Data has never been bigger. One of the main reasons organizations are not utilizing this information is because with it, comes a whole new set of challenges. A recent survey found that 55% of big data projects are never completed – ouch.

In fact, from what we’ve seen, the majority of on-premises big data projects aren’t successful. So what’s the problem? Why so many issues trying to gain insights from information you already own?

1. Challenging Tools

The most commonly used open-source tool, Hadoop is available free of charge, but it is most certainly not free of headaches. Even with the right talent on board, deploying Hadoop on-premise is a sizeable task that requires the knowledge to use it.

73% of the Infochimp’s survey respondents claimed understanding the big data platform was the most significant challenge of a big data project.

Managing Hadoop onsite is also quite a task, especially if big data expertise is limited. In some cases, businesses simply don’t have the required infrastructure in-house that’s needed for proper data integration.

“What made Hadoop so “damn hard to use? It’s low level infrastructure software,” said Todd Papaioannou, the former Chief Cloud Architect at Yahoo who was quoted in a recent article on, “and most people out there are not used to using low-level infrastructure software.”

2. Underestimated Project Scope

This is not your average IT project. If you approach it as such you are lacking the proper perspective. Instead, you should view your big data project as an ongoing, constantly evolving business strategy that is expected to grow, change, adapt as the business needs it to. It requires people who are open to being flexible with adaptive minds and skills to deploy effectively.

The big data strategy should be integrated into an overall larger business strategy. By looking at from a much wider angle, businesses are more likely to understand what they are up against and have a much better chance at achieving the goals of the project.

3. Lack of Specialized Talent

Not only do you need available resources to tackle a big data initiative, but you also need those resources to have a specialized skill set and experience. The new term is “Data Scientist” and it is serious business.

“A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math.” (

Businesses also need data experts with domain knowledge, or knowledge about the industry they’re in, to truly implement big data solutions that will benefit the company.

Finding and hiring people with these specific talents has proven to be a significant challenge for many organizations. Demand for data scientists, especially with industry knowledge, is high and supply is low. Until more universities and organizations start educating and training more individuals with big data skills and industry-specific knowledge, this will continue to be a major stumbling block.

4. Poor Planning

This relates to the earlier point about underestimating the project scope, but in addition to that, many organizations find themselves mismanaging time and improperly allocating resources. Proper planning requires skilled project leaders with the ability to see the big picture, manage the project and clearly communicate to the whole team next steps, what is needed and how to accomplish specific tasks. Without this constant communication and dedicated management resource, plans often go fall short and crumble as people quickly feel overwhelmed and confused.

The Solution

Big data project success requires a great deal of organization, skill, talent, tools and the proper infrastructure. Companies who insist on tackling this initiative in-house should make sure that they have the right talent on hand, the right strategies and the right technology in order to fulfill business goals and gain a competitive edge they desire.

Be sure to do your research. Plan carefully and remain flexible. Aquire the right tools and a staff with the expertise to use them.

Many organizations will find that the investment of time and resources required to properly plan, build and manage an on-going big data initiative in-house is simply neither feasible nor cost-effective. In this case, we highly recommend utilizing Bag Data-as-a-service. By purchasing big data in the cloud, you are able to get best-of-breed technologies, highly specialized resources and quicker results, allowing you to put your data to good use sooner.

61% of survey respondents agreed that managed, hosted platforms are a viable solution. 

With Big Data-as-a-Service, the service provider eliminates the pain points listed above by fully managing the technology and seamlessly becomes an extension of your team, providing communication, support and insights, allowing you to focus on your core businesses and benefit from the valuable information that is shared.


Infochimp survey, “CIOS & BIG DATA What Your IT Team Wants You to Know” 

Schedule a consultation today to learn how Flagship can assist you with your big data and analytics initiative.

If you liked this blog, you also might like:  Data at Rest Will Remain at Rest, Unless Acted Upon by an Outside Force

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Big Data and Analytics

Businesses and governments worldwide are being challenged to make sense of data and gather valuable insights from structured and unstructured data that are emerging from a variety of sources such as videos, blogs and social networking sites. To meet growing client demands in today’s data intensive industries, IBM has established the world’s deepest and broadest portfolio of Big Data and Analytics technologies and solutions, spanning services, software, research and hardware.Flagship Solutions Group helps clients tackle these big data challenges in virtually every industry – from public safety to healthcare, retail, automotive, telecommunications, and everything in between.As evidenced by our broad big data solutions portfolio, powered by IBM, consulting services, capabilities around cloud and capabilities around traditional software deliveries, we offer the full spectrum of analytics capabilities organizations need to handle big data and extract value from it — from descriptive, predictive and prescriptive to cognitive, including predictive capabilities that allow users to model once and deploy broadly on both structured and unstructured data. Effectively harnessing unique insights from big data can drive better business outcomes, helping businesses grow, attract and retain customers, improve the intimacy enterprises have with their clients and understand more about customers – how to anticipate their needs and provide more targeted and relevant marketing and services. Learn more by exploring our many content pieces. 

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