Big Data analytics – an IT requirement

There is a persistent hype in market when ‘Big Data’ is spoken of. However, data taken into consideration is nothing new. It’s just presented in a better assorted form with higher volumes taking over. With the rise of Internet industry, the volume of data flowing in and out has become colossal. And that’s when the industry experts came up with the concept of ‘Big Data’.

Big Data is typically a dataset, which happens to be huge and defined by several attributes. Also the defined dataset is not specific to a certain horizontal of industry. It traverses around different levels in the industry and gathers attributes which make it highly impossible to store and analyze for a typical Relational Database Management System. To fill the gaps, technical fore runners have come up with NoSQL databases like Hadoop to store this massive amount of data. However, data stored cannot produce results without applying analytics to it. So, Big Data Analytics is said to be the big news in the market.

Big Data analytics helps in addressing the 3 basic needs of any data – Volume, variety and velocity. The NoSQL databases have an efficient architecture much advanced over typical RDBMS, which provides solutions for storing this huge amount of data. Variety is a concern when the data obtained is from different sections of industry. The datasets encompass different factors that are independent and defined as per the sections of industry. Hence, there is a requirement to propose stratified solution to manage this diverse data. Big Data Analytics provide the feasible solution over the same. And when velocity is concerned, the highly stratified system works independently for every industry section, thus able to grant higher access speeds along with capacity to handle multiuser needs.

With the rise of internet and social media platforms, unstructured data sources have been commonly used. It has been reported that 84% of individuals are analyzing and processing unstructured data which hails from weblogs, social media, email, photos and videos. This data is sometimes in batch or sometimes real time. IT managers face the need to source out these both categories of data equally. However with time, the need to manage real time data is increasing. The prevalent IT infrastructure cannot handle the rising needs without turning cost sensitive. Hence, IT managers feel the need to choose Big Data Analytics.

Analytics of typical databases involved monotonous querying while NoSQL databases like Hadoop do not employ standard SQL querying techniques, allowing versatile queries to be used to gain probabilistic results. This gives an edge for Big Data Analytics to be used.

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4 best ways to increase company’s value using Big Data

A decade back, companies have been engaging in collecting data from daily transactions and storing them in databases. The prime aim was to keep track of records or make some necessary forecasts. However, with the internet revolution, the amounts of data and its sources have risen to a drastic level. For instance, Weblogs are available to collect history of individual customer interactions. Similarly, marketing people constantly gather information about what people of their endorsed brands. Though it sounds conceptually easy, an entire array of new processes, technology and governing mechanisms are needed, which are together called as Big Data.

Following are the 4 broadly implemented strategies to increase value of company using Big Data-

1>    Management of performance

Performance management typically involves proper understanding of the meaning of ‘Big Data’ in databases of company. This is done by using predetermined queries and multi-dimensional analysis. Data used for analysis is transactional in nature. A number of business intelligence tools are available under big data analytics for providing different types of reports and graphs. Thus, the performance of individual executive can be assessed and necessary training can be provided to improve their skill sets.

2>    Exploration of Data

Data exploration implies heavy use of statistics to experiment over data and discover valuable insights. This helps in answering many unanswered questions of managers and grants them a competitive edge.  For instance, Cluster Analysis is used to group customers based on purchase history in order to determine attributes, which may not have been visible to Analysts before. This aids the business in attracting prospect clients and thus sees a positive enhancement in the sales.

3>    Social Analytics

This happens to be the unexplored section of data which has gained importance recently. Data is collected from popular social media platforms like Facebook, Twitter, etc in context to awareness, engagement and spoken words reach. Based on this social metrics, managers are able to draw conclusions about the success of digital marketing and advertising campaigns. Big Data Analytics report in this realm also helps in unraveling the unexploited opportunities which can be focused upon.

4>    Science of Decision making

Decision science specifically involves processing of non-transactional data involving customer reviews, ideas and product feedbacks. This unstructured information is best sorted using the Big Data Analytics and suitable experiments can be carried over it. Thus, provisions are provided to source and poll ideas from various sources and draw decisions based on them. Thus, Big Data Analytics is a true benefactor of Decision making process. 

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Do you know how Predictive analytics helps you to improve your business?

Nowadays many big companies moving towards the approach of Predictive analytics, but still many of us still unknown with this term.  Some of the question arrives like what is Predictive analytics? And how exactly it’s helpful to improve your business?

Now the big question is what is Predictive Analytics? 

Predictive analytics encompasses a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future.

By using some of the different tools you can find out statistics and according to that you can make your future business strategies. By analyzing or outlining big data you can improve your knowledge about your business, competitors and mainly the customers. It helps you to reduce risk and make better decision which lead to better services to customers.  

How to implement Predictive analytics?

To improve your business with the predictive analytics you need to consider some of the important steps.

1.       Business Goal: For a successful predictive analytics project you need to be clear with your business goal. If you are clear with your business goal and implementing predictive analytics successfully it helps to increase in revenue. And for better business strategy lineup your goal should need to be clear.  

2.       Understanding and Preparing Data:  For a successful predictive analytics data plays a key role. So you need to understand data from variety of different sources. Advanced data visualization tools help you to understand different sources data including social media, government data, and other public sources. The next important challenge in predictive analytics is data preparation. You need to be very careful while preparing data because raw data is often unsuitable for predictive analytics.

3.       Creating and deploying predictive model: predictive analytics modeling tools run the analyst algorithm for best data analyst. Predictive analytics modeling tools runs to analyze data by that analysis you can get the predictive model. Once you complete with the creation of predictive model the next important steps come is evaluation of model. You need to test data set for effective predictive model. It runs continuous algorithm until they find the best predictive. And at the end the most important steps come i.e. deployment of predictive model in which the deployed  model comprise the logic to implement predictive rules and provide appropriate formulas and method to get appropriate data the model exactly need to return the result. 

4.       Monitor effectiveness of predictive model: for a better and continuous result you need to monitor effectiveness of predictive model. To stay on top you need to continuous with the predictive analyst including business goal, understanding and preparing new data, creating and deploying new predictive model  as per the new analyze data and continue with the monitor process.

So for better business strategies and increase the revenue you need to implement effective Predictive analytics.

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Big data analytics

Big data analytics can be referred as the process of examining and analyzing a big amount of data in a way that will expose unusual information, hidden patterns and correlations. The term ‘big data’ is used to describe the exponential escalation, availability as well as use of information, both structured and unstructured.  In simple words, big data analytics is creating useful information for the business by processing and analyzing the huge amount of data available.

This data can be useful to gain advantages over competitors and result in business benefits, if examined and analyzed professionally. Outcome of big data analytics can be used to prepare more effective marketing strategies, resulting in increased revenue.

Big data analytics helps organizations to take business decisions which are based on through research and accurate analysis. In big data analytics, data scientists analyze the data sources that may be left untapped by the traditional business research programs.

The big data may include anything right from the social activity reports to mobile phone call details, and web server logs to internet clickstream data. To analyze structured data, there are many software programs available in the market. However, these software programs cannot process unstructured data. Therefore, nowadays, many big data analytics environments use a new class of big data technology.

As name suggests, big data is a huge amount of data which comes from various sources. Therefore, it becomes necessary to link, match, cleanse and correlate data. In order to produce high quality information that is relevant, authentic and up-to-date, it is necessary to systematically integrate structured and unstructured data assets.

Acquiring large amount of data is just half of the story. What most important is processing and analyzing the data and utilize it in a way that will be beneficial for the business. The organization should use the data to run the organization more efficiently and make its position stronger in the market. Big data should be segregated and assessed to find the most valuable information from it.

It is important to note that the entire data will not be useful for the business. An organization should process the data through big data analytics and find out the most relevant and useful information for the business practices. In last few years, companies have understood the importance of nontraditional data sources. Over the period of time, the cost associated with storage and computing data has also lowered considerably. Therefore, more and more companies are including non-traditional yet potentially very useful data in their data banks and using it while preparing business strategies.

With the help of big data analytics, organization can develop a more thorough and impactful business strategies, which can result in enhanced productivity, a stronger position in the market and greater revenues.

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