Last Updated on November 24, 2020 by Filip Poutintsev
The term “big data” refers to the recording, storage, and processing of information related technologies. Big data does not necessarily define data sets and databases: it refers to knowledge processing systems, methods, and tools.
Table of Contents
- Pros and Cons of Big Data
- Pros of Big Data
- Cons of Big Data
Pros and Cons of Big Data
Data is also gathered through any sort of mechanism that produces data in the first place, including social media platforms, service networks, and public records, search engines, mobile phones, connected devices such as smart televisions, and any other source of information that businesses are able to access.
Upon collection, large data sets may be placed for further analysis and processing in a semi-structured, structured, or unstructured database. Usually, data is collected and analyzed at regular intervals, but services of real-time data analytics allow the continuous collection and analysis.
The revolutionary nature of a real-time data processing system enables users to be given immediate feedback without having to wait for further study.
Today almost every company at the enterprise level makes use of big data. With this information, the tools available generate a gold mine with possible benefits to use.
There are also some big obstacles to consider that could mitigate some of the benefits that small- to medium-sized companies might make, which is why an individualized look at this asset is important for each organization.
Pros of Big Data
1. Better Decision Making
Most businesses are primarily aimed at improving their decision-making by investing in big data. As more detail is accessible in a functional way, it is easier to see what consumers want or do not.
Data-driven insights allow smaller businesses to compete or expand, while bigger companies can use this information to keep up with various trends and behaviours. That doesn’t mean this advantage is a guarantee of success, but it can get the debate started on the right foot.
2. Reduce Costs
Both the surveys conducted by Sync Sort and New Vantage showed that big data analytics helped businesses slash their expenses. Nearly six out of ten (59.4 percent) respondents told Sync Sort Big Data Tools had helped them improve operational efficiency and cut costs, and about two-thirds (66.7 percent) of New Vantage survey respondents said they had started using Big Data to reduce expenses.
Ironically, however, just 13.0 percent of respondents selected cost savings as their primary goal for big data analytics, indicating that this is merely a convenient side-benefit for others.
3. Detects Fraud
Another important advantage companies find with big data is that it can help identify fraud. That benefit is one that the financial services industry most often mentions, but any company can take advantage of those opportunities. AI and machine learning will detect anomalies or transaction patterns for individual accounts that aren’t part of the daily routine.
This skill offers credit card companies, banks, credit unions, and several other retailers the option of detecting stolen identity materials, account information, or product access to avoid losses. That benefit is so profound from a financial services perspective that the identification always takes place before the consumer really realizes something is wrong.
4. Customer Service
One of its most-cited goals of a big data implementation effort is to improve the interactions between customers. AI, machine learning, and similar systems can analyze CRM systems, social media, and email interactions information to provide a wealth of information about how people really feel and think. Accessing the information from data collection processes enables serving consumers when anything unexpected occurs.
Another common benefit of big data is innovation, and the New Vantage survey found that 11.6 per cent of executives invest in analytics primarily as a means of innovating and disrupting their markets. They argue that if they can glean insights that their competitors don’t have, with new products and services they may be able to get ahead of the rest of the market.
6. Builds Trust
For several companies, the risk of a potential security threat often overshadows the idea of implementing big data. Big data provides more security by administering new techniques to protect customer privacy through the implementation of analytics, which can build trust and loyalty with potential customers.
Because of this process, organizations can identify small irregularities in any account, building relationships that could eventually lead to repetitive purchases and relatively higher revenues.
7. Safety of Information
The criteria for big data include the need to implement immediate real-time verification anytime someone accesses the information. Using these strategies with the company’s program makes it easier to protect the other data inside the organization.
To achieve this advantage, organizations must follow their protocols to the letter, but it allows an investigation into any threat that may be present and its eventual eradication.
Analysis of the data in real-time allows you to spot anomalies in expected patterns almost instantly. This allows you to identify and, in fact, fix any problems that may have occurred, resulting in better customer experience.
Moreover, such an analysis helps to spot fraudulent behaviour and breaches of security. This allows you to take the necessary measures in a timely manner, which helps prevent major security breaches that might have occurred otherwise.
9. Increases Productivity
Big data tools enable analysts to take a quick look at more information. That means they’re increasing their personal productivity levels, creating a tide that lifts all boats. This advantage also gives individuals more information about themselves so that they can recognize areas where they could be more productive in their activities. That’s why investing in this technology often leads to a slow rise in results starting from the bottom-up.
Cons of Big Data
1. Questionable Data Quality
A significant drawback to consider when using big data as an asset is the quality of the information the organization collects. Analysts and data scientists must ensure the accuracy of what they receive before any of the info becomes usable for analytics. They will then need to determine the relevance of each data lake and correctly format it for review. These required tasks can significantly slow the reporting steps.
It also creates an issue in which the insights gleaned from analytics may be worthless. It can also be detrimental if it is acted upon in other circumstances. That means that the investment might not if ever, generate returns for several years.
2. Security Risks
Almost all of the information businesses gather in a data lake includes sensitive information that requires a specific level of protection. Accessing such analytics can make an organization an attractive target for a potential cyber-attack. A data breach is often the single biggest threat a company faces when it attempts to create that culture.
Preventing a data breach begins by only keeping the information that you need. Reducing the volume of what you collect will stop some cybercriminals from paying attention. Then lock away physical records and destroy them before disposal to ensure that your company stays in compliance.
3. Lack of Talent
Big data analytics is not an asset which can be looked at by average IT staff to gather useful information for decision making. Companies need information scientists from this approach who know how to glean results. That makes that position one of the highest-paid IT areas available right now around the world.
Many small and medium enterprises cannot afford this cost, which means that they are forced to adopt systems with the internal talent they possess or rely on outsourcing.
Unless you have no expertise at your disposal or workers who understand this knowledge, then building a data lake is an almost pointless effort. Hiring or preparing the right people to optimize the processes will take quite some time.
4. Need for Cultural Change
Many companies who want to adopt the big data concept try to shift the culture internally so that the entire company continues to see the benefits of using analytics.
The amount of investment necessary to get this process going means a small reporting benefit would not be a good enough outcome. Nearly 99 per cent of executives said their companies were in the process of building a new culture for their teams in the NewVantage survey regarding the pros and cons of big data, but only one-third of them had success.
A mistrust of AI and machine learning is what makes the transition to big data so overwhelming to the average citizen. Any work providing repetitive tasks is one which could be replaced in the near future by computers.
5. Compliance Issues
Compliance with government legislation is another thorny problem for major analytics efforts. Some of the information found in big data stores of businesses is confidential or personal, meaning that when processing and storing the data, the organization will need to ensure that they follow business expectations or regulatory requirements.
Information governance, including arbitration, was the third-most important barrier to working with big data in the Syncsort study. Indeed, when respondents were asked to rate big data challenges on a scale from 1 to 5, this big data disadvantage received more 1s than any other.
6. Hardware Needs
Another significant problem for organizations wanting to accept big data is the need to develop the appropriate level of IT infrastructure. Analytics efforts won’t be as successful or useful if the system’s foundation is weak, lacks storage space, or provides inadequate security.
Networking capacity is another factor to study, and then the computer systems and servers may need to be installed. Much of this downside can be overcome when cloud-based analytics is a priority, but even this alternative will not eradicate future infrastructure problems. The smallest businesses tend to face this disadvantage more often.
7. Cost of Implementation
Many of the big data resources available today depend solely on open source technologies. This fact ensures that tech costs are practically gone from this attempt to collect information, but it also poses a problem with hardware, repair, and staffing issues.
Most of the projects in this group go beyond their allocated budget as the software’s cost ratio is required from a labour perspective, but it just doesn’t happen. Deploying new IT administrators typically takes more time than people initially expect.