What are the Reasons Why Big Data Does not Work?
The value of any organization's technology integration depends to a large extent on the quality of its big data for digital transformation machines. In short: big data can achieve digital transformation, anyway, this is the goal.
So how can big data technology bring success to enterprises in the grand plan of things? It turns out that it is not as good as hope. Optimistic expectations for big data may exceed our ability to actually execute big data.
The latest research on the UK online consulting and consulting platform shows that 70% of big data projects in the UK have failed. The study goes on to say that almost half of all organizations in the UK are trying to carry out some kind of big data project or plan. However, nearly 80% of these companies cannot fully process data.
However, this is not news. About three years ago, Gartner, a leading research and consulting company, reported similar situations on a global scale and predicted that 60% of big data projects in 2017 would fail the early implementation stage. Worse, this forecast is too conservative, because 85% of big data projects that year ended up flat.
So why do so many initiatives fail to meet expectations? When trying to drive value through big data projects, what measures can be taken to increase the likelihood of measurable success?
The promise of big data, despite the fact that so many organizations are still working on big data projects, there are some reasons.
Volume and speed——Data explosion: exponential data from more sources from increasing speed of creation
Diversity——Mobile and IoT terminals, the proliferation of traditional data types and the massive increase in the amount of unstructured data
Accuracy——As the saying goes: "Garbage in, garbage out." Big data projects are only as good as providing data.
Value——The white rabbit of big data. Discovering influential insights or new value streams for the organization is the biggest challenge. It is a symbol of differences in potential income and competition. Value is the reason for entering big data in the first place.
The continued potential of analytics and the prospect of deliverables have turned big data into a multi-billion dollar technology industry in less than a decade.
This has a lot to do with McKinsey Global Institute’s 2011 bold prediction of big data: “Big data will become the key basis for competition, providing support for a new round of productivity growth, innovation, and consumer surplus, as long as there are correct policies. And the driving force is in place."
The idea is that almost every company in every industry (retail, finance, insurance, healthcare, agriculture, etc.) is located in the large, diverse, scattered, and disorganized enterprise data left in traditional systems and infrastructure. In the gold mine. Generated by a business. In order to obtain this treasure trove of information, each company needs specialized access and analysis tools to properly connect, organize, and ultimately transform it into a digestible and analyzable form.
Assuming success, the big data infrastructure is expected to provide: Connect and unify all data sources Generate powerful business insights Allow predictive decisions Build a more efficient supply chain Provide a meaningful return on investment Comprehensively change every industry
Although the potential of big data has been proven to be successful in many cases (mainly in large multinational companies and brands), the final state of big data required by most organizations has proven to be a difficult problem.