Big Data technologies are changing the competitive landscape of business in every industry. But, as is the case with anything else, it means different things to different roles in the organization.
Business people typically want to understand what is “Big” about “Data”? Aren’t we already warehousing vast amounts of data and running analytics today? What will Big Data do to make a difference, and how much “bigger” can it get?
These questions are spot on. For starters, Big Data is data at such high volumes that it cannot be stored, crunched and accessed using traditional Relational Databases (RDBMS). Big Data technology is a large volume data management infrastructure as well as processes and tools to work with this large data infrastructure. The inability of the RDBMS landscape to process what is seen as “extraneous” data is limiting many of the insights that the business could have obtained to be competitive.
For the Business, Big Data processes data at high volumes and velocity, to generate new insights in real-time, making businesses more competitive.
Embracing Big Data does not mean throwing away the old ways of collecting, accessing, and using data. Nor does it mean overhauling the traditional processes in data management that is presently being used. Rather, it means, using Big Data technologies to augment the existing systems and processes.
IT’s key focus with Big Data is to deliver new value to business; Big Data increases the value of your existing data by integrating new streams of data, at high volumes, to generate more insightful analytics. As long as IT is focused on delivering new insights to business, the ROI naturally weaves itself in.
For IT, Big Data augments existing data with new streams of data at high volumes and velocity, creating a new, fertile processing environment to generate new insights at high speed.
In high-stakes manufacturing, such as Engines, Turbines, Machinery and equipment, failures and mistakes can cost billions – and human lives. It’s therefore crucial the company is able to monitor the health of their products to spot potential problems before they occur. The Design process produces terabytes of data that needs to be ingested, crunched and visualized across various data sets to spot design lacunae and good outcomes. An IoT driven manufacturing plant, tightly networks the actual environment and process towards more error-free automation. In the post-sales world, products delivered to customers come embedded with sensors for massive IoT driven analytics that feeds product and operations groups, and enhances the proactive maintenance and recall experience.
The Sales > Insight > Inventory > Logistics > Sales cycle in the Supply Chain is crucial in Retail. Timely analysis of real-time data is seen as key to driving business performance. In a complex global supply-chain environment, the goal is always to get information to business partners as fast as possible, so they can take action and cut down the turnaround time. It is proactive and reactive analytics. Event driven sales is even more crucial and sales across different stores in different geographical areas can also be monitored in real-time. One Halloween, sales figures at a Retailer, of novelty cookies were being monitored, when analysts saw that there were several locations where they weren’t selling at all. This enabled them to trigger an alert to the merchandising teams responsible for those stores, who quickly realized that the products hadn’t even been put on the shelves. Not exactly a complex algorithm, but it wouldn’t have been possible without Big Data real-time analytics. In e-commerce, Prediction Engines and Customer behavioral insights are key to driving sales.
Behavioral Analytics plays a big role in media and communications. With access to data on consumer behavior, companies can learn what prompts a user to stick around longer, and learn more about their characteristics and purchasing habits. Mobile Phone companies use multiple indicators like sentiment analysis and billing to determine who the high value, long term customers are and work to keep them satisfied. Media companies need a 360-view of how frequently customers tune in, and for how long, and peak usage times. This drives features to improve stickiness and deliver differentiated offerings in the marketplace.
The problem in healthcare is not lack of data, but the unstructured nature of its data; a staggering 80% of medical and clinical information about patients is formed of unstructured data, such as written physician notes. For a holistic understanding of care, we need to be able to mine unstructured data for insights. The goal of Big Data is for the data to be analyzed at an individual level to create a patient data model, and it can also be aggregated across the population in order to derive larger insights around the disease prevalence, treatment patterns, etc.
Across Financial Services, we are looking at Big Data Analytics for Customer-360 view, Big Data insights to drive Predictive Trading algorithms, Fraud Prevention in transactions, among other use cases
Energy is an IoT-rich environment creating a reliable environment for timely billing, proactive maintenance, network planning and outage planning, disaster planning and other key functions.
With a treasure trove of public data, local and national governments can increase tax collection compliance, optimize labor costs through employee and contractor productivity monitoring and increase transparency through timely publication of data and insights to improve citizenry.
There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help get the most value from information.
At Scadea, we have built a practice around Hadoop and complementary tools to deliver value to your Big Data journey.
We have experienced architects, with continuously updated skillset, working in both AWS and Azure Cloud environments. Our architects can work with Enterprise clients presenting complex solutions:
Our Hadoop Developers have certified skills in Apache/Spark/Linux/NO-SQL environments. Also confident in several programming languages such as Python, C#, and Java.