The Latest Trends in Big Data Analytics to Watch Out For!
Big data has become one of hottest topics of discussion over the past few years. It plays a pivotal role in various aspects of businesses across industries and is also the favorite subject of academics too. The ability to mine massive volumes of data from a myriad of sources to analyze and gain insight has radically altered the dynamics of business functions, marketing, and sales.
Not just the major corporations, but even the start-ups may taste the success fruit by effectively uncovering the insights derived from data. Digging, analyzing, managing, and manipulating big data is pretty easy now, and most of the companies now have the ability to do it with minimal cost.
At the rise of 2017, we have seen that the businesses have far outgrown the basic concept of simply converting data to insights, but they can now use data to derive actionable and directive principles. The focus is now more on mining the data effectively in light of the actual organizational goals and by precisely targeting specific products and services.
This insight-driven approach will get more strengthened by the third quarter of 2017 to facilitate enhanced customer experience, market competitiveness, advanced level of security, and increased operational efficiency.
For example, a company allocates most of its marketing budget for promotions through conventional channels such as print and television. However, on analyzing the consumer decision-making process, it was found that majority of the customers go through the retailers' websites to compare products, and hardly less than 10 percent visit their site.
Once the company effectively shifted from the conventional advertising to strengthening online content, the e-commerce sales of the company instantly increased by about 30 percent. Further, we will discuss some practical big data analytics insights for effectively knitting your business plans.
Applying analytics across the spectrum
Analytics will no more remain isolated to a particular department of an enterprise such as marketing or risk assessment. In fact, it will cover the entire enterprise, and data analytics will be effectively used to understand the end-to-end operational dynamics of business.
When done correctly, this will unveil more means to increase the overall functional efficiency and productivity of the organization. Insights from various departments will be integrated to derive a comprehensive business strategy, which will help eliminate overheads and redundant processes and also increase efficiency and growth within each department.
The next phase of Internet of Things
Another buzzword in the IT sector is Internet of Things, in which we may see the transition to Internet of People (IoP) at the next phase. Big data analytics may focus more on predictive analytics centering around human interactions, behaviors, and other cognitive specialties at all industry verticals.
For example, healthcare facilities may deploy machine learning processes to predict the likelihood of a disease's relapse on a particular person. This will help them precisely work out a patient care plan. For a manufacturer, predictive analysis will help to decide the next production cycle and distribution plan to a particular region.
Big data and cyber security
Without even knowing the extent of it, our dependence on digital technologies has reached to an unprecedented degree. Everyone tends to do site search now and then for a variety of needs. So, the possibility of a cyber attack is much more dreadful and far-reaching now. Apart from focusing on the major corporations and financial institutions, the hackers are now focusing more on personal data and devices. There is an increasing number of ransomware attacks getting reported across the globe lately.
With all these, cyber security has become a growing concern of the industry. Making it possible through the big data analytics is expected to be a rapidly growing sector in the coming years. Enterprises will upgrade their security processes to the next-generation technologies to handle the ultra-modern security hazards. Global cyber security spending is expected to cross the $1 trillion mark.
Application of artificial intelligence
Rather than depending on the conventional methods, businesses may have to deploy scientific techniques and artificial intelligence for better big data analytics. Pattern-matching and use case analysis, for example, are used to analyze DNA, and the text-matching algorithms widely practiced in bulk mailing campaigns.
In 2017, we get to see a close collaboration among the data scientists and scientific community, particularly in the specialties like neuroscience, particle physics, organic chemistry, molecular biology, and astrophysics. You can also see that image processing is now primarily used in social media tagging, whereas voice recognition is in use in personal assistant apps like Siri.
Bridging talent gap
As the demand is steeply increasing in data analytics, it is evident that there is a talent gap to be effectively filled in the field of big data. Corporate and academic centers need to closely collaborate to generate talent and industry-specific skills to meet the demand for qualified and experienced data engineers.
As big data analytics emerges as one of the key instruments in all size of organizations, employees from all functional capacities are expected to understand and work with various data analytics tools in future. As per the latest research from McKinsey, the shortage of skilled personnel in 2017 itself will be about 200,000 in the US alone, and it may double up by next year.
Academic institutions now started offering degree and diploma programs in data science. There are also boot camp style training schools offering crash courses in data analytics. The programs impart the essential data mining and process skills and make the participants capable of devising strategies to enhance the business operation and marketing approach.