Navigate your journey to BIG DATA in 5 easy steps

Apr 10, 2017

Big data has been the talk of the town for quite a while now. Thanks to the ever-expanding network of web, mobile, social and sensors, organizations today are inundated with data and information to the point, where they have no idea what to do with it. Big data is getting into the mix with other technologies and helping businesses derive insights right at the point of action. However, for most people and organizations it is still not easy to move beyond the realm of data intelligence and adopt big data analytics.

With the emergence of PAAS (Platform as a Service), IAAS (Infrastructure as a Service) and SAAS (Software as a Service) or ITAAS (IT as a Service) engagement models, companies find it easier to hit the ground in little time with less efforts. Accumulating data or finding ways to manage and store it efficiently is also well within the reach of most organizations, but the real hurdle for these companies is to create a framework that can enable them to connect all the dots and put all the data to determined use – which is to extract meaningful and actionable insights – precisely at the point of inflection. So for businesses looking to harness the power of big data, it all comes down to coalescing the right information and tools with a right approach.

The five steps to help you get started on your big data journey effectively:-


To succeed with Big Data, it’s important that you have a clear understanding of your business processes, data sources (structure and unstructured), workflows and methodologies. This will enable you to define an effective data strategy that would address the hurdles of volume, complexity, variety, and speed – typically associated with big data implementation. Subsequently, identify use cases tied to your business goals which could be more customer-centric, enter new markets, create new business models or improve operational performance. In doing so, focus on how the new data strategy will impact your top line, bottom line or both when it is implemented rather than focusing on how many terabytes of data exist and how innovative the big data environment or tools you have at your disposal. This exercise will help you to logically define the metrics, big data roadmap with required investments (both money and leadership commitment wise), and final business outcomes.


Your data strategy is ready. Now, let us spend some time on building a simple yet scalable (something that can be changed in size and scope based on changed requirements in the future) plan for bringing data sources, analytics tools, and people together to create the business value. Choose one use case or a business problem from all the identified use cases that would benefit the most with big data and bring focus there. This approach would allow you to mobilize management and staff support gradually while avoiding the injection of large set-up capital. Modern cloud platforms such as Microsoft Azure, Google Cloud and AWS offer big data and analytics capabilities where you can build big data clusters on clouds with modern interfaces. With little cost and less efforts, you can get your initial big data setup up and ready to run. The approach also eliminates lead time and cost barriers by accelerating the outcomes of your hypothesis. If it delivers the desired results, you always have an easy option to scale further and expand your big data universe.


Big Data currently serves as one of the most lucrative opportunity for IT solution providers. According to a recent IDC research report, big data and business analytics worldwide revenues will grow from nearly $122B in 2015 to more than $187B in 2019, an increase of more than 50% over the five-year forecast period. The entire big data landscape comprising infrastructure, analytics and applications is expanding rapidly with continuous influx of entrepreneurial activity and innovation. Finding the right technology skills or an implementation partner is one of the most daunting task you may face in your Big Data journey.

So how can you set things straight? The answer is to evaluate the potential business value that a software solution or an implementation partner can offer in terms of:

• Technology stack: Cloud, Database, Infrastructure, System Integration, Extraction/Loading, Testing, Governance, API/Connector development…
• Data talent: Developers, Data Engineers, Data Scientists and Analysts…
• Problem solving competencies: Data Science Labs, AI tools, Consulting Skills…
• Domain experience: Success Stories, Frameworks, References…
• Partnership ecosystem: Infrastructure, Platforms, Applications, Technology…
• Engagement models: Outcome based, Strategic, Hybrid…
• Support: SLA based service desk…

While this may look exhaustive in its entirety but the idea here is to help you to define a selection criteria in the form of an RFP (request for proposal) or a requirement document specific to your use cases or business goals. You need to pick up the most appropriate option to support your big data decision based on the ROI and overall impact it will have on your business. You can find an exhaustive list of data analytics vendors here.


It’s no secret that while scoping out a big data strategy, one need to keep data clean and keep processing it to prevent dirty data from accumulating in the systems. This data cleaning process ensures the analysis outcome is consistent, accurate and more reliable. However, when data influx becomes overwhelmingly complex, you need to set an agile, exploratory environment for your big data applications, where the results of computations evolve with the detection and extraction of more signals. By practicing a unified program for data management, you can coordinate diverse data management disciplines and overcome the challenges posed by data silos across the organization. It enables you to identify data sources more easily and monitor data interaction between all applications.


With every success, it’s important that you indoctrinate your team to spread or evangelize the outcomes by getting to the heart of the insights you have collected and how that helped the leadership in their decision making. When decisions are reinforced with data, the importance of a data-driven approach is bound to strengthen. Start by providing a measurement for aspects of the business thought to be immeasurable before such as predicting bottom line results with ROI, understanding customer’s behavior and action to personalize offerings, improving R&D by analyzing feedback data in real-time among many possible aspects relevant to your business.

Highlighting their efforts of being data conscious is another way to keep the momentum up! They will be happy and more comfortable with copious data collection and insight-based decision making and will no longer ask, “Should we assess this?” and instead focus on, “How should we assess this?”

-Orion Team

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