Big Data: Definition, Challenges, Processing Strategies, and Case Studies

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Advances in technology provide great benefits and opportunities for companies in terms of big data. What exactly is big data?

Big data is a collection of information in different formats and evolves over time. There are five principles of big data, namely volume, velocity, variety, veracity, and value. Volume is the scale of information obtained, velocity is the speed of collecting the data, variety is the diversity of types of data retrieved, veracity is the level of accuracy & validity of the data held, and finally value, the value of the data owned and generated.

Big data solutions can help improve consumer experience, identify and solve problems within a company, and many more benefits.

It was recorded that in 2020, big data production in the world increased 44 times higher than in 2019. This was also influenced by the emergence of big data companies that provide services from upstream to downstream.

Apart from having a positive impact on the companies that use it, this big data analytics software also creates new problems considering that the data that is processed is very, very large.

What are the real challenges in processing big data?

  1. Difficulty finding the data that is really needed

Through big data, you can find out what data is. Starting from consumer behavior, web visitors, conversions, financial data, market trends and so on. All this data is very important but becomes less effective if you are looking at data that is too big without knowing which ones are relevant for your company? Which should be observed and analyzed by big data analytics software first?

The problem with big data analytics arises when large amounts of data are presented in front of you in an unstructured and unfiltered manner.

 

  1. Retrieving invalid data

In simple terms, big data analytics can be interpreted as the process of analyzing a set of data with various specified metrics. Again, because the amount of information presented can be very large and may have been for years – you may choose the wrong information so that the data you use is invalid.

This often happens if you collect big data from many sources at once and the lifecycle of this big data gets mixed up with one another, coupled with different formats. Non-standard data collection results in inaccurate report results. We cannot make decisions based on the results of data analysis that are less relevant.

  1. Big data is stored in different databases

Since big data solutions use large amounts of storage, it is possible that this data is stored in different places. If teams in your company access from different databases, there is a very big risk of misinterpretation because they see different portions of data. Without full access to one place, it is very difficult to generate accurate analysis reports with existing big data analytics software.

  1. Little attention is paid to big data security

With so much data stored, it is very likely that it will be misused by irresponsible persons by hacking and stealing it.

 

As the amount of data grows, companies will use new tools and other software to integrate into the database. This increases the high risk of hacking. Other risks related to data security include, among other things, unsafe sources, so data is not protected.

 

  1. Lack of experts who can analyze big data

Technology and tools for performing big data analytics have developed rapidly but are not accompanied by experts who can operate this technology properly. Starting from collecting, organizing, processing it with big data analytics software and creating reports that can be used to determine subsequent company policies.

 

Then how to create an efficient strategy in processing big data?

Big data companies must be able to handle data from upstream to downstream. Where does it come from and what is the process for providing this information to relevant parties so that each team gets the right data for them. Before starting to use big data analytics software, it’s a good idea to review a few things below.

 

  1. Double-check your company’s data management process

Starting from CRM software, tools used for marketing as well as social media. Some of the software integrated into your company may have existed before you joined the company. That means these tools need to be evaluated whether they are still useful or need updating.

 

  1. Hold training for your team

If your team still doesn’t have members who understand how to process and generate reports from big data, try to provide training to your team according to the tools used in the company. This training can be done with workshops and special training so that each member of your team at least understands how to sort and process information.

  1. Integrate data

One way to maximize the function of big data analytics software is to integrate it internally or custom according to the business solutions you offer. The most sophisticated software in the world will not be effective if its function cannot be integrated with other tools that you use in the company.

SAs an end-to-end big data platform service provider, it can help your company store, process and analyze big data. Both small and large scale companies.

Don’t have qualified infrastructure or human resources to process big data platforms.

 

Case study

Financial Industry

Currently, many financial applications have emerged that help users save, pay, and plan their future. When almost all customers interact virtually, it will be difficult to determine consumer-based services without knowing consumer profiles and behavior. In addition, the financial sector is very vulnerable to internet fraud and crime, so it requires a layered security system.

Turn your financial company into a data-driven company so you can better understand today’s consumer needs through artificial intelligence (AI). Use big data analytics to maintain each of your customers and identify their behavior towards your products in real time in order to improve relationships with them.

With a big data system, you can implement network security solutions so that consumers are comfortable and feel safe when interacting with your product or service. To know more about how big data can help the financial industry in solving various problems.

 

Retail Industry

Have you ever asked, why does someone love a brand so much that they don’t want to buy another brand even though the brand is relatively expensive?

Today consumers are able to define the success of a brand. For this reason, data analysis related to consumers can make you win their heart. You can make personal service more confident because you can find out consumer behavior and sentiment towards your products and services.

With big data, you can predict whether a product will behave or not by considering consumer preferences. By changing the right prices and promotions, you can beat the competition more effectively. Want to understand more about how big data contributes to the retail industry.

 

Manufacturing Industry

The manufacturing sector requires monitoring assets that can identify damaged machines and maintain the quality of factory inventory.

With big data analytics, manufacturers are more connected to the network which makes performance more efficient and productive. With the ability to predict and avoid equipment breakdowns, you can minimize down time in your factory operations.

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