5 Tips For Turning Big Data To Big Success | Big Data
Summary
Big Data investments are revolutionizing industries from travel to telecom. To drive market growth, CMOs seek revenue generation from accumulated customer data, while CIOs aim for feasible Big Data initiatives. These 5 tips enhance success: ensure processing power and system reliability; plan for long-term adaptability; utilize Hadoop in warehouses for structured and unstructured data; combine technology with natural instincts; prioritize safe storage with strong security measures. Employing these strategies positions businesses to effectively harness Big Data's potential for success.
Table of Content
From travel to media, marketing, telecom, and more, investments in Big Data are impacting the current business scenario dramatically—and in more ways than one. With Fortune 500 companies and other organizations jumping into the fray, it has now become important to understand Big Data trends. Yes, there’s an urgent need to master these trends for bigger success across different industry verticals.
Globally, chief information officers (CIOs) and chief marketing officers (CMOs) are now facing grave challenges with regard to converting big data into equally large-sized profits. With a view of driving unprecedented business growth, CMOs are demanding that the customer data accumulated over the years be now used for generating increased revenue. On the other hand, CIOs, having the knowledge and technology for boosting revenues, are insisting on more feasible requirements for undertaking big data initiatives.
These 5 top tips for turning Big Data initiatives into the above-mentioned market growth will help your cause too. Read on for more!
1. What’s the Processing Power Like?
These days, organizations are boasting of an ever-increasing pool of business-specific data that’s extrapolating every minute. A powerful processor has to be in place before these companies start driving their massive volumes of data toward effective assets. Here, many questions arise with regard to your Big Data implementation needs too. What are the ways of processing data? Are they fast enough? Are your systems reliable? And so forth. The right processing systems, or supercomputers as they are called, are required for the accurate and timely processing of data. You need to track the performance metrics of your existing processing system and ensure that you have the appropriate bandwidth and capability for managing them effectively (also consider checking out this career guide for data science jobs).
2. Readily Adapt Changes and look toward Long Term Planning
Big Data is all about looking toward the future. With it in place, there’s no way that you can remain confined to present-day technologies and resources. In the current scenario, data pools are becoming larger at a breathtaking speed. You need to be technologically equipped to manage the same. So, as you gear up to handle the many benefits and challenges of Big Data with its best practices, tools, and methodologies, you need to be prepared for all that might come your way tomorrow. Flexibility in business intelligence, complete openness to new product ideas, and long-term planning are what you need to invest in at this stage.
3. Hadoop in Warehouse
The presence of Hadoop in warehouses is working just great for many industries. While Hadoop is responsible for all the unstructured data that’s capable of being utilized for future use, the data warehouses are stacking structured and important data. Hadoop is best at analytic processing and totally complements the shortcomings of data warehouses in this field. Overall, the combo works just great!
4. Your Natural Instincts Matter too!
Despite having all the benefits of high-end technology in place, your natural instincts pertaining to understanding patterns and detecting flaws are very important too. Along with visual discovery tools that are useful for procuring timely information, it’s essential to involve better data analysis tricks for treating all kinds of data differently. Yes, do know that different approaches are required for different Big Data pools—so you need to act accordingly (Here's the perfect parcel of information to learn data science).
5. Safe Storage is a Must
Big Data security should obviously be the foremost priority as organizations go forward with its implementation. It’s advisable to understand all current and future data risks, identify common attacks, and aim for the highest levels of security in data storage. From encrypting all kinds of data to the authentication of store keys—everything should be 100 percent safe. The good news is that it is not very complicated or costly to secure Big Data; it all depends on how smartly the big data environment has been created.
So, are you prepared to adopt these 5 effective tips for turning Big Data into a big success—begin right away!
