Big Data, the current buzz word in the IT and financial space has caught the imagination of a lot of organizations. Studies released in the past year or so have clearly shown that Big Data investments are rising across industries and around the globe[1]. It represents a business adoption paradox: it promises speed, but successful adoption takes time. While there are benefits to be gained from Big Data, the picture around its adoption is still fuzzy — as is usually the case with any new emerging technology.

Big Data is a technology-driven movement and its strategic importance requires special focus and attention during its adoption, owing to the following[2]:

The technology to support Big Data is evolving rapidly which takes time to mature.

  1. While the three Vs (Volume, Velocity and Variety) have been regularly used to define Big Data, the use of technologies that support Big Data are not confined only to the way the three Vs are defined. Their usage has a much broader scope when correctly analyzed from the organization’s perspective.
  2. The effective utilization of Big Data requires a change in mindsets regarding the way it is used. While other technologies help solve problems like streamlining the inventory management process or providing an online system enabling the order shipment tracking on a real-time basis, Big Data helps find the problems that require attention. For example, the combination of factors that causes defects in the manufacturing process, or the factors contributing to the sales differential between two stores.

Much like other evolving technologies, it is useful to first conduct small-scale projects in Big Data, for a better understanding of the technology and the business areas that may benefit from it. While these projects (more like proofs-of-concept) are good as a starting point, the mainstream adoption of Big Data requires a structured framework. This is because the solution space in this area comprises not only large, isolated and varied data sets, but also a rapidly evolving technology landscape. Trying to get all the requirements defined at one go and selecting the technology of choice at the outset can potentially derail the entire program and leave the organization with a dead investment.

 

Adoption Framework:

A key factor in the success of any new program is the way it is approached from the inception phase itself. Any Big Data program that requires the integration of data with strategic planning is going to be critical and will carry heavy penalties in case of failure. The right framework to enable the adoption of Big Data analytics within the organization must be adopted. The critical components of this framework include:

  • Data discovery
  • Analytics discovery
  • Tools and technology discovery
  • Infrastructure discovery
  • Implementation

Big Data Adoption1

Big Data Adoption Process[3]

 

Recommendations for Big Data Adoption:

Driven by the need to solve business challenges, in light of both advancing technologies and the changing nature of data, banking and financial markets companies are starting to look closer at Big Data’s potential benefits. To extract more value from Big Data, we offer a broad set of recommendations tailored to banks and financial markets firms.

  1. Commit initial efforts to customer-centric outcomes.
  2. Define Big Data strategy with a business-centric blueprint.
  3. Start with existing data to achieve near-term results.
  4. Build Analytics capabilities based on business priorities.
  5. Create a business case based on measurable outcomes.

Once adopted, Big Data technologies shall change the financial services industry in the following ways[4]:

  1. Machine learning is set to accelerate and shall gain momentum when applied with fraud and risk sectors. This gain in momentum shall come from education and real world applications like financial markets, banking etc.
  2. The gap between market leaders who utilize Big Data services and others which do not is set to increase as more and more businesses understand the importance of Big Data.

Data lineage, governance and data compliance shall be more deeply embedded with Big Data platforms to find a more comprehensive solution to handle compliance mandates, and to upscale their current legacy systems to newer and faster software services. Hadoop is at an advantage here since many new platforms can reach beyond current and legacy systems to give a holistic view of the regulatory scene today.

 

How Hexanika makes use of Big Data?

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Hexanika is a FinTech Big Data software company which has developed an end-to-end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance. Hexanika’s innovative solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

Hexanika’s unique Big Data deployment approach by experienced professionals will simplify, optimize and reduce costs of deployment. It strives to achieve this by following the process as shown below:

Hexanika_BigData

Big Data Road-map[5]

Hexanika addresses Big Data using its unique product and solutions. To know more about us, see: https://hexanika.com/company-profile/

Feel free to get in touch with our experts to know more at: https://hexanika.com/contact-us-big-data-company/

 

Contributor: Akash Marathe

Image Credits: www.publicdomainpictures.net

[1] Source: http://www.datawatch.com/2015/10/09/a-new-trend-emerges-in-big-data-adoption/

[2] Source: http://www.tcs.com/resources/white_papers/Pages/Big-Data-Adoption.aspx

[3] Source: http://www.ibm.com/services/multimedia/Analytics_The_real_world_use_of_big_data_in_Financial_services_Mai_2013.pdf

[4] Source: https://www.mapr.com/blog/top-10-big-data-trends-2016-financial-services

[5] Source: https://hexanika.com/history-of-big-data/

 

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