{"id":32543,"date":"2019-09-25T13:27:41","date_gmt":"2019-09-25T11:27:41","guid":{"rendered":"https:\/\/www.ferrovial.com\/blog\/?p=32543"},"modified":"2025-12-12T00:39:28","modified_gmt":"2025-12-11T23:39:28","slug":"data-driven","status":"publish","type":"post","link":"https:\/\/www.ferrovial.com\/blog\/en\/2019\/09\/data-driven\/","title":{"rendered":"What does it mean to be a data-driven individual?"},"content":{"rendered":"

\u201cWithout data, you\u2019re just another person with an opinion.\u201d William Edwards Deming<\/a><\/p><\/blockquote>\n

With the ever-present digital world and the linked profusion of data, we\u2019re hearing \u201cdata-driven\u201d being applied more and more to organizations that are or want to be more geared toward data in their management.<\/p>\n

Is \u201cdata-driven\u201d just another one of those terms that spreads and turns viral for commercial reasons, or is there particular linguistic snobbishness behind it? Do the people who use it have a deep understanding of its meaning<\/a>?<\/p>\n

Businesses steered by data have tools and abilities at their disposal to make better decisions based on evidence, but they especially foster a mentality and culture among their individual members<\/strong>. So let\u2019s take a look at what data-driven people and organizations are like and what they do with the data.<\/p>\n

\"Data<\/p>\n

Collecting<\/strong><\/h2>\n

Our digital trails are continually growing, and their origin is not just in the operational transactions of management systems. Recording and collecting data is a must for analysis, but having all the necessary data in time, in addition to making sure it is immediately interpretable and manageable is not common or free. Data-driven businesses don\u2019t only collect data by intuiting its value, but instead they focus on the data related to cogent questions and key metrics, working to ensure that they are precise, \u201cclean\u201d (often only after laborious processes), unbiased, and \u2013 perhaps above all \u2013 that they are reliable for everyone.<\/p>\n

Accessing<\/strong><\/h2>\n

Almost all businesses use spreadsheets, and they\u2019re turning into more sophisticated tools (SQL and Power BI, for example) when Excel and\/or their teams\u2019 abilities fall short. Data becomes more valuable if it connects to other data in the business (or outside of it), and it\u2019s important to be aware of that from its design. Being able to access different sources without significant technical effort and democratize access and the capacity for analysis in an orderly way expands its depth and increases its value.<\/p>\n

\"Data<\/p>\n

Consulting<\/strong><\/h2>\n

The tools for consulting and analysis are becoming more and more accessible and versatile. Filtering, grouping, and adding to turn detailed bulk data into higher-level quantities helps our brains understand what\u2019s going on. For anyone, regardless of their position, having some mastery (both technical and functional) over tools beyond Excel (like Power BI) is, even today, a competitive advantage, and access to those tools and to high-quality training resources is more in everyone\u2019s reach than ever before.<\/p>\n

Analyzing<\/strong><\/h2>\n

Having a lot of data and reports doesn\u2019t make us data-driven or good analysts. Even though most businesses evolve analytically from reporting, this tends to focus on showing us what has happened by providing references and baselines. To be more data-driven, we must focus on the future <\/strong>and try to understand why numbers change by trying to shed more light with tests that call into question unconscious convictions. A few definitions and this table from Thomas Davenport, a scholar and author on business management, encapsulate these focuses.<\/p>\n

Reporting<\/strong>: the data organization process to produce summaries of information in order to monitor the situation from different business areas.<\/p>\n

Analysis<\/strong>: the transformation of data assets into competitive insights that drive business actions and decisions by considering people, processes, and technology.<\/p>\n

 <\/p>\n\n\n\n\n\n\n
<\/td>\nPast<\/strong><\/td>\nPresent<\/strong><\/td>\nFuture<\/strong><\/td>\n<\/tr>\n
\n

 <\/p>\n

Information<\/strong><\/td>\n

\n

What<\/span><\/b>\u00a0<\/span><\/b>happened<\/span><\/b>?<\/span><\/b>\u00a0<\/span><\/p>\n

(<\/span>Reporting<\/span>)<\/span>\u00a0<\/span><\/p>\n<\/td>\n

\n

What<\/span><\/b>\u00a0<\/span><\/b>happens<\/span><\/b>\u00a0<\/span><\/b>now<\/span><\/b>?<\/span><\/b>\u00a0<\/span><\/p>\n

(<\/span>Alert<\/span>)<\/span>\u00a0<\/span><\/p>\n<\/td>\n

\n

What<\/span><\/b>\u00a0<\/span><\/b>will<\/span><\/b>\u00a0<\/span><\/b>happen<\/span><\/b>?<\/span><\/b>\u00a0<\/span><\/p>\n

(<\/span>Extrapolation<\/span>)<\/span>\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n

Insight<\/strong><\/td>\n

\n

How<\/span><\/b>\u00a0<\/span><\/b>did<\/span><\/b>\u00a0<\/span><\/b>it<\/span><\/b>\u00a0<\/span><\/b>happen<\/span><\/b>\u00a0and\u00a0<\/span><\/b>why<\/span><\/b>?<\/span><\/b>\u00a0<\/span><\/p>\n

(<\/span>Modelling<\/span>)<\/span>\u00a0<\/span><\/p>\n<\/td>\n

\n

What<\/span><\/b>\u00a0<\/span><\/b>is<\/span><\/b>\u00a0<\/span><\/b>the<\/span><\/b>\u00a0<\/span><\/b>best<\/span><\/b>\u00a0<\/span><\/b>option<\/span><\/b>\u00a0<\/span><\/b>\u00a0<\/span><\/p>\n

moving<\/span><\/b>\u00a0forward?<\/span><\/b>\u00a0<\/span><\/p>\n

(<\/span>Recommendation<\/span>)<\/span>\u00a0<\/span><\/p>\n<\/td>\n

\n

What<\/span><\/b>\u00a0<\/span><\/b>is<\/span><\/b>\u00a0<\/span><\/b>the<\/span><\/b>\u00a0<\/span><\/b>best<\/span><\/b>\/<\/span><\/b>worst<\/span><\/b>\u00a0<\/span><\/p>\n

outcome<\/span><\/b>?<\/span><\/b>\u00a0<\/span><\/p>\n

(<\/span>Prediction<\/span>)<\/span>\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

<\/td>\n<\/td>\n<\/td>\nDavenport: \u201c<\/span>Analytics<\/span>\u00a0at\u00a0<\/span>work<\/span>\u201d<\/span><\/span>\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Sharing<\/strong><\/h2>\n

All too often, data is in silos (systems, functional or isolated in poorly understood \u201cproperty\u201d) that tend to reduce scope, increase effort and expand deadlines, and definitively limit its value. Data-driven businesses encourage sharing data and sources because they know that the whole tends to be greater than the sum of its parts<\/strong> and that worth doesn\u2019t necessarily lie with knowing more (with regard to data) but with connecting more, questioning, delving in, anticipating, and deciding or getting advice on the decision efficiently.<\/p>\n