What are the latest trends in big data and analytics?

Overview: Big data technology is coming up with best practices and better trends every day. Big data is gradually coming into main stream projects also and gaining momentum. With big data, analytics is also getting much importance, as it is now capable of providing good insight into decision making processes.

In this article we will talk about the latest trends in the big data and analytics world.

Introduction: In current years, big data and analytics are the most focused area where all the organizations are concentrating. Applying analytics on vast amount of data using big data platform is also producing attractive results. It also helps the organizations to understand customer behavior.

In the following section, I will describe the trends one by one.

Analytics driven insight: As we know big data is all about vast volume of data from different sources. So without data, big data platforms are of no use. The other important aspect is 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 the intuitive decision making process, which might not be correct always. Organizations are trying to apply analytics in all the business areas where ever 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 applications. This is also true for big data applications. I would say, data security and privacy are more important especially 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 current 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 new area which needs to be investigated in more details. Companies are also investing into different big data platforms to explore the advantages and disadvantages. We 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 requirement and the end goal is not correctly planned. Companies are also investing into analytics tools which 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 organization culture: To accommodate big data trends, organization 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 organization culture.

Importance of data scientists: As the name suggests ‘Big data’, the importance of data has the top priority. As a consequence, people who are having expertise in data science is become an integral part of big data analytics. The expertise of data officer/scientists cover all the fields like data collection, data cleansing, data processing, extracting meaningful information by applying statistical algorithms/models etc. This data processing is a continuous process as the input data sources changes every day. The characteristics of data, its format, and volume all are having significant impact on the statistical analysis. So the data scientists should evaluate these aspects on a regular basis and provide input to the organization. The other aspect is to separate the meaningful data from the huge volume of input and discard the rest. Because, processing of data is a costly and time consuming. So the importance should be given on the extraction process and then apply analytics on top of it. In the coming years, data scientists will have great importance and demand. So the organizations should invest in resources having excellent understanding of data science.

Smart big data and analytics apps: Big data and analytics applications are different compared to the traditional applications. All these big data and analytics applications are smart applications as they have the self learning algorithm inbuilt. More and more Organizations have started working on analytics applications based on the big data. All of them are trying to bring the result of analytics to the masses and create significant impact to the improvement of common people. The main focus is on creating smart ‘self learning’ and ‘self service’ applications. These applications are smart enough to train themselves and improve over the time. As a result, organizations do not need to invest continuously on human resources like data scientists, application developers etc. In the coming year’s different startups, ISVs will come up to produce more and more smart analytics applications.

Importance of outside data: Identifying the input source of data is an important aspect. The success of latest big data analytics is significantly depends upon the input data sources. Few years ago, we did not have this wealth of data. During the last couple of years we have seen data explosion from various sources like mobile devices, social media, sensors, computers and many more. But initially we did not have the expertise to capture these data and use it in our processing. Now, the new technologies like Apache Hadoop (based on ‘distributed processing’) are coming up in a big way and helping the organizations to tap these oceans of data. The data available inside organizations were always accessible for processing, but capturing the outside data was almost impossible. But the reality is, these outside data percentage is much larger compared to the inside data volume. So it is very important to put more importance on the outside data.

Summary: For the last couple of years big data and analytics is become a point of discussion everywhere. In the coming years also it will play a significant role in data analytics. Earlier also, analytics were there, but 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 taken based on the past experiences. But now a day, the result of analytics based on the big data produces meaningful insight and predictions. Now the organizations are relying more on the analytics result and getting 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 also. The trends are always dependent on the latest development in the business and technology area. So it is also true for big data and its future.

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