From BI to Advanced Analytics

When a large hospital system wanted to reduce patient re-admission, they turned to business intelligence and analytics to help them. After reviewing millions of records of several years, a team applied advanced analytics modeling, which identified and scored patients at risk of re-admission based on their health history, demographics, and life style. Then, they developed a predictive modeling solution along with an interactive dashboard that also had things like individualized risk scores and helped the hospital reduce re-admission rates.

A major bank used analytics to study its debit card usage patterns, which helped it identify fraudulent activities better; and a university improved student retention by understanding the various variables that impacted a student's performance and providing prescriptive analytics to instructors for taking corrective actions. Further, this real-time advanced analytics was transferred onto mobile devices. They were also able to simplify the disparate and varied pieces of data and information that were streaming in, which helped them get a holistic view of the student data.

Companies are slowly but surely reaping the rewards of investing in business intelligence and analytics solutions. Today, the need for intelligent data analytics is growing steadily and analytics is moving from being an adjunct activity to becoming embedded deeper into the core business. A Gartner report says that by 2015, 25% of analytics capabilities will be embedded, compared to only 5% in 2010. Companies are already investing in analytics technologies to help them get that extra edge. They are looking for services and solutions that can help them understand large amounts of data, manage it effectively, so they can get better insight, and use it to make better decisions for their business.

Drawing intelligence from data

Business intelligence has been around for a little over two decades now. While earlier the focus was on storing data effectively and creating reports for the management to base their decisions on, today, it's about applying the analytics layer to that data. While business intelligence can simplify data and help present it in a way that makes sense, analytics can turn that data into information and insight.

Because today, it's not enough to know about existing or past data, it's not enough for a manufacturing company to know the inventory levels of the last quarter. What they actually need to know is what it will be, or rather should be for the next quarter. And this information can be based on several factors, including forecasted demand, market conditions, and seasonal spikes among others. In times where market dynamics are changing quickly, such information is critical for winning in the marketplace. The need of the hour is quick, accurate, and insightful information. McKinsey says, "a retailer using big data to the full has the potential to increase its operating margin by more than 60%.

Companies seek foresight, not insight alone

But why is the need for data analytics growing so rapidly? Because, today, data is coming in faster than businesses are equipped to cope with. Leading industry analysts predict that the size of the data will explode at a rate of 650% over the next five years! How can you make sense of this ever-growing data? How do you tame it? And, it's not only the volume that companies have to contend with, but also the velocity and variety. There are log files, texts, images and social media, add to that the varied number of new devices and technologies, which are churning out data every second.

This data explosion has been spurred by a variety of factors, key among them is social media and mobility. Real-time interactions with customers and cross-channel interactions are key factors in the data spike. Each time a customer interacts with a brand online, he creates data. If the same customer interacts with the same brand via mobile channel, he creates more data and what's more, it becomes cross-channel now. Imagine the thousands of customers interacting with the brand across channels. This exponential growth of data-most of which is unstructured-is creating several challenges for companies to store, manage, truly understand its value and more importantly use it to make smart business decisions. It is equally creating opportunities for service providers to help customers find solutions that will help them own the moment and make the best use of it. And they need these solutions to be real-time and effective.

Traditional BI solutions, though useful have limitations. For instance, they are not quite equipped to process the new and varied data sources and produce updates and reports on the fly. And while these systems may offer analytics and a good assessment of past data, the need of the hour is the ‘now data' and solutions that help them foresee-based on trends and analysis-what the future holds and even give them the power to shape it. Companies need a telescopic lens that will help them make the right decision quickly. It's not enough to control the large amount of data traffic, but predict in which direction it will move, at what speed and eventually control the traffic lights.

Analytics has evolved from being a rear mirror view (descriptive) to a telescopic view (predictive) where analytics provides the businesses with a view of the road ahead, by predicting business outcomes based on huge volumes and variety of data. The next stage of evolution that is already underway is analytics becoming prescriptive. Here, advanced analytics will not only be used to predict but also prescribe optimized actions to derive best possible business outcomes.

New Tools, Technologies & Skillsets

New problems need new solutions. The need to store and analyze huge volumes of data across a wide variety that is growing at an unprecedented velocity requires a whole new technology tool kit. There is a combination of both proprietary and open source technologies that are evolving to address this need. Traditional systems that were not capable of efficiently storing unstructured data are being replaced by some new open source driven products and frameworks. New technologies like in-memory computing are enabling the real time processing and rendering the much needed speed to analytics.

Companies need to also look at modernizing their BI and analytics landscape and infrastructure in this context. This modernization has several components-having the right technologies to address the analytical needs of the present and the future, having access to the right people, who have true insight, be they data architects or data scientists; the right process, that can help them innovate, including right governance processes and data stewardship. Modernizing BI can help companies better understand the value of their business intelligence and make decisions faster; it can also help them manage staff efficiencies and software costs more effectively.

The exponential growth of data and the ability to deploy rigorous analytics on it is a trend that companies cannot ignore if they want to stay competitive. They need to seek solutions that will help them get actionable intelligence from this growing body of data-solutions that will give them the power to shape their own future.