Does Real-Time Analysis Help?

Many people ask the question that is there actually a need for real-time analysis. How would it help them in their business and is it worth the kind of investment it needs to get into real-time analysis?

You can analyse your business to see how you can use it for your business development. We can site a few small examples of different streams that organizations have used to see the benefits. Let us start with healthcare – a small device worn on the waist belt injects desired quantities of Insulin to a  diabetic patient while monitoring the sugar level through the device.

Imagine you enter a hotel and the person at reception knows your name, booking details and your preferences. While the second part is already in control through club memberships, the camera on the door takes your picture, searches for the record and by the time you reach the reception, your details are on the desk.

Someone walking near the store gets a message on additional offers on their favourite product for the next 30 minutes.  If you reject a product from the shelf, you get a message of additional offers on the product. Always get a win-win situation for both the store and the consumer.

From Just Analysis to Real-Time Analysis

Human race has been playing with data for ever. Large volumes of data being generated, and reports created of these, have been the traditional approach the way business have been run and expanded. The future prospects were based on some trends of your own organization, market and competitors and all this was based on the performance.

Analysts have been analysing this data to help organization plan the above.

Times have changed now and businesses cannot only rely on past performances to plan their future. You need to capture the current trends and the needs of the consumer today. Analytics have changed now from analysing past data to perform real-time data. Data generated not only from the structured in-house databases but also from non-structured data generated through social media and consumer behaviour.

Tools are available today to collect data from these sources and put them together through what is called the process of “Data Conditioning” into databases to help analyse them. All this data gets processed real time to produce near instant results helping the businesses serve their consumers better.

What is Data Science?

While most of us believe Data Science is handling large volumes of data effectively and efficiently, Data Science is not restricted to this. Merely using data isn’t really what is meant by “data science.” A data application acquires its value from the data itself, and creates more data as a result. It’s not just an application with data; it’s a data product. Data science enables the creation of data products.

Several organizations have used this well and have given the industry several products that help handle Data Science. Key examples are from Google itself who realized the importance of a search engine and further made it more effective using tracking links. Spell checks with suggestions and speech recognition are other examples of Google’s way of creating products out of data. Facebook and Linkedin have used patterns of friendship relationships to suggest other people you may know or should know, sometimes with great accuracy.

The thread that ties most of these applications together is that data collected from users provides added value. Whether that data is search terms, voice samples, or product reviews, the users are in a feedback loop in which they contribute to the products they use. That’s the beginning of data science.