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    Top Challenges Related to Big Data and How to Overcome Them

    big data challenges and solutions

    In the age of "big data”, enterprises can take advantage of the insight to identify and solve issues within an organization, understand customer lifecycle, improve marketing and development strategies, and more. However, it also comes with a large share of challenges. 

    The amount of data created daily continues to increase; in fact, data creation in 2020 was 44 times more than in 2009. Consequently, organizations have more information than ever to analyze before coming up with the final decision. To generate the most effective results, the vast amount of data must be recorded, managed, secured, and enriched properly. As there are many aspects to consider, there may be problems occur. Hence, in this article, we will discuss some main challenges related to big data and solutions for businesses to overcome them.

    1. Data Growth Issues

    It’s unsurprising that data is growing at an exponential rate with time. As one of the largest super financial application platforms in Vietnam, Momo currently has over 31 million domestic users with 2 PB+ of data. Even more, the number of users and data volume are anticipated to double over the next two years, putting the company under the pressure of data overload. 

    Handling such vast amounts of information appropriately is a pressing challenge that many enterprises are now facing. Since there are thousands of new users registered and millions of financial activities happening every day, it is getting more difficult for financial service providers to store and analyze those data effectively. 

    Moreover, while the proportion of potentially valuable data is rising, there is still an abundance of irrelevant/unqualified data to sort through. Enterprises will need to analyze the tremendous amount of useful data to target prospective and existing customers more precisely, provide personalized offers, improve customer experience or boost engagement. 

    Enterprises often need real-time insights to make up-to-date decisions. With enormous real-time data, it’s challenging to sift through it and find the most crucial insights. When staff is overwhelmed, they may not thoroughly analyze data or just merely concentrate on the easiest-to-collect information rather than those that genuinely create value. 


    Large businesses can consider modern techniques and tools to handle these large data sets. By compressing data, companies can reduce the number of data bits, while deduplication can be employed to remove duplicate information from a data set. Besides, storing and managing information in the cloud, on-prem, as well as in hybrid environments also help enterprises decrease space consumption and cut storage costs. They can also use tools and data analytics software such as Hadoop, NoSQL, and many more to deal with this problem.

    Besides, to analyze databases in a scalable and flexible manner, organizations can consider solutions that support innovative in-database machine learning. In Momo’s case, it cooperates with Vertica - Big Data Analytics Platform to run its analytical workloads flexibly on-premises, in the clouds, as well as in hybrid environments. 

    Moreover, Vertica’s Unified Analytics Platform also helps the corporation derive immediately actionable insights and, from that, improve its development and marketing strategies. In Vietnam, KMS Solutions is the trusted data analytics service provider that is recognized by technology partners such as Vertica, Mixpanel, and GoodData for its data analytics capabilities.


    2. Changes in the Regulatory Environment

    New regulations also force financial institutions to concentrate more on ‘data-driven supervision’ capabilities and have stringent data privacy policies. Personal data privacy is a concern of paramount importance to any enterprise, especially for those operating in the banking and financial services sector. Regarding this issue, different countries may enact various regulations, such as:

    • The Personal Data Protection Act (“PDPA”) of Singapore requires businesses to comply with a wide range of data protection obligations if they conduct operations involving the acquisition, use, or disclosure of personal data.
    • The EU’s General Data Protection Regulation (“GDPR”) 34 indicates that the controller should implement appropriate technical and protection measures to render personal data unintelligible to people that are not authorized to access it.

    Even if a company considers itself to be compliant, regulations are continually changing, necessitating the implementation of new data privacy efforts.  Thus, maintaining compliance with the new law’s introduction or update is not an easy task.


    To keep up with data privacy requirements, enterprises should continue to be vigilant about the data they collect, store, and analyze and take steps to limit their risk of regulatory action.

    What corporations should do first is perceive privacy regulations as an opportunity. The Clean Air Act is a prominent example of any businesses that have adapted early would be well ahead of the game. Thus, large organizations that leverage data science for competitive advantage can have a higher possibility of success, especially in today’s data-driven world. 

    For instance, corporations that want to comply with the GDPR 34 can think about data encryption. By applying Voltage Format-Preserving Encryption (FPE) provided by Vertica, they can secure analytics without sacrificing usability and drive digital transformation simultaneously.

    Besides, creating a data-related compliance program is worth considering. Several businesses have already used data security frameworks as the foundation for their IT security program. Considering the ever-changing regulations, here are some key considerations for planning the program: 

    • A list of all the personal information in your environment.
    • Your process of collecting, storing, analyzing, ad sharing the information.
    • The gaps between current processes and applicable regulatory requirements.

    3. Loose Data Security & Protection and Changes in Customer Attitudes

    In light of the recent Facebook and Equifax data breaches, data security has become a growing concern, more so now than ever before. Enterprises need to ensure that their systems are steadfast and impervious. This is especially true for banks and financial institutions when the information is highly confidential. As a common target of cyberattacks, businesses in the BFSI sector should take extensive steps to safeguard customer and financial data. Data leaks can severely impact customers and undermine their trust in the companies.

    Besides, nowadays, users have become increasingly savvy about data collection and less trusting of corporations. According to the survey by cybersecurity company RSA, 73% of customers now have a greater awareness of data protection than they had five years ago. Therefore, neglecting to protect customer data can have severe business consequences such as fines from regulators, productivity disruption, loss of customer loyalty, reputation damage, or even turning them into a vulnerable target for later cybercrimes.


    To safeguard essential information, it’s necessary to encompass every aspect of information security, including the physical safety of the company’s hardware and storage devices, the storage environment, administrative and access controls, and the software’s logical security.

    Some possible ways to overcome these challenges are:

    • Changing corporate culture

    The company can revise data protection approaches by explaining their significance to all members. Security awareness training and data classification policies are also possible ways to raise security awareness in the workplace.  

    • Focusing on data protection strategies

    They should consider endpoint security practices such as encryption, two-factor authentication, etc. Besides, the data production strategy includes a plan for data backup, disaster recovery, and business continuity techniques. For instance, the plan for data recovery should mention the recovery point objective and the recovery time objectives for each type of data.

    4. Poor Data Quality and Data Silos

    Data silos or poor data quality —which may be unstructured, have diverse formats, include duplicate records, etc., processing errors—are another typical issue that clients of KMS Solutions often face. 

    Manual mistakes occurred during data input are a significant contributor to inaccurate data. If unqualified data is used to influence decisions, it might cause considerable negative consequences. 

    In addition, the inconsistencies in data that may overlap across silos also severely affect the data quality. Siloed data  leads to obstacles in sharing information and collaboration across departments, which means that even data regarding quarterly expenses can contain significant inaccuracies because the data from distinct departments of the enterprise are not synchronized with each other. 

    Moreover, it’s also a barrier for leaders to have a comprehensive understanding of company data. Keep in mind that as big data software becomes more complicated, mistakes will increasingly become more frequent and likely.


    The first solution that enterprises can think about is creating a centralized system. In terms of data management systems, the most effective method to eliminate data silos is to consolidate all data in a cloud-based data warehouse or data lake – a central data repository optimized for efficient analysis. Then, they should create a data directory where all records can be structured and sorted to reduce redundancy. 

    Besides, by integrating data efficiently and accurately, they can prevent future data silos. Some methods for integrating data include:

      • Scripting: This consists of scripts written in  SQL, Python, or other scripting languages to transfer information from siloed data sources into the warehouse. However, it’s inflexible and time-consuming since any new data input will require an update.
      • On-premises  ETL tools: ETL and ELT tools make the process of moving data from different sources to the data warehouse occur automatically. These technologies take data from sources, turn it into a standard format for analysis, and load the result into a data warehouse. 
      • Cloud-based ETL: This method leverages the cloud provider's infrastructure, which includes a data warehouse and ETL tools optimized for their environment. ETL eliminates silos by giving the technological capability to consolidate data from several sources in one spot for analysis. 


    All the challenges mentioned above can be addressed effortlessly by implementing analytic database management software like Vertica or having the help of data analytics consultants.  

    KMS Solutions has long been recognized as a trusted partner in providing tailored analytical solutions to enterprise organizations that span across industries. The company’s Data Analytics Center has offered various data analytics services for enterprises in Vietnam, and the APAC region, including Data as-a-service, Data warehouse, Customer data platform, and Seamless analytics integration. We also keep up with the latest technologies to ensure the solutions for clients are innovative and suitable for the market and regulation changes.

    Address big data challenges easily with the help of KMS SoLutions!