Big Data: Influence on C-Level Innovative Decision Process

A lot has been stated about the emergent significance of big data as a tool for business innovation. But how are these types of data utilized to drive innovation? How does it affect the in-house tactical initiatives of business managers and executives? Is big data beginning to influence C-Level decisions?

As earlier mentioned, big data offer enormous benefits to senior executives of a business organization. Big data have the capacity to improve insights into customers buying patterns, forecasts as well as increasing productivity and efficiency for a sales executive (Taylor and Labarre2006).

Furthermore, big data through analytics can assist a Chief Marketing Officer (CMO) in developing a vibrant marketing strategy, social media analysis and customer subdivision. They can also be utilized to understand impending design needs to make logical business decisions. For Chief Information Officers (CIOs), big data can establish the link between IT and business goals during board meetings with the Chief Executive Officer (CEO) (Lohr 2012; Piatetsky-Shapiro 2013; Taylor and Labarre2006).

7.3.1

Stimulating Competitive Edge

In order to be a source of business advantage, big data systems need to build new types of processing to data-sets targeted at stirring innovative impact through the formation of ground-breaking business models, goods or services (see, e.g., the discussion in Chap.4).

As of the time of putting this book together, big data driving income generation, optimization and business benefit which is getting the attention of C-Level executives. It covers many areas from direct advertising, to scam detection as well as transaction optimization. For example, in thefinancial sector, banks emphasize on performing analyses on in-house and customer transactions aimed at detecting scams; likewise retail outlets are modifying their inventories based on previously aggregated insights made possible by big data. A key strategy in getting measurable value from big data is actually analytics; this usually attracts the curiosity of C-Level executives who seek to drive a competitive edge in the global economy (Economics2013).

In a survey lead by a technology research organization, Economist Intelligence Unit discovered that senior executives require big data for their workforces due to the perceived belief that the use of analytics tools will enhancement productivity within the organization (Manyika et al.2013). Thus, business organization CIOs are now playing the role of software evangelists for big data analytics solutions. They are also making sure that the CEOs and other C-Level executives realize that insights derived from big data can be the key edge to drive a competitive advantage which results to the targeting of innovative markets, fraud reduction, better- informed business decisions etc. (Lohr2012; Taylor and Labarre2006).

7.3.2

Predictive Analytics: Data Used to Drive Innovation

The explosion of big data implies that predictive analytics is gaining wider acceptance for operational use. For example, SAP’s vice-president of product

marketing analytics, James Fisher, predicts that advanced analytics market will be worth about $3bn by the year 2016. He highlighted that many SAP customers make use of the organization’s predictive analytics platform—Hana, to improve profit and reduce expenses (Bell2013; Fan and Bifet2012). Although in the environment of customer relationship management (CRM), predictive analytics is at an experi- mental phase which explains the reason organizations whose business model are technologically driven, are in the forefront with applying predictive analytics to CRM (Huo et al.2014). In what follows examples of businesses using big data analytics are discussed.

Bigpoint, a games development organization, are utilizing predictive analytics to monetize players and upsurge their revenue by an estimated 10 and 30 % a year (Bell2013). The predictive model of the organization allows them to make clever real-time decisions about a player’s actions. For instance, it acts as trigger for the predictive system to analyze prior gaming behavior when a player’s ‘ship’ is damaged. If applicable, a custom-made context-related note offers the player a new

‘ship’for a minimal fee (Yan 2013).

Her Majesty’s Revenue and Customs (HMRC) has framed a predictive analytics platform called‘Adept’keeping in mind the end goal to build debt collection and risk estimation. Adept incorporates analytics into the debt administration process with a specific end goal to development debt gathering interruptions for deferred tax payments on a yearly premise. HMRC’s systems are integrated with ‘Adept’ utilizing predictive modelling techniques to inform a comprehensive risk and behavior-driven collection tactics. Remarkably the technology also recognizes diverse types of debtor categories and then targets its communications to these categories through particular attributes. By March 2015, it is estimated that HMRC will have the capacity to collect an extra£3bn of debt (HM Revenue and Customs

2013).

In the financial services sector, predictive analytics is used to improve the methods of debt collection. Various organizations are attempting to identify that a certain type of customers responds undesirably to payment prompts, while some will make timely payments without reminders; others tend delay payment when prompted. By means of recognizing these behavioral tendencies, business organi- zations are able to enhance their processes as well as effective customer relations. In the retail segment, Tesco is building on its reputation for new technologies to enhance CRM. Tesco has started a scheme, which offers suggestions to customers about products related to their purchases in real time. A‘loyalty card’is injected into a device attached to a shopping trolley, through which personalized sugges- tions are presented to the customer during thefinal buying process. For instance, a customer may place an electronic shaving clipper in the shopping trolley: as a result he receives a 2-for-1 offer related to the item, such as batteries for clippers. With the availability of data related to the customer’s identity as well as their purchases, Tesco is making an effort to forecast their next actions and then provide inter- vention mechanisms showing related offers.