How to Be a Big Data Star

Understanding Big Data as a concept and becoming a Big Data star-- mastering the art of solving business problems with Big Data-based solutions – are two different things. The name Big Data is very vast and there is a possibility that If you’re an IT professional, you might have a pretty good handle on Big Data – and you might spend a lot of time trying to explain it to your colleagues. If you’re a business manager, you might not know a lot about Big Data, but you know you need strong data and analytics in order to make smart decisions.

Here’s how you can become a big data star :

First, understand the problem.
Technically, Big Data describes the increasing availability and growth of structured and unstructured data – information like phone numbers and zip codes make up structured data sets, and unstructured data is the messy stuff, like web page information and Internet videos.

Big Data also is defined by four characteristics: volume, variety, velocity, and veracity. With apologies to data scientists in the audience, here’s a simplified look at what these four V’s mean for the enterprise:

• You need a cost-effective solution to manage large volume of data i.e lots and lots of information
•  You need to easily process Varieties of information i.e different types of information for effective analysis
• You need to process data that’s constantly generated to make business decisions very quickly
• You need to quickly and easily understand the quality, and therefore the value, of the data

Why do all these data matter? Because, increasingly, companies make critical business decisions just as information becomes available – and therefore the quality of data and a business’s understanding of its impact is vital.

Another truth about Big Data: it grows. Quickly. By way of illustration, here are some data statistics (which, of course, will have changed by the time you have read this):

• 90% of world's data was created in the last four years
• 80% of the world's data today is unstructured
• Only 20% of available data can be processed by traditional systems
• 1 of 2 business leaders don't have access to data they need
• There are 500+ Million users posting 55 million tweets every day
• Active Facebook users? 1+ billion, spending a total of 700 million minutes per month
• By 2009, utilities and their customers had put 76 million smart meters in place, which will grow to 200 million by 2014

With so much information moving so quickly, every organization needs to harness it and put it to work in marketing programs, e-commerce platforms, logistics and supply chain management, and a host of other business-critical applications.

Big Data Stars make that happen by following the three P’s:

1. Prioritize: what initiatives are most critical to your business? For online retail companies, a stable e-commerce platform is mission-critical. But stable technology must be matched by a user experience that reflects a deep understanding customer behavior on the web – an understanding that can be gained only by studying the data. Whether it’s an e-commerce platform or a portal for engaging enterprise customers, your customer engagement interface is only as strong as the data behind it.
2. Prepare: getting ready for a transition to a data-eccentric culture is just like any other culture change – challenging. This makes preparation massively important. A new logistics system, for example, might be designed for optimizing a supply chain. But what does that mean for, say, customer service employees with associated transportation companies? Thinking through and developing communications and roll-out plans that address these downstream impacts is crucial when undertaking a Big Data project.
3. Partner wisely. Just as you wouldn’t want a resident fresh out of medical school to perform open-heart surgery on you or a loved one, you wouldn’t want a Big Data amateur working on the software and solutions that will contribute to the success of your business. This means you need technology partners who have worked with companies like yours – not necessarily in the same sector but with similar characteristics and needs.

With the three P’s in mind, a Big Data star can put his or her skills to work on almost any business challenge. Here are a few examples of the most common business applications for Big Data solutions:

1. Exploration. You can’t get started until you know where you’re going which Includes finding, visualizing, and understanding all big data to improve decision making. Business Information is spread across various systems, and people need access to that data to meet their requirements, therefore translating into important decisions. Exploration allows mining of big data to visualize and understand your data by creating a unified view of information residing across many sources.
2. 360 Customer view. Bend the data to your will and extend existing customer views by incorporating additional internal and external data sources in order to gain a complete, unified view of their behavior. What do they buy? How do they prefer to shop? Why did they switch? Which factors lead customers to recommend a company? What will they buy next? Organizations need to leverage all their data sources, (internal, external, structured, un-structured, and semi-structured) in order to assess customer sentiment.
3. Security and Intelligence. Success depends on choosing the right strategy by lowering risk, detecting fraud and monitoring cyber security, in real-time. Think about high-tech crimes such as cyber-based terrorism, espionage, or major cyber fraud. These crimes possess a severe threat to organizations and individuals alike. In order to address these security challenges, organizations need to enhance intelligence and cyber security analysis platforms with Big Data technologies to process new data types.
4. Operations Analysis. This area focuses on the analysis of a variety of unstructured and scattered machine data, such as machine logs, sensors and GPS devices for improved business results. Organizations can gain real-time insights into operations, transactions, behavior, and even customer experience, by using Big Data for operations analysis.
5. Get started with basic data integration use cases: reference architectures that map out how you can use data integration platforms to solve your biggest business challenges for example data warehouse augmentation  is the Integration of Big Data and data warehouse capabilities to increase operation efficiency. Big Data and data warehouse solutions are meant to coexist; Big Data solutions are meant to augment Data Warehouses, not to replace them. Organizations can combine streaming data sources to existing data warehouse investments. For example, Big Data capabilities can determine what data should move into the data warehouse, and which should live in a less-easy-to-access distributed file system like Hadoop.
6. A wise Big data star learns from those who have gone before. Luckily, their stories are documented so you can see how others are succeeding with everything from high level architectural decisions to nitty-gritty data transformations and seamless real-time analytics.

 In conclusion Once you understand the areas of opportunity for Big Data transformation in your organization, you’ll start to develop  – a predictive ability to see Big Data problems coming and plan accordingly. If you have used your Star-like skills to solve Big Data problems recently? I’d love to hear about it – drop me a comment.