Technology is great, most of the time at least. It simplifies processes, increases our productivity and allows us to innovate.
It also has its issues, one of which is all of the data it generates. According to IDC, the world could generate 180 trillion gigabytes of data by 2025, more than an 18-fold increase from the amount of data present in 2015.
Having data is one thing, but making sense of it is another, Amir Orad said. Mr. Orad is the CEO of Sisense, a business analytics company that simplifies complex data preparation, analysis and visualization. Its single stack solution provides all the technology a company needs for data management, visualization and transformation in one tool.
“The reception has been amazing,” Mr. Orad began. “We found that half our customers ended up being people who provide their own customers insights on data they already have.”
While people have been using data over the years to run companies, no one thought of taking that data and making it a product on its own, Mr. Orad explained.
And that product has value. A financial institution can compare its high wealth benchmarking tool against industry peers. Utilities tracking usage patterns, consumer industries and healthcare providers are others sitting on treasure troves of data.
First you had to recognize data’s value, then you had to capitalize on it, which was a cumbersome process, Mr. Orad said. Until recently such an interested company would have to create an IT-driven project where they meet with the IT team, define objectives, build a data warehouse, and determine what they want to measure. A few months later a report would be generated which may not even still be relevant if new data sources or corporate priorities were identified in the interim.
With Sisense you don’t have to build the mountain, Mr. Orad said. You are agile enough to generate relevant data sets on the fly using their single stack technology.
“That changed the whole landscape,” Mr. Orad said.
In order to be truly valuable, such a technology has to adapt to varied use cases, Mr. Orad said. A local team in a large corporation wants to interpret a data set that has meaning to them but which is generic to the rest of the company. An opportunity quickly arises that mandates a rapid response. Non-technical personnel quickly need information but cannot access the IT department.
Companies are also finding benefit in providing embedded analytics to the services they provide their customers, whether it be outside financial services or crucial data related to medical devices. The machines can collect the data and quickly provide accurate information to investment advisors or doctors who need it now.
“That accomplishes two things,” Mr. Orad explained. “It cuts costs dramatically and becomes a differentiator in the space. You will sell more MRI machines if you provide that value added service.”
The marketplace is ready to embrace organized data interpretation, Mr. Orad suggested. Netflix tells us what we should watch, and Amazon is eerily good at predicting what we’ll buy.
“If we’re exposed to all of that as consumers, we should expect similar advanced results when we get our lab results and manage our stock portfolio,” Mr. Orad said.
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