Big data means volume, variety and velocity. These things have become critically important thanks to a flourishing social media revolution. Internet, digital and online advertising companies are dependent on technology because of the sheer volume of data they deal with every day. Here are 10 of the challenges these companies face when it comes to big data business intelligence:

• Immature Vendors and Naïve Products: Mushrooming growth poses a challenge. This is a result of clear hype in present technological scenario trends.
• Infrastructure Investment: This is typically phenomenal when compared to LOB applications for routine daily operations. It includes servers, storage, bandwidth, transfer and skilled resources (data scientists, statisticians) costs. Necessary investment further depends on how much data is stored in TBs and how much is processed. Corporate data retention policies are critical in such scenarios, and organizations have to assess costs accurately, including hidden costs, before making investment decisions.
• Extended Gestation Periods: Software evaluation and assessment is time consuming (~50%). There is a variety of proprietary tools, software and open-source, big data BI tools separate from SaaS-based cloud offerings. At times, these choices can contribute to significant confusion as organizations struggle to decide what is next.
• Capacity Planning: Organizations must predict data volume and trade-off. This is a core prerequisite for successful implementation of big data BI solutions.
• Real Time Analytics, Streaming Analytics and Insights: These are critical but typically require additional investment and significant infrastructure setup.
• Commitment Concerns: For any big data BI solution implementation, it is imperative to have a roadmap aligned to corporate strategies, objectives and visions. Business sponsor buy-ins and their expectations are crucial concerns.
• Big Data Platform and Analytics Framework: This requires significant engineering effort and unaccounted hours in R&D before organizations can show results. There is a need to divide and conquer, progress in phases, and use agile methodologies that match program execution goals and objectives.
• Perfection of Big Data BI Solutions: Organizations must first achieve operational reporting efficiency to have the best chance for success. It is next to impossible to achieve perfection in big data BI solutions, but organizations must strive for it anyway. Businesses should not view solutions as one-shot cures, as it is normal to experience failure after failure. Even the social media giants are not exempt from these challenges. Rather than focusing on failures, it is important to realize that there are always opportunities for improvement. The better the design and architecture, the better the performance. Infrastructure investment is a defining factor for solution implementation.
• Need for Big Data BI Solution Implementation Versus Current Daily Operations Focus: It is difficult to align, integrate and manage unstructured, semi-structured and structured data in a consistent and timely manner. Organizations can benefit from leveraging machine learning systems and technologies for big data BI Implementation.
• IT Budget Constraints: Budget concerns represent a huge challenge, and it is critical to prioritize incremental investments. Detailed cost-benefit analysis, total cost of ownership (TCO) and return on investment (ROI) are critical factors. These metrics influence implementation and adoption decisions.



Technical Architect