Christophe De Greift shares an article that offers tips on how to focus data analytics on the user.
Alicia and Mateo used to drive to the office and order their purchases from the employee through a handwritten list and cash. The global quarantine changed everything: today they work by videoconference, shop through e-commerce and pay without cash. The generation of data corresponding to this digitization is accelerating at such a rate that the world will probably reach 100 zettabytes of data next year. They can visualize that number with 1 and 23 zeros to the right: 100,000,000,000,000,000,000,000 …
Encouraged by this data explosion, data analytics platform vendors are developing increasingly automated and user-friendly tools. At the same time, most analytical projects fail. I estimate that only 20 to 35% of analytical projects manage to create value and the rest – between 65 and 80% – are never born, show no impact or fail to be sustained over time. The data explosion is accompanied by another: the hours lost by high-income professionals.
The main causes of failure in analytical projects are known and have to do with processes and people, rather than algorithms or technology: the project does not respond to a relevant business problem, it does not achieve the adoption of key users or the ‘buy- in ‘of the’ stakeholders’. The new post-COVID 19 reality exacerbates the problem.
It is urgent to focus data analytics on the user to reverse the situation and thus unleash the economic potential of data, which leads us to the following question: How to focus data analytics on the user?
Key points include:
- Problem solving
- Agile project management
Read the full article, How to focus Data Analytics on the User?, on ChristophedeGreift.com.
In this evergreen and ever useful post from Christophe de Greift, he provides the key questions an executive should ask a data analyst to ensure they can deliver what is required.
Voice recognition, interview robots, real-time movie recommendations, advanced data analytics are a part of our everyday lives and cannot be ignored by the 21st century manager. The potential benefits for the company are found along the chain of value, from purchases to after-sales, through talent management.
To reap these benefits, an organization must develop diverse capabilities in data management, analytics and planning for example. Each of those capabilities represents a challenge in itself, but I would like to address another major obstacle to adopting advanced analytics in this article: executive confidence in the outcome. Indeed, the most valuable algorithms such as ‘deep learning’ in artificial intelligence are also the least understood, generating a natural fear of misuse that an executive must overcome before being able to properly use those tools.
Hiring the best scientist is not enough to avoid going from artificial intelligence to artificial stupidity, since the knowledge and business judgment of the senior executive is essential in the decision-making process supported by data analysis.
I learned during my years of consulting that asking the right questions is complex but powerful… Therefore, I recommend a list of questions for the executive to ask throughout the decision-making process, from problem conceptualization to conclusion.
Key questions include:
- How do we ensure random selection of data?
- Using what criteria do we filter inconsistent or atypical data?
- Why is the algorithm used the most suitable?
Read the full article, 10 Questions to Trust a Data Analyst, on Christophedegreift.com.
Christophe De Greift provides a post designed to help you get it right with these five tips for a more data-driven 2021.
2020 accelerated several trends and one of them is the need to be ‘data-driven’ in decision making. From vaccine testing to last-mile logistics in e-commerce, data analytics is part of the path to making the right decision and generating value.
Having collaborated and talked with dozens of Peruvian companies this year on their greatest analytical challenges, I share below my recommendations for a 2021 full of analytical successes.
Turning data into value requires such a diversity of knowledge and skills that it cannot be achieved without effective teamwork. However, collaboration in analytics does not occur naturally, as the business user seeks a practical solution to their problems and does not know about machine learning, while the data scientist prioritizes rigorous analysis and knows little about the business. A solution that several Peruvian companies have successfully implemented is the incorporation of an Analytics Translator to the team , connecting business users and data scientists.
- Strategy second
I have been a staunch advocate of strategic planning for business, despite mounting criticism. Data analytics also requires a plan, but that doesn’t mark the beginning of the analytics journey. Indeed, a minimum of knowledge about artificial intelligence, data governance and technology is required to think strategically about analytics and an organization must experiment first to develop this knowledge. An online course is not enough. If you are just starting out, choose a use case following the advice in the next point.
- Overcoming cognitive impairment
- Using minimal viable outsourcing
- Moving with speed
Read the full article, 5 tips for a more ‘data-driven’ 2021, on christophedegreift.com.
Christophe De Greift explains why now is the time to plan your analytics transformation, why you should, and the first step to take.
Artificial intelligence is a relatively young discipline in companies and in constant evolution. However, the experience of pioneering companies in various sectors and continents confirms their high value generation when accompanied by a true transformation of the company. Those who have not yet internalized their analytical transformation plan should start now to be able to arrive on time, as I explain below.
Being analytical is increasingly necessary to stay competitive
Many important business decisions are better when supported by data. Neuroscience has recently confirmed what common sense has always allowed us to understand: man can be irrational, biased and even blind. The machine is not 100% reliable either; the key is to precisely define the role of man and machine for each decision, as explained in a previous article . In very repetitive problems such as forecasting the demand for mass consumer products in retail stores, the most advanced and successful companies limit human intervention to exceptional cases. In more sensitive problems such as medical diagnoses, the radiologist is assisted by artificial intelligence.
Across all sectors, the competitiveness gap between analytics pioneers and the others is growing, threatening the sustainability of the latter. Those who have succeeded in analytics redouble their efforts and investments to become even more analytical, creating the virtuous circle explained in a recent MIT and BCG article.
Being analytical requires a transformation at the people, organization, processes and technology level.
Key points include:
- Data as an opportunity
- Predictive maintenance
Read the full article, 3 reasons to plan your Analytics Transformation now, on christophedegreift.com.
Christophe De Greift shares an article that contradicts popular culture and redefines the image of the data scientist.
The ‘sexiest profession of the 21st century,’ named in 2012 by the Harvard Business Review to define the Data Scientist, has gone through various emotional states lately, depending on its field of action. Those who support understanding and predicting the health situation are heroes, whose analyzes define life and death questions such as where to send new artificial respirators. On the other hand, those responsible for predictive models for product demand, credit risk or customer churn, for example, may feel frustrated when seeing them lose precision and relevance with the sudden changes in demand and supply caused by the quarantine. How will the Data Scientists of Peruvian airlines feel about the challenge of forecasting passenger demand in the coming weeks, the next few months?
Many corporate decisions that until February this year were made based on predictive artificial intelligence models are now made subjectively by people, as was done before the ‘big data’ era.
Is returning to pure intuition the solution? Moment … We can talk about a new reality, but humans are still irrational, biased and even blind, that is, intrinsically limited to make decisions. Quantitative models, with all their limitations, can be great companions for making good decisions, if we know how to use them.
Points covered in this article include:
- The data scientist
- Artificial intelligence
- The decision scientist
Read the full article, The Sexiest Profession Post COVID, on Christophe’s website.
Umbrex is pleased to welcome Christophe De Greift with NEXUSQUANTS. Christophe De Greift has 15 years of experience in management consulting, part of it at The Boston Consulting Group and more recently running his own consulting firm focused on business analytics. Christophe has led 75+ projects in Latin America and Europe and has particular expertise in marketing analytics and supply chain analytics in sectors such as media, finance, logistics, consumer goods, energy and mining.
He lives in Lima, Peru with his wife and two young children and will keep exploring Peru with them, as soon as lockdown ends.
Christophe is happy to collaborate in transforming data into business value, both remotely and in Latin America or Europe.
Christophe De Greift identifies the problem of low data literacy and shares four rules that can improve statistical data during COVID-19 and our understanding of the situation.
The world was caught off guard by a new virus that we are still trying to understand. If we turn to official sources to find answers to our questions, we often find graphics that are not very relevant and even misleading about COVID-19. In the era of artificial intelligence and predictive analytics, we continue to suffer from low data literacy in institutions and circumstances where decision-making based on reliable data should take precedence…
Hoping to see a rapid improvement in the official sources of communication on the health situation, I recall below some basic quality criteria for statistical communication, and I illustrate each criterion with an example recently found in official sources – anonymous so as not to hurt sensitivities – as well as a proposal for improvement.
The four rules covered are:
- Key questions
- Building Indicators
Read the full article, 4 Rules to Improve Our Statistical Communication in COVID-19 Time, on Christophe’s blog.