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If your business doesn’t need It, forget big data!

Everybody’s talking about big data—but what is it? Big data can be defined as large structured and unstructured data sets produced in real time and on a continuous basis, and whose growth is exponential. Because of the sheer size of big data, it’s impossible to manage with classic database management tools.

Everybody knows that data comes from everywhere.

But what motivates companies to exploit big data? Why put so much energy into it when companies can’t even make full use of the data warehouses they have invested so much time and money into? What are the risks that Quebec companies face if they don’t exploit their data—and the massive volumes of data coming from external sources—more intensively? These are some of the questions we’ll try to answer in this series.

The general consensus is that Quebec companies need to protect their internal market from international competition and tackle the global market. To do that, the best companies are banking on solid and flawlessly executed business strategies. Increasingly, these strategies involve exploiting data from internal sources (i.e. company systems) and external sources (created by other organizations or systems, but used by the company).

Information about customers and products is no longer coming from companies’ operations systems alone. It’s also flowing in from social media, digital communications, the Internet, smartphones, cloud services, the Internet of things and connected devices. Companies want to capture this data to derive new knowledge and a better understanding that will ultimately help them respond to specific questions or concerns.

This information should help managers with decision-making. If you opt to leverage big data, it has to come from upper management or at least be rubber-stamped by them because all initiatives should be strategic and driven by business needs.

For example, let’s say a company wants to tap into the South American market using e-commerce. External data from various sources, such as social media, could improve customer segmentation and help the company adjust their products and services to this customer base’s preferences and behaviours. It could even help the company get around the traditional method, which involves hiring a marketing firm in the target market.

Or let’s suppose that a company wants to protect its local market after a new foreign player is introduced. Internal and external sources of data about customers would allow the company to better understand their customers and find ways to gain or strengthen their loyalty. This information could also help them track the company’s reputation, assess its products and services, or develop a brand new offering.

How exploiting big data can affect the company

Big data is based on technologies that allow massive data volumes to be ingested, stored and processed at a significantly lower cost than traditional data warehouses and with enough flexibility to quickly adjust to data growth.

Introducing this new information and related technologies can have a number of organizational and technological consequences, all of which need to be governed by the same management practices already in place at most organizations. These impacts are multi-dimensional and require a holistic approach as they affect management processes (mainly data and system management), the types of skills required and technology infrastructure. Not to mention the potential impacts on the various company functions, like marketing or customer service.

Adopting big data should be done slowly, one step or maturity level at a time. However, the very architecture or bases must be well defined to support the progression and the changes that will come after the data analysis.

In conclusion, this article has given us the chance to look at why some companies are adopting big data and all the issues that come with it. Later in this series, we’ll discuss the opportunities that can come with this transformation.

Read the next article from our Big Data series : Big Data transformation: where to start?