How Much Data do You Need to Create a Data-Driven Culture?
We live in a world flooded by data! There is nothing new in this sentence and — perhaps — you are bored with this claim. Renowned business magazines had discussed this subject on its covers. It is impossible to forget the cover of The Economist in 2010 with the heading: “The Data Deluge”. Also, the Harvard Business Review which shows the “Getting Control of Big Data” headline in 2021.
Eleven years after, companies from diverse markets yet feel confused about how to be profit from data. However, revolutions — as it is has been remarked from diverse researchers — never go backward. Neither, do they advance at a constant rate. Considering the immense transformation unleashed by data analytics, by now, it is clear-cut the data revolution is changing businesses and industries in profound and unalterable ways.
But the changes are neither uniform nor linear, and companies’ data-analytics efforts are all over the map. A McKinsey research suggests that the gap between leaders and laggards in adopting analytics, within and among industry sectors, is growing. Some companies are doing amazing things; some are still struggling with the basics; and some are feeling downright overwhelmed, with executives and members of the rank and file questioning the return on data initiatives (Díaz, Rowshankish, & Saleh, 2018).
“The emergence of data analytics as an omnipresent reality of modern organizational life means that a healthy data culture is becoming increasingly important”. But to follow a data-driven culture it is imperative that companies worldwide know what big data analytics means and its impact for business (Díaz, Rowshankish, & Saleh, 2018).
Big data analytics is the process of analyzing a vast volume of structured and unstructured data created globally to uncover hidden patterns or unknown corrections. Through big data analytics, the companies can apply a narrower segmentation of customers, develop innovative products, develop more precisely customized products, minimize risks, and improving decision making.
However, there are three main characteristics of big data analytics: the data itself, the analytics of the data, and the presentation of the results of the analytics (IDC, 2012). The results of big data analytics provides better business prediction and decision making. So, big data analytics can be used by diverse market segmentation and in many areas including: (1) the analysis of customer segmentation and behaviors, (2) optimized marketing campaigns, (3) identification of data driven products, (4) defining marketing strategies, (5) risk management, (6) the analysis of social network and relationships, (7) detections and prevention of fraud, and (8) attrition predictions (Information Resources Management Association, 2016).
Up to this moment you may have realized that this article does not specified a minimum amount to define a big data. It occurs because that fact does not matter in the big data universe. Any data professionals know that big data are not related to volume. In this aspect, “Size doesn’t matter” (Davenport, 2014).
“Big data refers to data that is too big to fit on a single server, too unstructured to fit into a row-and-column database, or too continuously flowing to fit into a static data warehouse. While its size receives all the attention, the most difficult aspect of big data really involves its lack of structure.” (Davenport, 2014)
Therefore, in a data-driven culture what really matters is how companies run their data than the storage data volume for an organization. A new report developed by Melbourne Business School and A.T. Kearney has highlighted benefits afforded by intelligent use of corporate data. The survey realized with C-suite-level decision makers at over 350 companies across the world, nearly 50 countries and 27 industries quantified the relationship between analytics maturity and corporate profitability. The result makes clear that “investing in analytics can yield substantial returns”. Companies tagged like leaders in analytics by the survey have a 9% average of profit attributable to analytics (A.T. Kearney and Melbourne Business School, 2019). A study developed by McKinsey & Company showed that consumer-goods industry with high analytics performance achieved compound annual growth rate (CAGR) for the total shareholder returns (TSR) of 10.2% between 2010 and 2018. This value is approximately 60% higher than the low analytics performance companies (Halbardier, Henstorf, Levin, & Rosales, 2020).
Although big data analytics advantages are well-known and widespread, “only 3% of the potentially useful data is tagged, and even less is analyzed” (IDC, 2012). A large number of experts’ advocate that the smallest issue of big data analytics is related to technology. For example, a McKinsey & Company study listed the four most common mistakes that hinder organizations from capturing value at scale from analytics (Halbardier, Henstorf, Levin, & Rosales, 2020):
1. Neglecting to connect digital and analytics programs to the enterprise strategy: Laggards companies tend to treat digital and analytics efforts as side projects rather than important enablers of enterprise-wide priorities.
2. Making big investments prematurely: Some companies do strong investments in the latest technology before they understand the real needs of the business.
3. Waiting for “perfect” hires: Generally, ‘laggards’ companies spend as much as six months searching for two or three data scientists or wait until they feel they have found the “perfect” hire to lead the team. On the contrary, perhaps the good way is to invest in training internal talent, disaggregating roles, or partnering for new capabilities.
4. Underinvesting in change management: As usual, executives often invest money and energy as much or more on change management as they did on technology. The good balance should be 25% on data, 25% on technology, and 50% on change management.
Diverse well-known authors highlight how important a data strategy is for business success. They remember that before, data was crucial in only a few back processes, such as payroll and accounting. Today, they are central to any business, and the importance of managing them strategically is growing. In a must-read article, Leandro DellaMulle and Thomas H. Davenport affirm there is no escaping the implications. Companies that have not yet built a data strategy and strong data management area need to recover much as quickly as possible or start planning their exit from the market (DalleMule & Davenport, 2017).
For companies which decided to include their boats in the data ocean, the data culture works like a lighthouse. A McKinsey research interviewed executives who are at the data-culture fore. The research observed that none of these leaders thinks they have got data culture “solved,” nor do they think that there’s a finish line. But they do convey a palpable sense of momentum. They believe when an organization make progress on data culture, it strengthens the nuts and bolts of an analytic enterprise. McKinsey focused on seven takeaways (Díaz, Rowshankish, & Saleh, 2018):
(1) Do not approach data analysis as a cool “science experiment” or an exercise in amassing data for data’s sake. The fundamental objective in collecting, analyzing, and deploying data is to make better decisions. (2) Commitment from the CEO and the board is essential. But that commitment must be manifested by more than occasional high-level pronouncements; there must be an ongoing, informed conversation with top decision makers and those who lead data initiatives throughout the organization. (3) Get data in front of people and they get excited. But building cool experiments or imposing tools top-down does not cut it. To create a competitive advantage, stimulate demand for data from the grass roots. (4) An effective data culture puts risk at its core — a “yin and yang” of your value proposition. Although companies must identify their “red lines” and honor them, risk management should operate as a smart accelerator, by introducing analytics into key processes and interactions in a responsible manner. (5) The board and the CEO raise the data clarion, and the people on the front lines take up the call. But to really ensure buy-in, someone’s got to lead the charge. That requires people who can bridge both worlds — data science and on-the-ground operations. And usually, the most effective change agents are not digital natives. (6) There’s increasing buzz about a coming shift to ecosystems, with the assumption that far greater value will be delivered to customers by assembling a breadth of the best data and analytics assets available in the market rather than by creating everything in-house. Yet data leaders are building cultures that see data as the “crown jewel” asset, and data analytics is treated as both proprietary and a source of competitive advantage in a more interconnected world. (7) The competition for data talent is unrelenting. But there’s another element at play: integrating the right talent for your data culture. That calls for striking the appropriate balance for your institution between injecting new employees and transforming existing ones. Take a broader view in sourcing and a sharper look at the skills your data team requires (exhibit).
Conclusion
Most important advice in the data world is not about the data itself nor just about the analytics. It is about people! When an enterprise decides to start a data-driven culture they must kickoff with their employees. The Chiefs Executive Officer should be a custodian e enthusiast for the data culture. The Human Resources Department needs to seek the right professional, among its talents, for the right position. A data-driven culture is to have a look at yourself and not the market. Data-driven companies define their own ideal data size for their analysis. How much data do you need to create a data-driven culture? How much your employees think will to be sufficient.
References
Davenport, T. H. (2014). Big Data at Work. Boston: Harvard Business Review Press.
Díaz, A., Rowshankish, K., & Saleh, T. (2018, September). Why data culture matters. McKinsey.
Halbardier, F., Henstorf, B., Levin, R., & Rosales, A. (2020). Solving the Digital and Analytics Scale-up Challenge in Consumer Goods. McKinsey & Company.
IDC. (2012). The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. Retrieved May 30, 2020, from https://www.emc.com/leadership/digital-universe/2012iview/big-data-2020.htm
Statista. (2018). Volume of data/information created worldwide from 2010 to 2025 (in zetabytes) . Retrieved Jun 01, 2020, from https://www-statista-com.rlib.pace.edu/statistics/871513/worldwide-data-created/
