Article

Jasmine Morgan
Jasmine Morgan 17 October 2017

Reimagining Business Innovation, Competitiveness, and Productivity through Big Data

Big data has infiltrated all areas of business and can now be used as a real growth factor for innovation, getting a competitive advantage and increasing productivity.

The world is on the brink of a new technological revolution and at the heart of this lies Big Data. The concept is characterized by the 5 Vs: volume, variety, velocity, veracity, and, most importantly, value. This last V is the reason Big Data has already become part of finance, retail, healthcare and is beginning to expand into more conservative areas like the public sector and even fraud detection and crime fighting. Big Data is slowly becoming a vector of innovation, a currency and a way to get a competitive edge in an increasingly driven market.

Innovation Catalyst

Innovation is supposed to overcome some significant hurdles, including returning the cost of R&D, getting acceptance from customers and moving from early adopters to the mass market.

Create new products

Big Data can act as the testing and breeding ground for new trends and product ideas. By using deep learning algorithms, an organization can identify underserved needs, niches that are not adequately developed or they can pinpoint their marketing efforts to the right audiences.

Using big data could boost product innovation at every step, from ideation to post launch. Social media platforms can serve as the origin of new ideas, business news can identify the need and development opportunity for an individual product, existing databases validate the prototype and help refine the design, while real-time monitoring on a sample market can show potential.

Create and transform businesses models

As data becomes an asset on its own, companies can develop or extend their value proposition by making use of the information extracted for current operations. The same sets could prove valuable for other enterprises that share the same client base, not only competitors but complementary businesses. Every company with a massive data repository can create a side business from this and have multiple revenue streams. As an example, a pharmacy retailer which records prescriptions sold can sell its data to a health research institute or even an insurance company.

Driving competitiveness

A company works at full capacity when it succeeds in identifying its golden segment of customers, serving their needs in the best way possible and striking a satisfying balance between costs and revenue. Big Data can help adjust all these variables for optimizing outcomes.

Create relevant segmentation

AI feeds on big data to derive patterns and create clusters. This process helps dividing customers into like-minded groups that tend to behave similarly. By analyzing vast amounts of seemingly unrelated data, algorithms can uncover patterns and correlations and create customer segments that are different than those simply based on demographics or sociographic information, but relevant for their behavior about the product or brand, as an expert from the big data consulting company Indata Labs explains. A working concept based on big data was coined by Facebook in “lookalike audiences,” a way of finding similar client groups to those already performing well.

Online reputation

Getting an edge against your competitors can be done by performing sentiment analysis on the information willingly thrown around on social media by clients or leads. Checking their perception on your brand, their affinity for the competitors’ products or simply discovering their interests can translate into new business.

Also, scraping the online environment for references to your brand can uncover some negative reviews or even unfair competition practices that could be detrimental to your brand. 

Enhance customer experience

Knowing who your customers are and what they think about you is just the tip of the iceberg. The real challenge begins when companies strive to pamper their clients more than their competitors. You can be on top of this game by learning from the information they provide about themselves online and offline and tailoring your offer to suit their taste. Cookies, third party data, or census data, anything is permitted in this war. Clients have become aware of the fact that companies collect information and if they give up some of their privacy, they expect top customization and the best deals in return.

Boosting productivity

Time saving

Making employees keep logs of their daily tasks is counter-productive, but automatically collecting such data and taking decisions based on its analysis can result in double digit growth. Of course, certain systems like detecting system login during work sessions need to be set-up, but once data collection and analysis methods are established, all you have to do is act based on the recommendations.

Cost saving

By analyzing cost centers through algorithms, companies can learn more about the ways money is spent and how inefficient decisions can be eliminated. Even in highly regulated areas like healthcare, public sector spending or law enforcement, AI algorithms can identify duplicates and advise reducing unnecessary spending. A careful analysis of contracts can reveal better opportunities.

New work flows

Using data in taking decisions might seem like a new approach to work for some employees, but it can be fairer, faster, and cheaper. A company running on big data instead of management hunches has more chances of developing quickly. Making staff embrace the new working style could prove complicated, but this too can be changed if the culture of the organization encourages self-evaluation through data available in a personal account instead of a one to one evaluation meeting.

Concerns related to Big Data implementation

Right now, the biggest problem Big Data programs face is the lack of specialists. Currently, there are about 7,000 openings for qualified Data scientists and data engineers and another 9,000 support jobs like data analysis assistants and similar positions.

The other primary concern is related to privacy, third-party security of information and authorization rights from the originator. As long as there are proper anonymization procedures in place and encryption of traceable data these should not be threats, but solvable challenges.

A new primary production factor?

The classic economy has considered land, labor, and capital as the main production factors and subsequent economic schools of thought have added others including know-how, technology or entrepreneurship. Maybe now is the time to include Big Data in the core of the production factors?

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