Big Data Examples For Startups

Data examples

Big Data Examples

Small businesses and startups tend to be more open to innovative new technologies and readily implement fresh tools into their daily operations. This is, in part, because smaller businesses typically have the benefit of being more agile, but also because resources and capital are less than that of their larger competitors, and so finding ways of improving efficiency while reducing costs is part of ensuring business success. Big data is a notable technology that’s often entwined with startups, and through the rapid growth of both the raw data and the tools available to analyze it, it’s given many smaller businesses the edge they need to compete effectively with some of the bigger players in their field.

Big Data Examples Infographic

(Infographic Source: Datacamp)

How Startups Successfully Manage Data

Launching a new business is a challenging ordeal chock-full of so many different demands that many would feel the obstacles never-ending. Big data, however, can be highly beneficial when applied to prioritized tasks, and if challenges are clearly defined analytics helps produce active methods of tackling them. Another consideration is the end goal: for many startups, the ideal situation is early success that leads to a buy-out by an organization able to invest heavily in growth (and line the founder’s pockets). In such scenarios, having a full set of data and analytics information in place helps sway the interests of potential buyers, providing the necessary data around business success, but also enabling better integration of the startup into the purchasing company. Of course, managing all of this data is only one of many demands on new business leaders; fortunately, self-service BI solutions available are cost-effective and user-friendly enough to suit most needs, and with the many customizations available ensure scalability and flexibility as required.

Marketing & Big Data

In the early days, startups find themselves taking on competitors with much greater resources and a well-developed market share to boot. Developing their own customer base is essential for any kind of success, and big data analytics is one of the strongest available methods for growing their market share. Detailed information and the insights gleaned from it help create personalized consumer engagement strategies, an area in which small businesses are often more able to meet customer needs than their larger competitors. Big data can also provide timeous and relevant feedback and monitor brand progress in the social media realm. Significantly, the tools necessary to employ big data for marketing strategies are available in so many different forms that startups find themselves as equipped as industry leaders to create successful marketing campaigns.

Compliance Issues

Of course, big data and it’s employment does come with some difficulties, compliance issues not least of all. As data complexity grows, along with privacy and security issues, collecting and storing information needs to be carefully managed. Startups would do well to carefully assess internal compliance efforts, pulling them in line with regulations in all of the areas they work. It’s also imperative that data protection measures be in place, and the creation of policies around encryption and accessibility is a task not to be neglected. Though keeping up with data compliance can be a challenge, particularly when working across different regions, it’s a fundamental piece of building an efficient business.

Considering the many complexities around big data, some new businesses might deem it an unnecessary complication in the early stages; this would most likely be a mistake. If necessary, utilizing a data scientist or analytics service in the early days for essential functions only could provide the starting point for the development of a big data strategy. Because startups are uniquely positioned to quickly act on big data insights, it would be impractical to dismiss the field in the early stages of a business’s growth when product visions and customer engagement are most malleable. Finding a way to incorporate big data processes is an effort well worth taking.

By Jennifer Klostermann

Patrick Joggerst

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