In business analysis, size matters – El Financiero

In previous articles, I have explained that business analytics should not be a fashionable topic for organizations, but rather a discipline that must be included in their daily organizational life and that talent is also essential in creating of the analytical strategy, its execution, its adoption and its implementation. and reading this recipe, it seems easy to make it, however, the reality is completely different; But what’s the difference between established analytics companies and those that are in a nascent stage or perhaps haven’t even considered putting in the effort to begin their professionalization of analytics?

Answering the above question is complex because, in the first place, there are countries that are more mature in the adoption of analytics, not only in the commercial domain but also in the academic domain where there is a greater training offer for data scientists. After working with companies across different industries, sectors, sizes, and leadership styles, analytics behavior and maturity patterns appear to be tied to company size; In other words, size matters: transnational companies, present in various regions and countries of the world, in general tend to be more open to the adoption and successful execution of an analytical strategy than family companies. countries with a smaller team of collaborators and a limited presence.

But it’s important to note that company size has associated variables that really make a difference, such as: the perceived complexity of using and adopting analytics (which includes available technology architecture , the technological diversity of the company, the perceived security in the processing of information and the confidentiality of data). In other words, greater perceived complexity is detrimental to the adoption of the analysis strategy.

A second variable is compatibility, i.e. the degree of consistency of the new system or platform with those already used within the company, which implies that an analysis tool will be more easily adopted if the procedure with which it works and the affinity with the current tools within the company, it’s more organic.

The third variable in question is the support of senior management to adopt the analysis strategy, which is frequently mentioned as the central axis of the success of an analysis strategy, however, it must be reviewed that this support from the C- levels helps create an environment conducive to adoption, but also provides the right resources to adopt analytics in the daily practice of the company and, above all, to do it in an agile way.

The fourth variable relates to the extent to which the environment within the company is conducive to the adoption of analytics, i.e. the ability to access information they did not know not before or were not available to them, the ability to manage new sources of information, in various formats, order and good governance in the management of information and the willingness to create management discipline high quality data.

The fifth variable concerns the readiness of a company to adopt new tools. This is somewhat difficult to measure, as how do you determine if it is ready or not? However, it basically refers to the ability of the business to manage and invest in the adoption of new technology, including the technical capacity and experience of the technology department. provide the analytical tools and the data science team to democratize analytical knowledge and convert it into the business language that its internal contacts need to understand the “insights” and be able to put them into action.

A sixth variable is related to factors external to the organization: competitive pressure, which tends to positively influence the adoption of the analytical strategy since it entails the need to seek new competitive advantages and ways to innovate, requirements that a good analytical strategy makes it possible to solve since with the data we obtain a better understanding of market developments and the generation of “data-driven” proposals.

The seventh variable, also external in nature, refers to the external support that tends to facilitate analytical adoption: the selection of technology providers. While a well-designed analytics strategy tends to be technology agnostic, it is also true that there are technology partners who often have experts with more developed capabilities and greater experience in deploying the analysis strategy, which provide a better guide because they know how to do it and have also capitalized on different success stories, but even better because they have understood that technology is the means, but the solution to the problems of the business and a real impact on its business indicators is the end.

The last variable is based on analytical knowledge management, i.e. the company’s ability to generate knowledge adoption products aligned with business objectives, shared with stakeholders and also in right time.

These eight variables, although they are not a single formula, much less a panacea, it is true that together they tend to make an analytical strategy more successful and to be more likely to be present in mature companies (generally large), who understand the value of ideas; that they have integrated it into their DNA as part of their proposal for differentiation and agile response to the market; those where the “I have other data” is outside the organizational discourse and where the guiding axis of the strategy is analytical solidity, and for these variables to have meaning, it is important that their impact be measured in terms of organizational performance, which involves how much customer satisfaction has increased, market responsiveness relative to competitors, research, innovation and development productivity, and new superiority launches against the competition, to name a few.

Fabiola Vasquez

The author is Doctor of Marketing, Professor in the Department of Marketing and Analysis of the EGADE Business School and the Business School of the Tecnológico de Monterrey; director of customer development and retail analytics; Lecturer and writer.

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