Technological breakthrough especially in social media has on one hand opened far greater information for collecting information and on the other has made it extremely complex for managers to screen and use the relevant data for marketing and advertisement activities. Social media websites like Facebook and Twitter have immense data and the challenge lies in screening the relevant data. The data available on these websites is far more comprehensive than the data garnered through CRM as it provides a rear view mirror of the consumer to the companies.
The data available in these social media sites can be tapped. However, this is not without potential challenges. This data, also referred to as ‘Big Data’ contains both relevant and irrelevant information from the viewpoint of the firm. The challenge lies in streamlining and capturing the relevant 20 percent data and accordingly using it (Boorman, 2011).
Big Data and Managerial Decision Making
Big Data analytics deals with applying sophisticated analytic mechanisms to diverse, large data sets which frequently includes streaming data and diverse types of data. One of the most common confusions arises from the integration of Big Data Analytics with Big Data Storage. However by accurately cashing in from the integration of analytics and storage can help managers to handle Big Data and get rapid information. The information available in the form of Big Data needs to be excellently harnessed and hence the tools used for this purpose go beyond the scope of conventional data warehousing methods (Webster, 2013).
Scholars have already determined the association between competitive analytic values and performance. A survey was conducted in order to understand the manner in which firms try to collect relevant data and apply analytics to meet current and future aspirations.
The survey maintained that top executives and supervisors are more interested in determining rapid manners in which complex data may be rapidly absorbed and accordingly relevant action may be taken. Some steps which managers maintain will help in churning out relevant data from Big Data would be to include process simulation and data visualization both voice and text, reviews of social media and other prescriptive and predictive mechanisms. Such methods help in quick transfer of data into insight and these may be readily employed rather than relying on further interpretation or being uncertain about decisions to be taken regarding such data.
Managers further maintained that the main hindrance is not posed in churning Big Data. One of the main challenges comes in the form of cultural barriers to technology and information. One of the main hindrances is that supervisors have less information on the manner in which analytics may be employed to enhance business and this helps in thwarting their decision making process (LaValle et al. 2011).
In order to facilitate decision making, they should ask accurate questions depending on the circumstances. Right questions help in recognizing particular decisions with respect to analytics and information and thereby drive the favourable impact for business enterprises. The more the data available with managers, the greater are the chances for them to hone consumer behaviour frameworks which in turn provide them with more realistic opinions about risks and opportunities. However, the path to success lies in developing innovative methods to use this vast data available.
Managers should ensure that the reports are simple in nature so as to facilitate decision making despite having an overwhelming amount of Big Data available. Here we come to the context of developing powerful visual interfaces which in turn helps in recognizing potential consumer markets and thereby aiding to take right decisions despite having Big Data (Gordon, Goyal & McGuire, 2012).
Finally, supervisors may employ five general manners in order to facilitate quick decision making despite having an overwhelming load of Big Data. First, the information available can help in potentially tapping critical value by quickly employing and making information transparent. Next, as the firms develop and store transactional information by means of digital mechanisms more detailed and accurate performance data may be garnered ranging from sick days to product inventories thereby aiding in boosting performance. Big Data also permits to narrowly segment customers thereby making it possible to concisely provide customized services and commodities. Also employing sophisticated analytics helps in facilitating complex decision making for superiors. Last, Big Data may be employed by managers in order to enhance the growth of upcoming generation services and products (Manyika et al. 2011).
Thus, Big Data can help managers to gain competitive edge over rival organizations. It is essential for all firms to seriously churn Big Data and gain potential business impact. Using Big Data can further aid managers to take accurate decisions which may lead to surplus consumers and increased productivity and innovation. The forerunners in all organizations need to accurately churn Big Data to derive success by taking the right decisions rather than grappling with the overwhelming chunk of data available. The trick is to know the manner in which Big Data can be used to churn out relevant portion of information and innovatively use them for garnering surplus consumers, new markets which in the end leads to increased productivity and growth of an organization (Manyika et al. 2011).
Boorman, C. (2011). Why data mining is the next frontier for social media marketing. Retrieved November 25, 2013 from http://mashable.com/2011/02/25/data-mining-social-marketing/
Gordon, J., Goyal, M. & McGuire, T. (2012). Big Data and advanced analytics: success stories from the front lines. Retrieved November 25, 2013 from http://www.forbes.com/sites/mckinsey/2012/12/03/big-data-advanced-analytics-success-stories-from-the-front-lines/
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S. & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21 – 31.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. & Buers, A. H. (2011). Big Data: the next frontier for innovation, competition, and productivity. Retrieved November 25, 2013 from http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
Webster, J. (2013). Understanding Big Data analytics. Retrieved November 25, 2013 from http://searchstorage.techtarget.com/feature/Understanding-Big-Data-analytics