Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

7 Steps to Consider Before Kickstarting Your Big Data Project

DZone's Guide to

7 Steps to Consider Before Kickstarting Your Big Data Project

Big data is turning out to be a game changer in shaping the future of businesses. Check out these steps you should take before starting a successful big data project.

· Big Data Zone ·
Free Resource

Hortonworks Sandbox for HDP and HDF is your chance to get started on learning, developing, testing and trying out new features. Each download comes preconfigured with interactive tutorials, sample data and developments from the Apache community.

Big data is the new oil. Big data is too big to ignore.

Regardless of who coined these phrases, there is no denying the fact that ever since the arrival of the internet, big data has been the buzzword that has caught the eye of executives like never before, albeit the definition of big data continues to evolve. Businesses are increasingly turning to big data analytics solutions to measure and improve their performance and productivity. Whether it's banking, retail, insurance, telecom, mining, manufacturing, customer services, or competitive sports, big data is turning out to be a game changer in shaping the future of businesses.

What began as a nascent concept in early 2010, big data has already gained huge momentum in just in a couple of years. Historically, organizations have relied on legacy RDBMS systems, which were limited in their abilities to process only the structured data. Thanks to accelerating advancements in IT and processing capabilities, web usage, social media platforms, and connected devices, an unprecedented amount of semistructured and unstructured data is being generated. Simultaneously, the capabilities to store and analyze big data to bring deep insights are also trending, benefiting all stakeholders.

Here are a few more staggering statistics on the amount of big data that is generated across different industries:

  • Facebook gathers and processes more than 10 terabytes of data according to an IBM report
  • An oil rig generates 5 terabytes of data every minute
  • A jet engine generates 10 terabytes of data every 30 minutes

Globally, more than 2.5 quintillion bytes of data are generated every day — that's 2.5 followed by 17 zeros!

All these examples substantiate the same narrative: data is another resource to economic input, just like labor, capital, and technology.

According to research firm International Data Corp. (IDC), annual spending on big data is expected to grow by 23% CAGR from 2014-2019 and could reach $49 billion in 2019 — a signification proportion of the aggregate corporate spending is on IT. Another research study carried out by GE reveals that 87% of enterprises believe big data analytics will redefine the competitive landscape of their industries within the next three years.

So, with the growing conviction that big data can bring a paradigm shift into an organization's market positioning and competitive strategy, an inevitable question companies need to ask is: Are we ready to switch gears and include resources for a big data framework adoption?

Big data framework adoption can be a daunting undertaking. However, a forward-thinking organization can build a big data ecosystem backed by robust data warehousing and analytics capabilities to position itself ahead of its competition. Findings from Bain & Company showed that early adopters of big data analytics are twice as likely to be a leader in their industries and five times likely to make decisions faster than their competitors.

Having worked with some of the most renowned organizations across industries, Trianz has developed a big data adoption framework that includes seven steps that organizations should consider before they kick-start a Big Data Projects.

1. Understand Industry Point-of-View on Big Data

Gauging prevailing sentiments and acceptance of big data's potential is the first step an organization should take to begin its transformational journey. Assessing current capabilities and trends, as well as appreciating success and failure of near-rivals, can help an organization sidestep mistakes made by others. Additionally, research reports from well-regarded publications like IDC, Forrester’s, and Gartner can assist in benchmarking potential analytics solution providers and vendors.

2. Identify Business Case Proof of Concept (PoC)

Once the potential and strength of big data are articulated, it is very important to identify the business cases that could lead to an increase in RoI for the organization. Developing a sound business case with a clear demonstration of associated costs and values and other strategic outcomes can help in getting executive approval and funding for a pilot phase.

3. Evaluate Current Tools and Technology

After a decision has been made to go ahead with a big data pilot phase, the next logical step is to evaluate and finalize the best-fit tools and technology stack that meets current enterprise architectural guidelines. Identify vendors with good credentials and demonstrated success in the big data field. Once each vendor is scored based on enterprise product/tool evaluation framework, the product with the most points can be finalized. It is also critical to select an implementation partner with expertise in executing big data projects since such an association can accelerate the project pace with an opportunity for the organization to focus on its data talent.

4. Develop Big Data Implementation Framework and Process Steps

A thorough evaluation and understanding of the various steps involved in implementing a big data framework can lead to setting up success benchmarks. Over the course of implementation, organizations can use iterative and agile techniques with evolving business requirements to deliver quick solutions for different business units. Therefore, an incremental approach that addresses both short- and long-term aspirations of businesses would be ideal, as it provides stakeholders the resources to reassess the budgetary requirements as well as focus on any unanticipated risks.

5. Finalize Architecture for PoC/Pilot Project

Organizations that have invested extensively in their business intelligence and information management systems need to do their due diligence while finalizing the appropriate architecture. Some elements of the current IT stack might need a redesign to support newer applications. In our experience, most organizations favor a modular approach to big data with a flexible and scalable functionality, without causing any undue strain on their existing IT infrastructure.

6. Capture Business Measures of Successful PoCs

An analysis of pre-implementation expectations with the outcomes achieved from the big data pilot can help identify the success and failure of the effort, as well as develop recommendations to drive future enterprise-wide big data initiatives. Realization of two or three measurable outcomes can help demonstrate the potential of big data analytics from both an IT and a business perspective.

7. Envision Big Data Roadmap

Once the business results are proven with PoC, business and IT unit heads can envision a long-term big data roadmap for the organization with measurable and meaningful business and financial goals. The execution team can drill down into this roadmap with clearly articulated key metrics, expectations, and values to be gained.

Hortonworks Community Connection (HCC) is an online collaboration destination for developers, DevOps, customers and partners to get answers to questions, collaborate on technical articles and share code examples from GitHub.  Join the discussion.

Topics:
big data ,data analytics ,big data projects ,poc

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}