We asked 13 executives involved in Big Data this question. Their answers refer to what their company is doing as well as how Big Data is affecting their lives today.
Dr. Greg Curtin, CEO and Founder, Civic Resource Group | Mikko Jarva, CTO Intelligent Data, Comptel Corporation |Matt Pfeil, CCO Co-Founder, DataStax |Dan Potter, CMO, Datawatch |Gena Rotstein, CEO and Founder, Dexterity Ventures, Inc. | Puneet Pandit, Founder and CEO, Glassbeam | Philip Rathle, VP Products, Neo Technology, Inc. | Guy Kol, Founder and V.P. R&D, NRGene | Hari Sankar, VP of Product Management, Oracle | Ray Kingman, CEO, Semcasting | Scott Sundvor, CTO, 6SensorLabs | Vikram Gaitonde, Vice President Products, Solix Technologies | Paul Kent, SVP Big Data, SAS | Margaret Roth, Co-Founder and CMO, Yet Analytics |
Here’s what they had to say:
We are able to start bridging the gap between qualitative and quantitative. Different systems of hardware and software are being built to capture the data of experiences - where someone was, who they were with, what did they do, what did they do next. By triangulating data sources we will be able to predict what a customer needs or wants thereby providing a better customer experience.
We’re already using Big Data to improve the LA transit system, implement the San Diego WaterSmart Target program, create Santa Monica as a Smart City, and create the Mission Viejo eGovernment.
It’s a very iterative process. By sequencing rice genomes and wheat genomes we open up huge opportunities to address stresses that prevent the breed from moving ahead. We can do this quickly and effectively to improve crop production in Africa and India. A group of people have been working on sequencing the wheat genome for the past 10 years spending $50 million and they’re only 1/17th of the way there. We took the 12 gigabyte genome without mapping into parts, reconstructed technologically for $500,000. We’ll be finished in a year for about $1 million.
There’s a huge policy shift in how we regulate and manage charities is needed. What are the economic costs of some of the problems we’re not solving. Big Data is improving the efficiency and performance.
Major advancements are being seen in healthcare and hospitals. Able to go from reactive to preventive/proactive by monitoring patients in the hospital and remotely. This improves recovery and enables us to address problems before they occur.
It’s nascent at this time. Big Data is hitting an inflection point where every device (automobiles, appliances, smartphones and internet) has 25 to 50 million readings. When the device information is collected, analytics ratchets up the value of Big Data. For example, Home Depot will be able to better manage their inventory at the store level while also proactively connecting with end users that it's time to take some action (e.g., change a filter, buy more light bulbs, have HVAC serviced). Big Data enables the anticipation of needs.
IoT analytics is the killer app for Big Data. Support analytics is key for manufacturers concerned about becoming proactive and predictive by mining the M2M datastream. We’re able to take equipment off-line for preventive maintenance at a predetermined time that doesn’t negatively affect workflow versus waiting for an unplanned. emergency.
Customer projects in the telco space are looking for ways to apply the data they’re generating to understand their customers better. Need to Identify value generation opportunities around customer lifecycle management. Maintaining devices, networks and technology in a proactive and predictive manner. Preventive maintenance to predict device and network failure and identify the root cause. Identifying the use cases around data retention and reporting. Current requirements are to store data for 10 years. Agile storage and access of data will help meet these requirements. Predictive analytics are not yet being used yet; however, they will enable telcos to be proactive rather than reactive.
Intellectual property tech company client was very protective of their files. Because of the volume of the information stored, it would take months to find the root cause and see who inside the company were attempting to download protected assets.The company started using Solix who built a dashboard to provide information in minutes. We learned that the people attempting to download files, to which they were denied access, were about to leave the company. So the company ended up having a leading predictor of who was about to leave. An unintended consequence, and benefit, of being able to analyze Big Data in real time.
A major airline has a passenger in first class, with a large number of Twitter followers, tweeting about the bad food before the flight even left the gate. A follower of this person was a member of the airline ground staff and was able to message the flight crew who went out of their way to provide an outstanding customer experience for this passenger. The passenger tweeted about how terrific the airline was when he landed. The airline now has real-time social media monitoring dashboard to see who’s saying what to be able to preemptively address customer concerns.
An automobile manufacturer has three different SAP installations. It would take two years to build out a business works repository where all three SAP installations could talk to each other. They can now access all three, pull a report, extract the data, transform and analyze it in minutes. This provided a huge cost and time savings for the client.
A retailer in Canada was pulling hard copy “green sheet” reports every morning and performing a manual reconciliation of sales and inventory for every SKU. We got the reports, digitized them, extracted the data and did the reconciliation in minutes.
I personally have a Nest thermostat and a scale that connects with the internet. The scale syncs to my iPhone with Healthkit stores and provides invaluable information. They both prove great user experiences. The core of software is the database. As data is used more places, it will invade everywhere.
We’re taking antiquated batch processes based on last night’s data and now doing it in real-time. Talked to four analysts in the last month and they each had their own term: insights into action, transactional analytics, translytics, operationalizing analytics. By 2018, 70% of all organizations will be using operational analytics to make decisions.
What drives the greatest value is highly dependent on the organization you’re talking to. The range of problems addressed by Big Data is really broad. Many businesses find value in improving one-to-one customer engagement; others target efficient business operations by identifying and eliminating wasted resources; fraud detection is an important area in financial services; in life sciences, faster and more effective discovery of drugs drives substantial value; public sector organizations target an entirely different set of problems using Big Data. So, there is really no single formula for unlocking maximum value. When working with customers who are looking at Big Data for the first time, we like to start with a discovery workshop to help uncover the optimal projects to work on, get both the business and technology stakeholders together and ensure alignment across the company to maximize the likelihood of success.