Antonio Pita Lozano

The selection of use cases is a common approach to the data-driven transformation of companies, but in most cases these use cases do not achieve the expected result, generating disappointment and slowing down the necessary transformation. In some cases, we find ourselves with failures that dynamite the entire transformational effort of the company. For this reason, I always recommend carrying out an analysis after each case to help us identify possible areas for improvement that will allow us to improve the results of the following use cases. Pillars of a data-driven organization After analysing many use cases implemented in different companies, we can identify the main cause of their failure, which is none other than the poor management of expectations, but if we delve a little deeper into the underlying reasons, we find that most of them are caused by an inadequate alignment of the use case to the state of maturity of the data-driven pillars of the organisation, which if we remember are three: technology, talent and organisation and culture. Technology: the technologies implemented in the company must be identified and used to solve the use case. If the use case requires a new technology for the company, it is desirable to carry out the process in two steps, first we introduce the technology with another simple use case, known and easy to implement. Then we approach our analytical use case with the technology, as long as we have sufficient experience. It is important to remember that to approach an analytical use case with a new technology is to increase the risk of failure to the maximum. Talent: there should be an internal capability map covering all the actions to be performed in the use case. Otherwise, new capabilities should be brought into the company internally or externally and tested in another simple, known and easy to evaluate use case to build professional confidence among all team members. If we identify a problem in a new case that is being performed by a new team, mistrust will arise among the members. Organisation and culture: it is necessary to ensure that the company is prepared to be able to put the knowledge extracted from the data to good use by having the right processes and culture in place. It must be remembered that everything new generates reluctance and even more so when it is not perfectly understood, especially when we know that all the models we generate will fail in specific cases. Company's data-driven pillars will dictate which use cases will be a success and which will be a failure. Use case selection If the organisation is in an incipient stage of its data-driven journey, it is advisable to select simple use cases that require easy or non-existent technological implementation and that affect the smallest number of functional processes. In addition, it would be convenient to select from among those that have been successful in other companies in the sector. On the other hand, if the degree of maturity of the pillars of the data-driven organisation is high, more innovative and risky use cases can be chosen, as the company will assume and reward this risk. AI OF THINGS AI in Advertising: Prediciting Trends or Reinforcing Stereotypes? August 12, 2019 Sometimes we find companies with heterogeneous maturity levels, for example, strong in technology and talent, but weak in cultural transformation. In these cases, it is essential to identify the strengths and select the use cases that are based on these strengths and reject those that are based on the other pillars that are less developed or evolved. If you want to succeed in developing analytical use cases and become a data-driven company, remember to align the use cases with the maturity of the company's data-driven pillars. ■ Originally published in the book "A Data-Driven Company" by Dr. Richard Benjamins. If you want to know more about the book, I recommend a previous post.

Written by Antonio Pita Lozano, Operations & Solutions Director at LUCA Consulting Analytics. Head of Big Data Science Data economy, Big Data, Machine Learning, Artificial Intelligence or Internet of Things are some of the topics that are increasingly featured on the steering committee. But not only do these words appear in top management circles, but also with new divs. This is the case of the Chief Data Officer, a newly created profile in many companies. The Chief Data Officer (CDO) is responsible for the data of a company at the highest level, both from a technological and business point of view, including security. They help with managing the data as a corporate asset; among its functions is the strategy for data exploitation and data governance; that is, defining security policies in the management and storage of data, privacy policies, as well as keeping up with the latest developments in regulatory issues that are set by the country, or in this case, the European Union. The versatility of the CDO is opening a debate on the role that they should occupy within the organizational structure of the company. Theoretically, all companies should have a CDO since nowadays all companies use data, but it is not mandatory to have this div at regulatory level. In SMEs this div does not usually exist and falls to the business director or IT director. In contrast, all large organizations already have a CDO. For those companies that have decided to incorporate the role of the CDO, there are various currents about the position it should hold within the organization. In some cases the profile of the CDO is ICT, in those cases the focus is on the processes and the quality of the data. In other companies, the CDO has a business profile, in these cases the attention goes to use cases and application of data. But, where is the best option to place the Chief Data Officer (CDO) in the organizational structure of the company? We should start from the basis that the transformation of companies is horizontal as it affects all business units and where data is the most valuable asset in the evolution of any business model, the current IT-business binomial must end and we must accommodate to the trinomial business-science-technology, with the data as the basis of knowledge. Thus, the profile of the CDO must be hybrid: technological, business and with regulatory knowledge, a profile that is very versatile at business level. That is why the CDO is usually chosen among the profiles that already work within the company, given the depth of knowledge of the business that they should have. The Chief Data Officer (CDO): the new bridge between IT and business The CDO must act as the bridge between business and technology. Regardless of the ‘background’ of the CDO, its role is always to be the guarantor of the unique truth of the data, ensuring that there is no inconsistency in this regard. For this, it is usual for the CDO to have a technical team and a functional team that knows the business. Within its team, the Data Steward is key, responsible for implementing all the policies promoted by the CDO in each of the company’s organizational units. In addition, the CDO must have an expert team in advanced analytics and an Analytical Center of Excellence to extract knowledge from the data and make it available to the entire firm. The CDO has become the person in charge of the data governance of the company, and they must act as a bridge between business and technology. They should decide what data is used, when it is used and for what purpose, validate the technologies used, to assure and agree on the traceability of the data to be aware of any transformation suffered and all the users that make use of them, as well as the business application that has the use of data. Gradually, this profile is gaining weight within the organization and in some cases they are already part of the Board of Directors, but without any doubt the capacity of the CDO as a ‘translator’ between the business and technology areas places him in a global position with Direct line of reporting to the CEO. You can also follow us on Twitter, YouTube and LinkedIn