Take a peep into the future, and you will come face-to-face with many Big data challenges. With paradigm shifts taking place on the technology and business sides of predictive analytics and Big Data consistently, it’s important that we gear up to face the challenges of tomorrow.
Hype Leads to Fear, Uncertainty, and Doubt (FUD)
As per Scott Knau, CEO of Teradata, one of the biggest concerns faced by Big Data marketers and users is linked with the irrational hype surrounding the same. This, in turn, is leading to uncertainty, fear, and doubt with regard to its adoption in various industry verticals and practical applications. The ever-escalating combination of this hype and FUD is releasing mixed messages that are capable of being misinterpreted by decision-makers. This is complicating issues further with organizations juggling between using Big Data to add more value and avoiding the same.
This problem set has been analyzed by Olly Downs, senior VP of Globys Olly Downs, a reputed Big Data company, "It's all about getting into a situation where Big Data is no longer concerning," he opines. He finds an analogy between business intelligence (in its earlier days) and Big Data and how the former struggled to gain the confidence of its users in its entirety. Even as things unravel for Big Data and help it consolidate its position on firmer grounds in the future, it’s important to keep researching, gathering concrete knowledge, and removing all uncertainties that fog implementation-related decisions.
Scarcity of Talent
Big Data has managed to make its mark in a short time, thereby leaving recruiters, data warehouses, and large-sized companies in deep waters with regard to talent procurement. Fortunately, a host of Data Science courses and certificate programs are collaborating with corporate needs and higher education, thereby creating an optimistic scenario for the future. The need of the hour is to understand and adopt the shifts in data science education, encourage the younger lot to go in for more specialized majors—specifically with respect to Biog data use in Business, and upgrade Big Data team members and data scientists with a view to enhancing existing in-house capabilities.
The right way of addressing the challenge of scarce Big Data talent, which is bound to magnify in the years to come, can be seen in the form of a corporate-based model to build a Big Data talent base via a smart mix of toolsets, techniques, and education. Today, companies have to start working on the right people (and also new ones) by:
Building architectural setups and toolsets for developing professional services teams.
Building on all available knowledge worker skill sets to align with the business and technology needs of predictive analysis/ Big Data.
The idea is to recognize the "sweet spot" in the tasks that need the skills and tools of a knowledge worker and a data scientist alike. If companies manage the act, then they will obviously be in a stronger position to leverage the benefits of Big Data as a robust business platform. They will be able to save more time and focus on other higher-end Big Data tasks with more skilled resources in hand.
Unprecedented Growth in Data
The growth of Big Data has been rightfully summed by Russ Kennedy, VP of strategic marketing, CleverSafe, "There is obviously a deluge in information as Big Data expands to capture social media, mobile, and other data-intensive areas," he said. This said it is now time to capture available data in more economically viable and reliable ways. From designing and managing to replicating and securing the data, there is a lot of work in store for organizations dealing with Big Data tools and datasets. It is essential to think of more innovative and better storage strategies today so as to handle this Big Data challenge that is bound to arise tomorrow. Additionally, adherence to international compliance programs and all potential legal issues dealing with the storage of growing data in diverse geographic locations have to be handled too.
Careful foresight and timely preparations, finding ways of storing and protecting expanding data, and innovating the tools of Big Data analytics for transforming the data into more useful information, will create a better tomorrow for all users of this technology.
Currently, most Big Data headlines are tracking how customer behavioral patterns are being impacted across multiple industries. Market watchers are also keeping an eye on how Big data is entering the domains of heavy machinery industries such as construction, mining, and industrial manufacturing. With automation enabling analytics and decision-making like never before, especially where Big Data metrics are concerned, the need for customized applications, software, tools, and technologies is increasing, too; yes, one size doesn't fit all in this case. If companies desire prospers in tomorrow’s Big Data world, they have to start putting their infrastructure, processes, tools, and staffing investments in order. Right way!