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Three Reasons Big Data Projects Go Wrong, and How to Make Them Right

Organizations that implement big data solutions should be applauded for wanting to make better use of their data. All too often, though, initial enthusiasm fades as highly touted big data implementations miss their targets for return on investment.

The reasons why are often the same. Most big data projects are not:

  1. Optimally tuned to analyze and manipulate data;
  2. Don't provide proper quality controls, nor
  3. Identify data lineage.

That means they are only partially complete. A big data solution without those elements is like filling the gas tank of a Ferrari with regular unleaded, or running it on the cheapest tires; you're saving a bit at the expense of performance and safety.

Realizing the Value of Big Data

"Realizing the full value of big data implementations begins not with technology but with the foundation, which means adherence to sound data management principles," explained Sila managing director Tapan Shah. "That includes five key pillars around the big data infrastructure: data governance, data security, data quality, master data management, and metadata management."

Data management is foundational and incredibly important to the success of any big data project. This is the first piece, which works in complimentary fashion to the technology.

The second corresponding part is data engineering and analytics. This includes data manipulation and data availability, plus analytics and data enrichment where various pieces of data are joined together for even greater insight and value.

Data ingestion is particularly important here, and includes moving data to the cluster, cloud, or other big data architecture with very good efficacy, and in a way that is sustainable, scalable, and that follows best practices in performance.

The misconception, however, is that organizations can reach their big data goals with just the technology piece. That's not usually the case because technology alone is not enough.

"The entire big data implementation only hums when technology is combined with data management, engineering, and insightful analytics," said Shah. "These pieces working in a complementary fashion are what provide the return on investment that justifies the program in the first place."

Benefits of Value Realization

The journey to becoming a data-driven organization that unlocks the value of its data assets undeniably leads to significant benefits. These include:

Business at the Speed of Data: Waiting for data insights is no longer acceptable for organizations operating in competitive, complex, and regulated markets. Proper data management and engineering creates insight from data in real time.

New Revenue Streams: Organizations can develop new products that are driven by data knowledge, and can monetize their data by providing curated access to their massive data troves.

Tailored Offerings: Harnessing the power of data lets consumer offerings be tailored to individual customers and other applications.

Improved Risk Posture: Decision making can be risk-based by leveraging predictive analytics driven by historical and current trends.

Cross-functional Value: Big data insight delivers business value across multiple functional areas like supply chain, finance, operations, human resources, marketing, and sales.

Improved Regulatory Adherence: Comprehensive reporting capabilities ensure that data provenance is recorded, access to data is governed, and that data and associated reports are easy to find.