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Big data transformation: Where to start?

Big data is no different from the other emerging technologies that companies have adopted and integrated over the years. But adopting this technology generally goes hand-in-hand with a corporate transformation, as it always results in change to the company’s business and IT processes or to its products and services.

In order for a big data project to be successful, you need transformation methodology that involves gradual upgrades—one maturity level at a time—across all company dimensions (processes, skills and technology). This methodology should aim to reduce risks and help you understand how implementing the solution will impact your organization.

The NOVIPRO maturity model

NOVIPRO offers an agile big data transformation program that’s designed to help companies initiate big data projects (once they’ve decided to move forward with one). Our Maturity Model Program includes an analysis and objective progression grid that is strictly supplier-independent. This model offers two paths: 1) a path to improve IT processes and skill sets; and 2) a path to optimize infrastructure to attain the level of agility you want for your business units.

This program establishes a path that’s based on implementing IT architecture patterns or capacities taken from big data best practices. The architecture patterns are the big data capacities or functionalities that you need to fully exploit specific information for your business needs. These patterns or functionalities are created by IT processes and skills, and supported by technologies, while remaining independent of supplier solutions, as previously mentioned.

Factors to consider in a transformation

Are you concerned about data acquisition costs, threats to your country’s security or deciding what IT infrastructure is needed? Are you also worried about integrating big data into your company systems, BI and business processes, as well as selecting big data solutions in an emerging market? Our big data transformation program will ease your concerns.

Data acquisition costs can definitely put the brakes on a big data initiative. In fact, considering all the steps in the big data lifecycle—from identifying the source to its final use—processing costs can be prohibitive. In fact, these costs are influenced by various factors, including: data acquisition costs, the quantity of missing information, data verification for accuracy, and data volume, variety and velocity.

The acquisition of externally sourced data presents additional risks to security, as it can cause vulnerabilities and impact the enterprise or BI systems that the organization’s processes rely on.

How complex does big data IT infrastructure have to be? It depends on the three Vs: volume, variety and velocity (ref: Gartner). The higher the V values, the more complex the infrastructure needs to be—not to mention more flexible, so that you can quickly add resources for calculations, memory, communications and storage.

Most companies operate data warehouses to extract business intelligence. Big data infrastructure and tools need to be integrated into your existing infrastructure and systems. It’s important to avoid creating separate, unconnected silos, as this can cause problems with the management and reliability of data that that company executives base their decisions on.

The big data market is constantly growing, with a steady stream of new software and materials (cloud or enterprise) being introduced. For many companies, this makes choosing big data solutions a daunting task.

The big data transformation program is designed to help you determine exactly what your target architecture should be and pinpoint the possible solutions given the risks, impacts on the organization and ability to obtain results fast enough for your business units’ needs.

In the next article in this series on big data, we’ll explore the internal risks and issues to consider when embarking on this important transformation.

Read the next article of our Big Data series: Ready to take the plunge? Assessing risk and internal issues.