Using Data Analytics for Business Growth

Introduction

Exponential growth in technology has led to the immense growth of data being produced. Over 2.5 quintillion bytes of data is being produced per day and the same is expected to grow at a much faster pace in the coming future (Marr, 2019). Almost every human being in some capacity is linked to Social media platforms like Twitter, Facebook, etc. along with devices like smartphones, smartwatches, Internet of thing devices. All these combined with strong internet bandwidth capabilities are the reasons for data big bang. To survive in the highly dynamic competitive environment where technology and processes are accessible to all, Organizations are adopting analytics to extract data power with the hope not just to survive but to gain an edge in the market. Though only a small number of organizations, around 20% as per the survey by Mckinsey (Lizaso Miranda, 2018), are successfully able to harness benefits of advance analytics for generating a scalable or countable effect. Deeper dive into the failure cases revealed that insights or outcomes from the analytics never made its way to influence the decisions, which disrupts the sole purpose of analytics. I believe, the end goal of analytics is to influence decisions, as also pointed by Davenport and Harris (2006) – “we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (p20). Abundant artifacts are present and few of them are discussed in the following paragraphs confirming that analytics is a “People Business”, i.e. how organization leaders shaped their decisions via analytics.

Analysis

No introduction is required for Google, it has excelled in almost all the areas it has stepped in, which include internet-related services and products. It is counted among the big four technology companies in the world. Analytics is embedded in Googlers’ (what Google calls its employees) DNA right from the top leaders till bottom. Any decision made in the organization is purely based on facts and data-driven approach which has carved its way to be top of the technology pyramid. Let’s discuss an example from their HR function which helped them in deciding the policies, which ultimately led to retaining the best talent. Within HR function they created a people analytics department to answer the question: Do managers matter? They took the existing data of employees’ survey and performed the analysis which resulted in the finding that the managers are divided into groups: good managers and not good managers. Employees under good managers were performing well and have a better chance to stay. Once the importance of the managerial role is confirmed they moved onto the next query that is “what makes a good manager?”. To start with this, the people analytical team implemented two new data collections. One is “Great Managers award” where employees can nominate their manager based on certain behavioral aspects and the second is data from interviewing the managers. This helped Google to answer the top 8 behaviors of good managers and 3 causes why managers are not being counted as the good one (Marr, 2012). Google leadership utilized these finding to lay down the expectations from managers. Measurable actions were taken which plays a part in shaping Google into what it is today. This emphasizes how the purpose of analytics is achieved.

Companies like Coca Cola, Netflix, Target, Marriott relies heavily on the data-driven decisions to remain a step ahead from their competitors (Bruzzese, 2019). Amazon uses customers personal data and purchases history to predict and show ads and recommendations. Netflix records the reviews and behavior of its customer to recommend the movie and deciding what genre of series to prepare which can earn better viewership. Marriott records the behavior and routine of its guests and results obtained from analyses them are included in deciding preferences for customers. Coca-Cola records the customer data and feeds which it analyses for providing a better experience and enhancing customer loyalty. All these companies utilized the analytics to make decisions.

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Contradictions do exist marking doubts on the analytics role in decision making as can be seen from the survey conducted in 2014 by Fortune Knowledge group with 720 senior executives, 88% of whom are the director level or higher in the firms with more than $500 million of annual revenues.62% of these participants contended that their decisions are more based on their previous experiences and “gut feelings”  more so than results or findings of analysis. (Fortune Knowledge group, 2014). This does raise questions on the purpose of analytics existence for decision making. But there could be multiple reasons that led to leaders and executives believing more in the gut feelings or experience.  One could be the lack of setting up proper analytical structure in the organization or unawareness how proper data-driven decision making can take their organizations to a new high. Analytics is an iterative process and it may take some time to draw insights for answering the question or providing direction. Also, the gut feelings and experience used by these leaders could be driven by past events which in itself can be thought of as an analysis of those events.

                            Conclusion

Though events have been recorded when Analytics role has been doubted or judged incorrectly, as many of the organizations implementing analytics are naïve to absorbing the results or purpose of the analytics. But I strongly believe that maturity on the data-driven approaches in decisions is making its way into the system. Organizations which can understand the end goal of analytics are able to make noticeable differences in terms of better positioning and being future ready. These organizations can be used as referential point on how to adopt advance analytics for data driven decision making by the ones starving to make a mark in industry.

References  

• Bernard Marr (2019), How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Retrieved from: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#41ad3fbd60ba
• Gloria Macias-Lizaso Miranda (2018, October), Building an effective analytics organization. Retrieved from: https://www.mckinsey.com/industries/financial-services/our-insights/building-an-effective-analytics-organization
• Thomas H Davenport, Jeanne G. Harris. (2006). Competing on Analysis: The New Science of Winning (1st ed.). Harvard Business School Press
• Fortune Knowledge group (2014). Only Human: The Emotional Logic of Business Decisions. Retrieved from: https://www.gyro.com/onlyhuman/
• Bernard Marr (2012, November 17), Analytics at Google: Great Example of Data-Driven Decision-Making. Retrieved from: https://www.smartdatacollective.com/analytics-google-great-example-data-driven-decision-making/
• Anita Bruzzese (2019, March 11): A Look at Real-World Data Analytics Success Stories. Retrieved from: https://blog.storagecraft.com/real-data-analytics-success-stories/