How Big Data Sources Contribute to Big Progress in Your Business
It is hard to underestimate the importance of data, especially when it comes to business. Big Data sources allow companies to radically change the results of their activities. They open up new opportunities for planning advertising strategy more efficiently and increase sales. In this article, we decided to clear up some benefits of Big Data and discover what spheres this technology is successfully applied to.
What is Big Data?
Answering the question what is Big Data, we may say that it is a set of specialized tools and methods that allow someone to convert a huge scope of technical information into comprehensible statistics with a variety of parameters that could be used to optimize business-related processes.
Big Data may be useful for:
- Companies that need a variety of technical information to be converted into business statistics
- Companies that want analytical tools to be integrated into everyday activities
- Industrial enterprises that need to gather and analyze data about their equipment
Talking about the importance of Big Data in marketing, we should say that it allows users to find out better ways to improve customer relationships, choose a strategy for achieving increased conversion rates, as well as optimize customer's lifetime value.
For example, this technology is applied by a well-known service called Airbnb. Thus, while browsing the website, the system will show you a landlord's Facebook friends that you are also acquainted with.
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Netflix also applies a variety of Big Data analysis techniques. The company has developed an algorithm that allows it to form qualitative recommendations of the content. Moreover, based on this information, the company creates it's own competitive content.
One more interesting example of advanced data analytics is Progressive Insurance company that analyzes the customers' credit rating and compares it with their own data. This way, they can predict the probability of an insurance events occurring.
How to conduct Big Data predictive analytics?
Receiving information is not enough. It is much more important to estimate this data properly.
Since the stream of information produced by Big Data is really huge - not all information is valuable. Thus, you have to structure all the information. Let's consider the different ways of processing statistics that we have received with the help of Big Data.
This process is especially important in case you are getting ready for your app development or create any other product. The Big Data predictive analytics may be collected from dozens of sources, and everyone of them is able to provide you with a unique information.
- GPS sensors serve for getting geolocations data
- A smartphone's accelerometer is used by some insurance companies to learn the average speed of the client's driving
- Wi-Fi signal is a great way for retailers to learn the number of customers
- Social networks such as Facebook, Twitter, and LinkedIn are great sources for getting required information
- Transaction data or other information related to a company's finances
The ways of data collection
It is worth singling out that one of Big Data analytics solutions called Import.io. It is able to transform an ordinary website into a structured data-reading machine. The service focused on converting the page into a spreadsheet that may be visualized, analyzed and used while making a decision.
The data that's received initially should be processed and structured for analysis. For example, the information may be organized in a table or put into static software to pave the way for further analysis.
However, after the processing and structuration, your data may be incomplete, duplicated or contain some errors. For that purpose, there are several types of data cleansing depending on the kind of information, be it phone numbers, emails or something else.
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The open-source data analytics app called OpenRefine is ready to give you a hand in data cleansing. It allows you to research huge arrays of information fast and easy.
Analysis of received data
After the data is cleaned, it is high time to start your analysis. There are lots of approaches about how to analyze data arrays that could be conducted with the help of Big Data applications like MapReduce, 1010Data etc.
Concerning MapReduce, it is an algorithm developed by Google that simultaneously processes a big scope of unstructured data. It consists of two functions: Map and Reduce. Map function splits all the incoming information into small pieces that may be analyzed independently from each other. When information is split - the Reduce function starts working and analyzes every single piece to gather them all together. That is how the hidden patterns are found in with the help of Big Data analytics technologies.
Types of Big Data analysis
We have prepared examples for you about the types of Big Data analytics, as well as the examples of their realization methods.
This type is generally used for data selection, reporting, simple visualization, and basic monitoring. This way, it is a suitable type of analytics for making simple Business Intelligence reports. There are a variety of open-source projects that allow you to get the statistics for making reports easily.
This type is used for predictive modeling and the compilation of extended statistics. Advanced analytics use 'smart' data aggregation to get the required information related to the dynamic changes influencing the work efficiency. Also, it is needed to get data for optimization and modeling. All these may be implemented with the help of a Data mining approach allowing you to identify the most appropriate customer category to promote a certain service or product, predict the behavioral model of consumers, get real time Big Data and much more.
This type of analytics serves as a part of a business process and contains the set of approaches intended to predict the behavior of consumers within a certain market segment.
This type is used for income management. It is appropriate for advanced data analytics since it uses a set of numerical methods to redesign complicated systems and processes in order to achieve the improvement of one or several indicators.
Types of Big Data analytics
How to choose applications of Big Data analytics?
Such companies as Microsoft, Google, Amazon, IBM etc. are ready to provide you with ready-made applications of Big Data analytics. The matter of choice depends on the flexibility of the system you need as well as the resources you are ready to spend on it's adjustment and support.
For example, in Microsoft Azure you can find all the necessary tools and even more. Besides, to apply these tools within your startup you do not have to purchase expensive software or hardware since they are available in a service-as-a-product form. They are ready to work as soon as you connect them to your cloud.
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Moreover, to gather information from different Big Data sources, your web solution or application can be built using any programming language, tool or framework. It also can be integrated with any cloud service.
Underneath you can see the rating of some top ready-made solutions.
Ready-made Big Data platforms (Source: SelectHub)
After the data is analyzed, you may visualize the final results in a view of informational displays, diagrams, tables etc. For that purpose, Big Data applications like Plotly or DataHero can be applied.
What opportunities does Big Data provide you with?
Creation of new or improvement of existing products. In general, Big Data consists of information collected from multiple channels that in turn help you to understand customers' needs, both current and future.
Customers' detailed personalization. Target marketing claims that understanding customers preferences may improve the user experience.
Quality improvement in customer service. Having detailed customer information makes it possible to solve some problems connected with interaction faster and in more efficient ways. Also, analyzing the product's refusal and return rate, you may conclude what went wrong and how to improve it.
Self-service improvement. Using Big Data analytics technologies it is possible to find out what aspects of self-service your customers are interested in. Besides, the development of self-services may not only lead to better product interaction but also to decreased expenses on physical services.
For instance, different online banking services analyze cases when users prefer referring to a call-centre over doing self-service. In turn, banks are able to understand what difficulties it's are users faced with while doing certain actions and make the service better by fixing these issues.
Problems you may face within Big Data
According to CA Technologies, a lot of companies using this technology are faced with some difficulties when it comes to projects requiring Big Data programming. The most serious obstacles are undeveloped infrastructure and challenges connected with new approaches to data gathering. That is why it is important to conduct advanced testing of the product and interaction with it.
Another problem is actually the huge scope of data that is not always valuable for the company. For example, the list of leads along with the net worth of goods is much more informative than the data concerning users' clicks within your online resource.
The benefits of Big Data show that this technology is definitely worth your attention. However, to use the full power, you have to invest money not only in it's integration and adjustment but change the management approaches as well as debug business processes.
So, Big Data helps companies get additional profit as well as cut on different expenses. Also, it contributes to generating leads, along with the attraction of more of target audience. It is worth mentioning that the creation of new methods related to customers' loyalty increasing is getting easier with this technology since you are able to better understand all the needs of your audience.
If you want to develop your own custom Big Data solution - our team will be glad to help you. Contact us to get your free consultation.