In the last few years, many organizations have been concentrating on big data and analytics and applying analytics to vast amounts of data using a big data platform have been producing attractive results. This analysis also helps the organizations to understand customer behavior. This article will describe the current trends one by one.
As we know, big data is all about a vast volume of data from different sources and it's important to use this data to get real actionable insight, which is commonly known as analytics-derived insight. So the trend is to grow in the data driven analytics area based on big data platforms. As a benefit, companies do not need to depend on an "intuitive" decision-making process, which might not be always infallible. Large Organizations are trying to apply analytics in all the business areas wherever there is a possibility. As a result, they are getting clear visibility and reliable predictions.
Big Data Privacy and Security
Security and privacy are the two most important keywords involved in any software application. This is also true for big data applications. Data security and privacy are especially important in big data applications because it is all about data processing and gaining insight. So the organizations are getting serious and taking proper steps to ensure the privacy and security of their data (which is a gold mine). In the coming years, companies will put more focus on building a strict security, privacy and governance policies for their big data initiatives. It is also important to remember that big data sources and technologies are increasing day by day. So the security policies should change continuously to meet the need of the changed environment. Big data is a vast area, so the security policies should be made robust and flexible.
More Investment in Big Data Projects
Big data is a newer area that requires investigation in deeper detail, as companies are investing in various big data platforms to explore the advantages and disadvantages. We all know that big data insights are not freely available, but the investment has to be made strategically. There is always a chance of bad investment, if the requirements and the end goal are not correctly planned. Companies are also investing analytics tools that are capable of handling big data output and make sense to the end user. The demand for these analytics tools and big data platforms are increasing every day. But it is the responsibility of the organization to evaluate the features and capabilities of these tools before investing big money.
Change in Organizational Culture
To accommodate big data trends, organizational culture needs to be changed. In the past, data and analytics were the responsibility of a specific team in an organization. It was a completely separate project and confined within a particular unit. To get the real benefit of big data and analytics, all the units of an organization have to participate in the initiative. In the coming years there will be a significant change in organizational culture.
Importance of Data Scientists
As the name 'Big data' suggests, the importance of data has the top priority. As a consequence, people with expertise in data science are become an integral part of big data analytics. The expertise of data officer/scientists cover all fields such as data collection, data cleansing, data processing, extracting meaningful information by applying statistical algorithms/models, etc. This data processing is continuous as the input data sources changes frequently. The characteristics of data, its format and volume all have a significant impact on the statistical analysis. So the data scientists should evaluate these aspects on a regular basis and provide input to the organization. It's very important to properly to separate the meaningful data from the huge volume of input and discard the rest because processing data is costly and time consuming. In the coming years, data scientists will have great importance and demand and organizations should invest in resources with an excellent understanding of data science.
Smart Big Data and Analytics Apps
Big data and analytics applications are different compared with traditional applications. Big data and analytics applications are smart applications with a built in self-learning algorithm. More and more organizations have started working on analytics applications based on big data. Each is trying to bring the benefits of analytics to the masses and create a significant. These applications are smart enough to train themselves and improve over time. As a result, organizations do not need to invest continuously in human resources such as data scientists, application developers, etc. In the coming years, a multitude of startups and ISVs will appear to produce increasingly smart analytics applications.
Importance of Outside Data
The success of big data analytics depends upon the input data sources. A few years ago, we did not have this wealth of data. During the last couple of years we have seen a data explosion from various sources such as mobile devices, social media, sensors, computers and many more. Initially the expertise to capture these data and use it in our processing did not exist. Now, new technologies such as Apache Hadoop (based on 'distributed processing') are coming up in a big way and helping to tap these oceans of data. The data available inside an organization was always accessible for processing, but capturing the outside data was almost impossible. But the reality is, the outside data percentage is much larger compared with the inside data volume.
For the last couple of years, big data and analytics have become a point of discussion everywhere. In the coming years it will also play a significant role in the success or failure of many companies. In previous years, while analytics were available, the data was structured and volume was much lower, so the results of analytics were, to some extent, limited. As a consequence, most of the business decisions were made based on the past experiences not hard data. But now a days, the result of analytics based on big data produces meaningful insight and predictions. Organizations are now able to rely more on the analytics and are more likely to get a good return on investment. In this article I have discussed some of the major trends in big data and analytics domain. But we must remember that the trends are ever changing and it will keep on changing in the coming years as well.
About the Author
Kaushik Pal is a technical architect with 15 years of experience in enterprise application and product development. He has expertise in web technologies, architecture/design, java/j2ee, Open source and big data technologies. You can find more of his work at www.techalpine.com and you can email him here.