data that is ‘small’ enough for human comprehension. It is data in a volume and format that makes it accessible, informative and actionable. The term “big data” is about machines and “small data” is about people.
In contrast to big data, small data is a data set of very specific attributes that can be created by analyzing larger sets of data. It is often informative enough to find solutions to problems and achieve actionable results. In other words, small data brings people timely, meaningful insights that are organized in an accessible and understandable way, without requiring the use of expensive technological systems necessary to tackle big data.
Most of the references contrast Small Data to Big Data by asserting that Small Data is about a personal connection to a limited amount of information, whereas Big Data is about the need for smart machines to sort out the every-expanding volume of available signals.
Big Data is primarily about correlations whereas Small Data is about causal relationships.
The Small Data approach is intended to foster insights and to transform mindsets. Small Data is intended to help us gain insights that we can put into practice.
The notion behind thick data is that you can’t always depend on numerics and algorithms to summarize the 360-degree experience of a customer, or of any other human activity or relationship where unforeseeable factors can enter in.
Micro-Moments and Predictive Assistance
Ever since the iPhone was released in 2007, the world as we know it looks very different.
What started a decade ago as a device where you downloaded apps has now grown into a device that you turn to throughout the day to solve any number of problems. It’s become so ingrained in our society that Google has coined a term for them—Micro-moments.
Micro-moments are the times when Google says we, “reflexively turn to a device—increasingly a smartphone—to act on a need to learn something, do something, discover something, watch something, or buy something. They are intent-rich moments when decision are made and preferences shaped.”
Micro-moments are tremendous opportunities for brands today. They fuel what’s coming next in our data-centric world—predictive analytics.
Predictive analytics delivers the personalized experience so many consumers are looking for today. Using micro-moment insights, you can better predict what a person needs to see from your business to keep progressing in their path-to-purchase.