“Data-driven” can imply many things. One of the most basic ones is that data is considered as a strategic asset that needs to be managed as such, and not as an operational “exhaust” of a business process. It therefore needs a dedicated department responsible for its management: a chief data officer, just like a chief financial officer for the financial assets. But the data in itself does not create value: you need Artificial Intelligence or advanced analytics to create this. In a data-driven company the important decisions are taken, based on information generated from data in addition to expertise, experience, and intuition.
But why is becoming data driven so important? As of 2021, there is no disagreement that there are huge business opportunities thanks to Big Data and Artificial Intelligence. Possibilities are coming from generating more revenues (doing more with the same), reduce costs (do the same with less), or even create completely new businesses. Moreover, all companies will do this, so if you don’t, you’re out. And apart from business opportunities, there are also huge social opportunities. Think about better monitoring or achieving the sustainable development goals of the United Nations, fighting COVID-19 or natural disasters caused by climate change, or alleviating forced migration consequences.
And yes, Big Data and Artificial Intelligence is not only for the private sector. It is equally applicable to governments and public administration. It is therefore no surprise that governments around the globe have included these technologies in their technological, educational, economical and sustainability programs, among others. Also, few citizens would disagree with the need for more evidence-based policymaking and less voting-based policymaking. In this sense, the new European Data Strategy which also covers business-to-government data sharing is of utmost importance. Public administrations and governments, however, do have some extra challenges when we speak about data and Artificial Intelligence. Firstly, overall, public bodies lag behind the private sector in digital transformation. And without digital transformation, you cannot start a data journey. Secondly, from a data culture perspective, not all public decision-makers embrace data “by default” as a source for taking decisions. Data is the cornerstone for providing transparency, and sometimes data might show things that are against certain political agendas.
Concrete and practical experience for the decisions that large organizations need to make for moving forward on their data journey are included in my recently released book called “A data-driven company: 21 lessons to create value from AI. These “lessons” are organised in five categories: organization, business and finance, technology, people, and responsibility.
While the organization chapter is about decisions related to organizational aspects of becoming data and AI driven, the business and finance lesson deals with the main business decisions related to data and AI, including how to select AI and Big Data use cases and how to measure economic impact, among other aspects.
The section about technology provides lessons about the technological decisions that organizations will face during their data and AI journey. Here it is important to bear in mind specific dilemmas, such as whether to use cloud or on-premises, whether a unified data model is needed or how to approach a data collection strategy.
Given the importance of people in this transformation, I would like to highlight a few specific lessons learnt. One has to do with data democratization. In many organizations, there is a dedicated data or Artificial Intelligence team that takes care of value creation from data. This team receives requests from the different business units and then starts working on solving their problems and providing data-based solutions. Experience teaches that once an organization has learned how to create value from data, everybody wants to do it. This puts a high strain on this central department and in the end becomes a bottleneck (the central team cannot grow indefinitely) and slows down the data journey. It is therefore important to gradually increase the number of employees that can create value from data. That doesn’t mean that each employee needs to become a data scientist. There are several tools that provide machine learning or natural language processing tools that can be used by non-technical people. Of course, those tools cannot solve the most complex problems (for this we always will need data scientists), but those tools can solve maybe 60% of the business problems. This relieves the dedicated, specialist data team from working on the simpler problems, so they can focus on solving the really hard and interesting problems, also maintaining their motivation with the corresponding retention effect. In summary, data democratization enables both scaling up value creation across the company while at the same time retaining the data experts.
The other lesson has to do with the (natural human) resistance against change. Becoming data driven requires that data is shared company-wide (of course respecting all privacy and data protection regulations). Traditionally, data is stored department-wise (the notorious data silos), and the responsible department director has control over the data and decides who has access to it. Data is power, and it is not easy to share power. Dealing adequately with sceptical people is therefore crucial for moving forward on the data journey. One way to do that is to look for a champion in the “data-sceptical” department, work with him or her on a data project -under the radar- and once there are interesting results, let the champion present the results to the department. Change from inside is always easier to accept then imposed change from outside.
The last subject is actually a recent category and is related to responsibility. Today, in respected companies, using technology to only generate profit is not done anymore. Increasingly more companies care about their impact on the planet and on the societies they operate in or with. As we have seen, the use of Artificial Intelligence and Big Data enables huge business opportunities, but those same technologies can also have unintended, negative consequences. Who has not heard about facial recognition algorithms that discriminate against coloured people? AI systems they hire more men than women? Algorithmic decisions that are opaque, black boxes that no one understands? Autonomous decisions without the proper human oversight? Excessive automation substituting human labour? Those are all ethical aspects of using this technology, and it is important to deal with them head on. Increasingly more organizations are adopting so-called AI principles or code of conducts that must ensure that the use of Artificial Intelligence and data technology does not lead to such undesired consequences or impact: responsibility by design is what I call it. In Telefonica, we published our AI principles in 2018 and since then we are implementing them in our businesses. So, we want to make sure that the technology we use for our business and our clients is used in a responsible way and does not have unintended, negative side effects.
The other part of responsibility is related to using AI and Big Data for social purposes: AI for good, or Big Data for social good. Governments around the world have used Big Data and Artificial Intelligence in the fight against COVID-19 as reported in this special issue of Cambridge University Press’ Data and Policy journal.
In conclusion, these recommendations are for (large) private and public organizations that struggle with questions such as: You are you planning to start working with Big Data, analytics or AI, but don’t know where to start or what to expect. You have started your data journey and are wondering how to get to the next level. You want to know how to fund your data journey, how to organize your data team, how to measure the results, and how to scale. If that is your situation, don’t worry, you are not alone. Many organizations are struggling with the same questions, even across many different sectors.
More information: “A Data-Driven Company: 21 lessons for large organizations to create value from AI”: is the latest work written by Richard Benjamins, Chief AI and Data strategist at Telefónica. This book captures all those common experiences and formulates them in practical and concrete decisions with alternative options along with their pros and cons. It also offers the perspectives of 20 International experts and professionals from organizations including AXA, BBVA, ENGIE, KPMG, MTN, O2, the ODI, OdiseIA, Rabobank, Telefónica, and Vodafone.
Follow the presentation of the book on Thursday 9th of September at Fundación Telefónica. More details: https://boletines.fundaciontelefonica.com/institucional/invitacion-repensando-el-manana-empresa-data-driven/
