While help with data management and fixing and finding query performance issues faster is assured through the right process, most organizations today have no idea about the entirety of big data and are getting it very wrong. A good number of companies are not too keen on big data, while others are remaining on the sidelines for a long time only to fail in big data. Clearly, all the rage today is big data and lots of organizations are keen on ensuring their data has been put to use. The hype on big data continues, but over 92% of organizations are still clueless or unwilling to invest in it. Some are planning on getting started in the future or simply avoid investing in projects of big data altogether.
Avoiding Management Resistance
While big data tells a company so many things, there’s a continued management resistance right from the start across the board. For instance, it has been found about 62% of business leaders have indicated they trust their gut while 61% rely on their real-world insight and do not give a second thought to hard analytics while making their decisions.
Most organizations are not understanding or making most of big data since they are choosing wrongly. Companies are either starting with very ambitious projects they are unable to tackle and maintain or attempt to deal with big data issues through typical data technologies rather than specialized tools like Apache Pig. The result is failure in either, which shouldn’t be the case.
Without a doubt data science is complex in its own right since it means blending diverse domain knowledge of programming, statistics, math, and industry knowledge. Lots of companies today are hiring data scientists siphoned from programming and math disciplines and geniuses in these areas only to find out that they lack the critical domain knowledge component. One way of dealing with this has been the consistency that data scientists need to come from within the industry.
Lack of Proper Skills
While this is closely interlinked with asking the right or wrong questions, lots of big data projects today are failing or stalling since those involved are showing insufficiency in terms of skills. Lots of individuals involved with big data are sourced from IT yet they are not the people considered to be highly qualified to ask the proper questions surrounding big data.
Unexpected Problems Outside Big Data Tech
Clearly, data analysis is just one of the big data project components. The ability to access as well as process big data is the most important, yet it’s easily thwarted through a number of things such as personnel training, network congestion, among others.
Its clear today that where there is success in big data projects the projects are not isolated at any given time but become a core component of the way an organisation is using its data. Problems can be exacerbated if diverse groups value strategic priorities or the cloud a lot over big data.
Sometimes big data will compel us to engineer a certain course of action or take a route that is not so desirable or profitable that it is ignored consciously. For instance, a mining company can refuse to run human-mineral effects analysis to avoid the legal obligation that will subsequently follow when reporting the adverse effects of mining and minerals on people around a given area.
Essentially, it’s very clear while every organization does want to focus on big data and make the most of it, people are continually blocking its inception.