How Data as a Service Providers Simplify the Complex
A Framework to Use Data for Business
How to turn Data into Profits?
Do you have data but aren’t sure how to make business with it?
If so, you’re not alone.
One of the biggest challenges we hear time and again from executives is the lack of concrete plans for how to turn hard data into actual revenue.
Data science requires that you tap into the analytical and scientific side of your brain to understand complex data sets. 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 an art 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.
The primary focus of a DaaS provider is to get enough information to validate assumptions and gut reactions to the market. This validation is used in conjunction with vision to enhance the approach that’s so often seen in the startup world. A DaaS provider will then take that data collection a step further and draw out insights that can be visualized and mapped out into action steps for the organization. The goal of all of this? To transform big data sets into profit-building, intelligent initiatives. Monetization is a big deal and a huge hurdle, so it’s worthy of a closer look.
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.
Making big data smaller is the wave of the future. If you aren’t already doing it, you are already behind. Computers and software, at the moment, are only as good as their coders and you need human eyes to see the big picture.
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. Bonde makes this point explicitly, that Small Data is intended to help us gain insights that we can put into practice.
Some suggest that we should start with Big Data and then reduce the output, creating logs and other artefacts. I am not enthusiastic about that strategy. Instead, I think the power of Small Data comes when we use our mental models to notice or find the critical pieces of information. The five examples in this essay all illustrate the skillful discovery of critical data, rather than condensing the output of a Big Data exercise.
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.
Samsung adopted a thick data analytics approach to determine its next-generation television design concepts. Instead of solely relying on big data crunching, the company hired external help to conduct hours of interviews with customers, including the analysis of videos and conversations. Samsung wanted to understand how modern households viewed television sets as inputs to its engineering and marketing processes. What the Samsung research ultimately revealed is that people thought of TVs in their homes primarily as furniture and not as electronics.
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. Until now, communications were left up to human guesswork to determine what various cohorts wanted. But predictive analytics are powerful because they don’t have to be left up to human interpretation and action. Algorithms are now able to analyze data points and pull out patterns to better predict future behaviors. This is called machine learning.
Deep learning goes a step further in the machine learning spectrum, beyond task-specific algorithms, and mimics human biological nervous systems. Based on the patterns learned, the machine is able to adjust its performance and output in order to respond to consumer preferences faster and easier than ever before. This adjustment is done without any human intervention on the backend, so analysis and reaction to those findings can happen 24 hours a day, 7 days a week, and 365 days a year.