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Six Ways Big Data Has Transformed the Business Market

Guest post by Rachel Stinson

 

Big data has become viral; it's a certainty.

Linked to technological developments in recent years, data processing was intensified, and has become a real challenge for organizations of all sizes. The collected data are of any nature such as the clicks on a web page, the generated content by a user, the social media or the commercial transactions (intentions of sale or purchase).

In a rather unfavourable international economic context, it is perceived as a miracle cure, which could allow companies to create value and gain a competitive advantage. But beyond the buzz, is there 'big data' a reality? And how can this phenomenon redefine the operation and the very nature of companies?

The elusive nature of big data, as well as the difficulty of defining it in a precise way, seems to be partly due to the very use of the ambiguous 'big data' term whose origin is still debated until now. Add to this an indirect allusion to "Big Brother" rising.

SEO agency or Magento development, through its advice and development of websites, brings us a touch of hostility to the perception of big data by the media and the general public, to understand that it is therefore important to overcome the buzz 'big data' if we really want to understand the change that is taking place in the world of business and globally in our society.

Here are six ways that Big Data has transformed the business market.

B2B - B2C border markets less and less marked

If a few years ago, it was easy to differentiate very clearly the BtoC market from BtoB it is today less and less the case.

The digitalization of companies favours the fact that the B2B tracing its habits on uses that we usually find in BtoC. A decision-maker is also a consumer, the behaviours he will have in his professional setting will be similar to his habits in his daily life. He searches the web, consults opinions, gets information and turns into a real "expert".

Its level of requirement towards the companies will thus have significantly increased these last years compelling the commercial services to review their practices to answer as closely as possible the expectations of their prospects.

However, even if we observe that borders tend to be erased, there are still specificities, especially for companies that have not made the shift to digital and that impose on their prospects a more traditional approach, the only point of contact possible is still the commercial. A model that today seems rather archaic and will certainly be doomed to disappear in the very near future if these companies wish to remain competitive.

Customer experience is, therefore, becoming a clear priority for businesses, and businesses need to rethink their traditional sales processes to meet them. They must become smarter, faster and more intuitive. This includes a refocusing of all the divisions of the company to meet the expectations of their customers and prospects more effectively.

As well as by the harmonization of the data within the company, to obtain a very precise vision of all the points of contact of a customer allowing to offer them a homogeneity in their experience with a brand.

Big Data and new technologies, from accelerators to digital transformation of commercial services

Just as marketing has experienced its revolution, sales are changing, becoming techno and entering the era of digital transformation.

'Innovation', 'predictive intelligence', 'automation' are words that are becoming widespread today in companies.

If data have always been a component of marketing, the arrival of big data, targeting tools, and real-time communication have enabled more than ever marketing to implement multichannel campaigns and deliver contacts to the business ultra-targeted.

All these innovations favour the digital transformation of sales forces. The salesman is transformed into a consultant, and all the tools he has available will allow him to personalize each of his answers, anticipate each need, and refocus on his stake: satisfy his client. A striking example is the rise in power chatbots where technology can bring qualified contacts in real time but where the relationship remains important because the salesperson must know how to guide his prospect subtly to the act of purchase.

In its latest Sales Trends and Observations report, Salesforce also highlights that the implementation of smart technologies to automate and optimize sales processes will grow strongly in the coming years. In particular, we expect an increase of more than 118% for predictive intelligence tools and more than 139% for Artificial Intelligence.

Increasing process automation that does not replace human intervention

It is true that with the advent of big data, an entire pan has been added to the structures. It is becoming increasingly common to hear in companies such as Data Lake, DMP or data-driven marketing...

In reality, it is clear that the amount of data generated has become so important, it is increasingly imperative for companies to equip themselves with technological solutions to enable them to manage the mass of data and to facilitate their use.

The automation of these processes has become a must for all companies. However, if these technologies make it possible to transform the raw data into smart data, the human intervention in the understanding of these data remains paramount.

Proof of this is the multitude of new data-related jobs that we see emerging: Data scientists, data analysts, Chief data officers, who are the only ones able to identify the useful information according to their specific business needs.

Target or goals they wish to achieve.

All these technological advances around data are a real opportunity for companies, and the automation of certain processes should rather be perceived as a facilitator for marketing and commercial services.

At the origin of the big data phenomenon

The congruence of several technological phenomena is responsible for this acceleration of the volume of data generated. Firstly, thanks to web 2.0, the internet has moved from a static phase in which web content was frozen and only produced by developers and other webmasters, to a dynamic phase in which users have acquired the ability to generate new content. Content via blogs, video sharing sites and of course social networks.

The meteoric success of the Facebook behemoths (1.3 billion active users) and Twitter (650 million) is another factor that has amplified the generation of data by users. For example, 500 terabytes of data are transmitted daily by Facebook's servers (the equivalent of 20,000 Blu-ray discs). Add to this, the increase in the number of connected devices per individual. We are talking about an average of 2.5 devices per person on Earth in 2017.

Finally, the integration of standardized electronic identification systems in a growing number of objects is causing a massive disruption of the internet, dubbed web 3.0 by some and the internet of things by others. Houses are increasingly 'smart' and connected through their television, refrigerator, washing machine, but also thermostat and electric meter. But the Internet of Things goes far beyond homes.

For example, almost all aircraft equipment such as engines flaps or landing gear has an internet connection and generates data. According to Virgin Atlantic, a Boeing 787 provides nearly half a terabyte of data per flight. The sensors have invaded the production lines while the cities also become 'smarts' and connected.

An awareness rather than a technological revolution

Very often, names such as Hadoop, HBase, BigTable, Hive or Cassandra appear as representatives of the technological revolution associated with big data. All these solutions are based on the principle of distributed architecture that is far from new in computing.

The techniques used to analyze big data come from business analytics and business intelligence, both of which have been around for a long time. For example, the social network flow analysis is very often based on sentiment analysis techniques that have existed for several decades. In the same way, the analysis of big data is often accompanied by an algorithmic dimension whose techniques (like clustering, association learning or Bayesian networks) come from the discipline of machine learning, which emerged in the sixties.

Big data is therefore not a technological revolution. Rather, it is a general awareness of the amount of data that is now available to businesses and other organizations, and the potential they contain as well as the strategic opportunities that can be generated by their treatment. As a result of this awareness, there is a change in the way companies operate and a redistribution of competitiveness cards.

The new fuel of innovation

Being able to process, store and analyze these vast and continuous flows of data flows offers an infinity of new possibilities to support decision-making, to acquire knowledge, to optimize activities and to innovate. Organizations that can transform this data into strategic information will have a decisive economic and strategic advantage.

This is the case of Netflix. Thanks to the analysis of several years of data on the behaviour of Netflix users, the American company has successfully transferred from a simple provider of online content (series and movies available for streaming) content creator, for example, producing series such as House of Cards, which continues to generate revenue and win prizes.

UPS uses a new guidance system for vehicles through real-time analysis of data generated by customers, drivers and vehicles alike. It saves time, money and fuel consumption, giving UPS a competitive advantage over its competitors.

At the heart of the companies, this awareness generates a mutation.

First effect: a trend towards "datafication".

Many initiatives are emerging with the aim of digitizing as much as possible the information generated within the company but also that which revolves around it. Consumers are no stranger to the rule because the datafication of consumer behaviour is also a growing phenomenon, which causes a situation of rupture in certain sectors such as insurance or the bank.

Conclusion

However, the preponderant role of data and its analysis in the operation of companies does not imply a decline in the human factor. Big data offers managers the opportunity to measure, in a very accurate way, the activities of a company. This direct information about the state of the company has an immediate effect on decision-making.

Already in 2011, a McKinsey report had expressed concern about the global lack of expertise and 'big data' skills within companies, allowing data to be organized, extracting relevant information and making it make strategic decisions.

It is therefore imperative that training organizations become aware of the change that is taking place within the management function. A young manager entering the labour market today will inevitably be confronted with big data projects during his career and will have to evolve more and more in environments where data will have a predominant role.

This general awareness of the importance of data announces turmoil of rules in terms of competitiveness. Data has crossed a path from support to strategic resource, which in turn transforms organizations deeply.

Business strategies will be more and more 'data-driven' because big data is an engine of innovation and new business models. The company of tomorrow will be a "quantitative enterprise" in direct contact with its environment, its state and its activities. An entity in constant evolution that’s most precious resource will be its data.

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