What translates big data into business value? Beyond technical and data-related resources, study shows human-related resources and capabilities are more important. Then, how to leverage the human drivers of value creation through big data?
The importance of human-related resources and capabilities
Researchers have recently analysed 125 quantitatives studies on the determinants of value creation through big data. It’s not the first study with that aim but it’s the first to analyse almost all the previous studies and to propose a meta-analysis, which makes the interest of it.
Analysing these studies, Osterreich, Anton and Teuteberg identified 6 drivers which determine the performance improvement:
- Business Analytics ressources and capabilities: technical resources, human resources, management capabilities, data quality
- Contextual factors: organizational culture, external pressure
Then they analysed the quantitative results of the 125 studies and their main conclusions are the following:
- overall, business analytics have positive impact on all firm’s performance dimensions (operational, financial, and market performance),
- social aspects such as human resources, management capabilities, and organizational culture play a major role in the business value creation process,
- technological factors, such as the technical assets and data quality, are less important,
- the contextual factor external pressure has been shown to have only a moderate impact on firm performance.
Technical capabilities constitute imitable and thus outsourceable noncore resources, whereas the ability to interpret data insights as well as make decisions on the basis of such insights are core internal capabilities that create value.
Additionnally, firms can achieve higher than their competitors’ performance when they develop capabilities that combine technical and social assets.
Though essential prerequisite of success, investments in technical assets alone do not enable firms to create value.
Actions to leverage the human-related resources and capabilities
Personnel and management capabilities cannot be easily imitated and are, therefore, an important source of competitive advantage. Three types of human-related resources and capabilities are identified in the research: human resources, management capabilities, and organizational culture.
In terms of human resources, a lot of the possible actions refer to skills availability. It would include to recruit new staff equiped with these critical skills, like carmakers did when they massively recruited data profiles to level up their game in analytics. It also involves training non-data profiles to the opportunities and challenges associated with analytics as well as training them on analytics software so they can use them in their job. This is a significant effort for large corporations.
These efforts in skills are necessary (and costly) but they are not sufficient. Even the more talented and skilled team can be unproductive if the processes and capabilities do not evolve to leverage this upskilling.
In terms of management capablities, a critical action is to involve analytics in the decision making process. Not only to contribute to making a decision (e.g. changing a price, order a product, …) but also to automate some decisions, identify new decisions to be made or change the frequency of the decision making process.
Analytics is often about clustering and predicting. It’s already quite a challenge in some context but even if a team succeeds, absolutely no value comes directly from this segmentation or prediction. The value is derived from a response to the signal emitted by this information. If a bank is very good at predicting which clients will leave to the competition but has no course of action to keep them, the value of the data, the architecture and the human talents is near zero. Then, the ability to respond to the signal is key.
Last, synchronizing tech and human distinctive capabilities appears to be a strong source of competitive advantage. A lot of companies have developped an advantage because they cultivated distinctive talent pool (think about the design teams at Apple, the designers at Luxury brands, the analysts at Goldman Sachs). With big data, a part of their job can be supported or, even more, automated. The ability to combine a big data expertise and a distinctive human resource base or process is much harder to imitate, then is a stronger source for competitive advantage. It requires to redefine the processes and the contribution of both assets.
Defining a data-driven culture is a hard challenge. However, we can list some components or attributes of such culture. Some are related to the individuals. For example, the level of data litteracy accross the organisation. Others are related to data governance, for example a unified governance body and policy for all data in the company.
The maturity regarding data is also a critical feature of a data-diven culture. Are data considered as an asset whose quality must me assessed and maintained? In terms of decision making, are the options identified and discussed through a data analysis or mainly through individual past experiences and opinions? Are past decisions results analysed with data?
Last, one of the main feature of a data-driven culture is collaboration. In order to seize the opportunities offered by big data, teams must collaborate. It’s true accross the divisions or businesses of a company as some opportunities would require to leverage or change a broad scope. It’s true also accross the functions as some opportunities in one function would require data produced or maintained by another one. A company which does not foster collaboration inside the company has a low chance to take advantage of big data technical and human assets.