Data is often considered to be the crown jewels of an organization. It can be used in myriad ways to run the business, market to customers, forecast sales, measure performance, gain competitive advantage, and discover new business opportunities. And lately, a convergence of new technologies and market dynamics has opened a new frontier for information management and analysis. This new wave of computing involves data with far greater volume, velocity, and variety than ever before.
Big Data, as it is called, is being used in ingenious ways to predict customer buying habits, detect fraud and waste, analyze product sentiment, and react quickly to events and changes in business conditions. It is also a driving force behind new business opportunities. Most companies already use analytics in the form of reports and dashboards to help run their business. This is largely based on well structured data from operational systems that conform to pre-determined relationships.
Big Data, however, doesn’t follow this structured model. The streams are all different and it is difficult to establish common relationships. But with its diversity and abundance come opportunities to learn and to develop new ideas – ideas that can help change the business. To run the business, you organize data to make it do something specific to change the business, you take data as-is and determine what it can do for you. These two approaches are more powerful together than either alone. In fact, many innovative solutions are a combination of both approaches. For instance, a major European car manufacturer is collecting data via telematics from cars they produce. This data is used to influence offers they make to their customers. It is also used to better understand the conditions that the car has experienced, which in turn helps in root-cause failure analysis as well as in future automobile design.
The architectural challenge is to bring the two paradigms together. Sensors and all other connected devices in the IoT world generates streams of data. When taken together, they represent a volume that is big enough – but to start with the scale may not be big enough that it warrants the power of Big Data analytics platforms. Our research estimates that not all these industries require those powerful and expensive Big Data Analytics infrastructure. For many of them, a much simpler, minimalistic, cost-effective yet fast and efficient systems makes better sense. We call this Data Analytics Solutions for Small Business and start-ups.
For the past few years, we’ve helped several small businesses and start-ups to build a tailored, fast and simplistic data analytics solutions.
Over the past few decades, waste management has become an important issue all over the world. Operational costs for these businesses are not cheap, however. Trucks, manpower and resources can be a major expense for waste management companies. However my idea is to reduce the costs of operating a waste management enterprise with the help of data analytics.By installing sensors in individual trash receptacles, researchers can monitor variables such as volume and temperature to determine the most optimal time for workers to dispose of their contents.
Waste management companies could use this information to create more efficient collection routes as well as time the duration between visits. There are many potential benefits to this big data application. From the consumer standpoint, receptacles will be emptied before the volume of waste becomes too unmanageable, leading to fewer overflows. For waste management businesses, collection efforts can be more effective with fewer trips being made to collect bins containing little to no trash. This can lead to fewer expenses being spent on trucks, payroll and other resources.