Strategic Analysis and Operations in a Global Economy: Big Data Strategic Analysis and Operations in a Global Economy

How does Big Data affect your everyday decision making process?

Big data is a common term used for describing large data-whether structured and unstructured-that overwhelms a business routinely. The amount of data, however, is not the most important thing. What matters is the way organizations use data. Big data can provide insights that lead to strategic business moves and better decisions. Though the term big data is of recent origin, the gathering and storing of large volume of information for ultimate evaluation has been around for ages. In the early 2000s, the concept gained momentum when industry analyst Doug Laney expressed the now-mainstream meaning of big data to represent the three Vs: Volume, Velocity, and Variety. The significance of big data does not revolve around the amount of data at disposal, rather its deployment towards beneficial ends. You can retrieve data from any source of your choice and carry out analysis to discover answers that enable you 1) reduce cost, 2) reduce time, 3) new product development and optimized offerings, and 4) smart decision making. When big data is combined with high-powered analytics, you can accomplish any business-related task (SAS Institute Incorporated, 2016).

Anecdotally, most people in the marketing community have inadequate knowledge about big data and cannot make informed decisions of its impact on their day-to-day lives. It appears to be inaccessible and complex in the area of the elusive quants, which for most, is out of reach.

Nevertheless, new developments in the marketing landscape will soon change the way things are done; there is an ongoing race for the implementation of a new data-centric marketing model, one in which decisions are made based on quantitative data as against the traditional qualitative evaluations, such as gut-feeling and intuition. Marketing is fast abandoning its artistic roots and fast transforming into a type of science; incorporating mathematics, technology, statistics, psychology and neuroscience (Raghallaigh, 2015).

Lack of boardroom credibility remains one of the largest challenges marketing managers face today, with about 73% of CEOs reporting a serious lack of trust in the ability of the marketing department to generate any sales and increase the conversion of customers, demand and market share (The Fournaise Marketing Group, 2011). In the last few years, however, there has been an agreement among marketing researchers that make use of data to drive marketing decisions that can change the perception of marketing from a cost-center to a value generating profit-center.

Retailers were initially off the mark in their bid to accept an insightful decision-making technique; which some large organizations like Tesco pursued assertively, gathering and assessing customer data to gain deeper insights into what they need -- using integrated customer, behavioral and shopping data to make predictions on future buying trends (Savitz, 2012).

They are equally exploiting big data volumes in a bid to target customers better to gain more loyalty, in order to personalize their campaigns, offers and coupons to every customer.

The ancient spray and spray method is now relegated to marketing annals, replaced instead, with the aim and claim method facilitated by Big Data. This paradigm shift in marketing decision-making processes has also given rise to a change in competitive setting; which drives competitive advantage for tech-savvy companies that are digitally enabled who have embraced the data-centric business idea. Another major marketing aspect being changed by Big Data is the study of how social networks can be made a part of the marketing practice and customer relationship management (Raghallaigh, 2015).

Do you belong to a profession that relies on Big Data?

No

Should it incorporate it into operational strategy formulation?

Yes, for the following reasons;

Big data expands customer intelligence

From the point-of-view of customers, this is a simple question: "You don't remember me?" For any organization that has several hundreds of millions of customers, that recognition hardly happens. That too is changing. It is easy for the organization to use Big Data to identify its key customers today and tomorrow. This can be done by penalizing the customers who patronize infrequently. Big Data looks at different sources that involve structured information like the history of purchases, customer relations management (CRM) data, and astuteness from industry allies, as well as some unstructured information like social media.

If it is an airline company, the allies could include hotels, credit card companies, and travel agencies. Big data evaluation is also known to bring unstructured data into the whole scene, information retrieved from social media...

Data generated in the field often create the feedback in the loop, and grows at a pace that cannot be easily comprehended. Sensors On one commercial aircraft can generate about 20 terabytes of data per hour. Automobiles report data collected from onboard sensors and dealer service structures. Add to that the growing tide of RFID equipped automobiles, packages and crates. These unbelievable repositories of data, joined with machine-to-machine relations, are fueling an entirely new wave prognostic analytics, services that enable equipment like airplanes to establish their very own maintenance schedule, keeping the supply chain at alert to make sure the required parts get to where they are needed when they are needed. Big data moves from the realm of data scientists into daily business encounters and transactions.
Big data + mobile means new business processes

As companies get more data-driven, it becomes more natural for those insights to come into the hands of people who have what it takes to put them into good use.

Mobility will highlight the impact of big data on both the customer intelligence and operational efficiency by making all things instantly actionable.

With the instant decision-making capability and intelligence on your mobile phone, you can implement new business procedures that changes the way business functions.

No time to lose -- big data and analytics go "as a service"

Developing an internal big data department that has a stack of petabytes of storage, rows of blade servers and a large stack of data is within reach or a desired main proficiency of all companies.

That is where both data and analytics offered as a service will help. Companies of different sizes can use big data by sharing a pool or resources and scientists, putting the skills they need without going beyond their main capabilities or implementing a big fixed expense (Reddi, 2016).

Do you informally incorporate it into your own individual management strategies?

No.

Why not?

Big data has been so heavily overestimated that my potentials will be high and might not deliver more value than it can. Additionally, analytics-generated insights are replicated easily.

The cost of making use of big data is often higher than the usual methods. It involves companies who want to invest in data scientists, data warehouse, and data analytics software. Even with these, most of them have little or nothing to show for their efforts (Ross, Beath, & Quaadgras, 2013).

Think of the movie, Moneyball. In your everyday world of work, which character do you identify with most?

None.

In an ideal world, which one would you like to be identified with? Why?

Billy Beane

Why?

He displays determination by holding on to his story. He represents a man who was bitten by failure when he was still young and victimized for his inability to live up to the dreams and expectations set for him by others (Brennan, 2011). His Moneyball theory is a set of statistics he created to get a deeper sense of player's talents. His main goal is to build a championship team that can be affordable for the As. Once Billy fails to meet this target, he will be fired. Billy has come across other obstacles before this one. Before he became the general manager of the A's, Billy had been a professional player. He was among the top prospects in a baseball game. According to scouts, "Billy was what you could call 'a can't miss' prospect, who had every tool he required to hit stardom." During the draft of the 1980, New York Mets chose Billy in the first round. The Mets gave him a large amount of money to play professional baseball with the New York Mets. The major issue was that Billy was already committed to Stanford University on a fulltime scholarship. Eventually, Billy settled for professional baseball and consequently barred from Stanford. In the book, Billy notes that he took this decision due to his financial state. What made his decision a bad one was that his baseball career was a failure. Billy only had a high school diploma to work with. If he is to lose his job as the general manager, finding a new job will not be easy for Billy. This decision was mentioned several times in the book. It is Billy's motivation to work very hard without giving up (bbmoneyball, n.d.).

References bbmoneyball. (n.d.). Character Analysis. Retrieved from BBMoneyBall: http://bbmoneyball.blogspot.com.ng/p/character-analysis.html

Brennan, T. (2011, September 23). Moneyball. Retrieved from Dark Horizons: http://www.darkhorizons.com/reviews/1207/moneyball

Raghallaigh, E. O. (2015, June 8). Big Data…