How to Address the Big Data Challenges like a Pro

Data is an indispensable part of running a business today. With massive amounts of data being produced every second from stakeholders, customer logs, sales figures, and business transactions, data forms the fuel driving businesses. All this data gets accumulated in Big Data, which is a huge data set. Businesses need to analyze this data to enhance their decision making.?

However, leveraging big data comes with its own set of challenges. And it’s wise for decision makers to be aware of these big data challenges. In this post, our big data consultants outline the chief big data challenges and their solutions.

Addressing the Big Data Challenges

Challenge 1: Lack of Understanding and Acceptance of Big Data

Often, companies lack the knowledge of even the basics, like what are the benefits of big data, what infrastructure is required, and more. Without a proper understanding, a big data adoption project runs the risk of taking the path of failure. Businesses may waste a lot of resources and time on things they don’t know how to use.?

Moreover, if the employees don’t realize the value of big data and/or are unwilling to change the processes so as to adopt big data, they can resist it, hindering the company’s progress.?

It’s not enough for people at all levels of a company to understand and accept big data. It’s important for the right department in an organization to leverage big data, to actually reap its benefits. For example, if a company needs to recruit new employees and is conducting video interviews of candidates, the HR department of the company is the one to decide whom to hire. Here, the HR department needs to use big data to drive their decision-making. Only then, the company can actually derive the value of big data.?

Solution:

As big data is a gigantic change for a company, it should first be accepted by the top management and then by other employees. To make sure that big data is understood and accepted at all levels, IT departments need to conduct several workshops and training.?

For even more big data acceptance, you need to monitor and control the use and implementation of the new big data solution.?

Challenge 2: Shortage of Data Scientists

The thinking of data scientists and that of business leaders are on the same page quite rarely. Analysts, just starting their careers, always deviate from business data’s real value. As a result they end up with insights that are unable to solve the problems at hand.?

Moreover, there are a limited number of data scientists that have the capability to deliver value.?

Although all professionals in the field of big data are compensated quite well, companies still find it difficult to retain top talents. Also, it’s extremely expensive to train entry-level technicians.

Besides, the scarcity of data scientists in general, there’s a scarcity of data scientists with proper domain knowledge and expertise.?

Solution:?

Even if data scientists are available, companies need to ensure they are equipped with knowledge of data science specific to their industry, and not just the knowledge of data science in general.?

To address this challenge, many organizations have resorted to self-service analysis solutions, which leverage automation, AI, and machine learning for extracting meaning from data, by using minimal manual coding.?

Instead of settling for under-skilled employees and compromising, you can consider seeking the help of tech companies with expertise in data science and data analytics. There are many such companies that offer quality data analytics services at affordable rates. Hiring such a company can help you address the challenge of shortage of data scientists in your organization.?

Challenge 3: Data Growth Problems

One of the biggest challenges of big data is storing all the huge sets of data properly. There’s a rapid increase in the quantity of data being stored in databases and data centers of companies. As the data sets exponentially grow with time, handling them becomes extremely difficult.?

Most of the data is unstructured and is received from text files, audios, videos, and documents. This implies that they can’t be found in databases. Besides, businesses receive data from a variety of sources like customer purchases, staff payments, and social media trends, and devices like sensors, smartphones, and IoT devices. Each source consists of a massive amount of data. This can lead to major big data analytics problems, and you must resolve them as soon as possible, or it can hinder your company’s growth.?

Solution:

For handling these large data sets, businesses are going for modern techniques, like compression, deduplication, and tiering. Compression is used to reduce the number of bits in data, thereby lowering its overall size. Deduplication involves removing unwanted and duplicate data from a data set.

Data tiering lets companies store data in multiple storage tiers. It makes sure that the data is kept in the most suitable storage space. Data tiers can be private cloud, public cloud, and flash storage, based on the size and importance of data.

Challenge 4: Drawing Real-time Insights

Data sets contain a lot of valuable insights. However, they are of no value if you can’t draw any real-time insights from them. Real-time data and insights are especially necessary for certain businesses like cab services and large retail stores. Cab services may need real-time data on number of customers booking cabs at different times of the day, current road congestion status, etc. Large retail store owners may need real-time information on how their customers are feeling while they are in the store, which might be directly related to their shopping experience.?

The core idea is generating actionable insights from data sets to increase the efficiency of result-oriented tasks, like:

• Setting up new avenue for disruption and innovation
• Quickening the service deployment process
• Lowering costs through operational cost efficiencies
• New service offerings and product launches
• Encouraging a culture driven by data?

Solution:

One of the challenges of big data is generating timely insights and reports. To attain this, companies are investing in ETL and analytics tools that have real-time capabilities, for having a level playing field with their competitors.?

Besides, the use of AI can also help businesses that involve working with real-time data. For example, a taxi mobile app integrated with AI has the ability to optimize routes to enable drivers to drop passengers to their destination in the shortest time possible.??

In retail stores, AI has the ability to detect the mood of customers while they are shopping. Walmart employs a facial recognition system for this job. They have installed cameras at each checkout lane. When the cameras find a customer to be annoyed, a representative of the shop will talk to him or her. Tracking the mood of customers can help create stronger relationships with them.?

Challenge 5: Paying Lots of Money

Big data adoption projects are quite expensive. In case you choose an on-premises solution, there will be costs for new hardware, electricity, and new hires (developers and administrators), and more. Moreover, although the necessary frameworks are open-source, you need to incur the costs of the development, setup, maintenance, and configuration of new software.?

If you opt for a cloud-based big data solution, it’s still necessary to hire employees (as above), pay for cloud services, development of the big data solution, and setup and maintenance of necessary frameworks.?

Furthermore, in both the cases, it’s important to allow for expansions in the future so that big data growth doesn’t get out of hand and costs you a huge amount of money.?

Solution:

Minimizing your company’s expenses depends on the business goals and specific technological needs of your company. For example, a cloud-based solution is beneficial for companies looking for flexibility. On the other hand, companies having extremely strong security requirements opt for on-premises.?

Hybrid solutions are also there where some part of the data is stored and processed in the cloud while the remaining part is stored and processed on-premises. This option can also be cost efficient. You can also save money by leveraging algorithm optimizations or data lakes.?

• Optimized algorithms can lower computing power consumption by 5 to 100 times, or more.
• Data lakes can offer cost-effective storage solutions for data that doesn’t need to be analyzed at the moment. .

To solve this challenge, you need to analyze your needs properly and choose a corresponding course of action. Big data experts can guide you on which option will be the most suitable for your business.?

Wrapping Up

Applying these solutions can help businesses navigate the tricky waters of big data. Nevertheless, if you truly want to reap the benefits of big data for your business, it’s wise to have a specialist in this field by your side.?

aQb Solutions offers expert big data and data analytics solutions that can fuel your business growth. If you are seeking assistance in leveraging big data, we’ll be happy to talk to you!?

Tackling the Big Data Challenges

Challenge: Lack of Understanding and Acceptance of Big Data

Solution:?

• Acceptance by the top management first
• Workshops and training for all
• Use of big data by the right people

Challenge: Shortage of Data Scientists

Solution:

• Using automation, AI, and machine learning to get meaning from data
• Hiring data scientists with industry-specific knowledge
• Hiring data science and analytics service companies

Challenge: Handling Growing Data Sets

Solution:

• Using techniques like compression, tiering, and deduplication

Challenge: Drawing Real-Time Insights?

Solution:

• Investing in ETL and analytics tools with real-time capabilities

Challenge: Huge Expenditure

Solution:

• Analyzing your needs and choosing among on-premises and cloud-based big data solutions
• Hybrid solutions can be cost-effective
• Using data lakes or algorithm optimizations can save money
• Seeking big data experts’ help to choose the right option