I have recently published a book chapter titled, “Big Data and Entrepreneurship” in the Handbook of Media Management and Business. The chapter helps students, industry professionals, and researchers understand big data analytics and the role of data scientists in media management and entrepreneurship. It also brings to light the opportunities and challenges brought by data and analytics.
Media managers should use the following Action Plan as a guide as they develop a big data strategy.
Action Plan:
- Hire and train skilled data scientists who understand the company goals.
- Never break audience trust for the sake of obtaining data. Be transparent about how you are going to use data and user information to increase revenue. Protect the privacy and security of your audience.
- Use big data during almost every media production and post-production process.
- Remember to also rely on qualitative and hybrid methodologies to better understand why your audience may be behaving in certain ways.
Key Takeaways:
- Big data is best characterized by a huge volume of frequently updated data in various formats, including numeric, textual, and images/videos.
- The application of big data has proven a major disruptor in today’s media marketplace, especially in the music, film, and advertising industries.
- Media managers are able to use big data to better understand audience behavior and better connect them to their product.
- Many key challenges still exist in big data, including extracting value data, the rapid spread of misinformation, and privacy/security concerns of audiences.
The chapter offers the following conclusion:
Data and analytics lie at the heart of the digital revolution. Capitalizing on data and leveraging the power of analytics for entrepreneurship and various other hinges on a carefully planned and sustained effort. Big data is already an integral element of the overall business strategy for many media organizations, and it is expected to become even more important for managers and various types and size of business in an increasingly competitive and convergent environment. It is believed that by the end of 2020, the big data volume is expected to surpass 44 trillion gigabytes or 44 zettabytes (EMC, 2014). This indicates a major challenge in terms of data volume and complexity, but also an opportunity that needs to be seized.
Here we can unequivocally see time and speed as the two integral components of big data. In order to turn big data into something useful for media businesses, analytics must be carried out swiftly so that these data can be efficaciously categorized and structured. Media organizations that do not try to stay on top of their analysis of these data might occupy a disadvantaged position. Davenport (2014) highlights the possibility of big data to introduce media organizations with more information and materials about how customers react and behave toward certain products, therefore leading to the proliferation of advertisements, products, services that are customized and created for particular segments of these customers.
While technology allows media organizations to gather more data, more attention should be paid toward how entrepreneurs adapt to a big-data environment and how they make sense of and structure big data. Such a change that embraces the big data analytics requires “considerable imagination, courage, and commitment” as essential entrepreneurial characteristics (Davenport, 2014). Within this context, one can understand the interplay of various and disparate factors that can work together to make the best out of big data. What make big data appear appealing to businesses, corporations, and organizations stems from the notion that big data can reduce cost as well as contribute to the development of new ways to improve data gathering and collection.
While many companies have embraced big data and analytics as part of their strategic mix, a large number still lag behind in full utilization of the data advantage. It is clear that big data analytics enables informed decision making. What is required is the realization of the importance to cultivate a culture that values data. The advent of cloud-based computing has lowered the barriers to the adoption of big data analytics. This has certainly opened up more opportunities especially for small and medium-sized entrepreneurial ventures as they too can embrace analytics technologies to their advantage.
It is worth remembering that in the globally competitive world only the smartest would survive. Being smart implies that companies and organizations are agile, embrace changes, and inculcate newer solutions that help them make informed decisions in a timely manner. Since data is being constantly generated, opportunities also continually expand.
Media scholars are beginning to incorporate big data into their own academic research. Results are being met with a combination of enthusiasm and skepticism. On one end of the spectrum, it is finally possible for the average researcher to deal with datasets that are affordable and include a representative sample. One the other end, new waves of research illustrates that data size really doesn’t matter at all (Davenport, 2014). Instead, it matters much more what you do in your analysis. Academics must be careful not to rely solely on big data, especially those generated on social media. It must be carefully considered which populations are included and excluded from these measurements. However, as more Ph.D. programs train future data scientists in big data measurements, the results should only improve.
Big data has proven itself as one of the biggest drivers of success in today’s convergent environment. Like most things, we must be cautious that just because something is new, it does that mean that it is better. The next chapter will explore the best way to merge “new” concepts and trends in media management with more traditional “old” foundations.
You can cite the book chapter as follows:
Khan, M. Laeeq (2020). Big Data and Entrepreneurship. In L. M. Mahoney & T. Tang (Eds.), Handbook of Media Management and Business (Volume 2, pp. 391-406). Rowman & Littlefield. ISBN-13 : 978-1538115305
You can download a copy of the complete book chapter here: Download PDF
Laeeq Khan, Ph.D. 