Data Culture Starts With Management
Digital Transformation and Data Culture are two of the big hype terms to emerge recently from the tech industry think tanks, very high concept and intended to resonate in board rooms globally. As with most such buzzwords, these phrases are remarkably devoid of actual meaning, and as such can be filled in to mean whatever happens to be in the listeners mind at the time.
However, there is enough here that these terms do have significance, in great part because they point to a reality that makes many managers uncomfortable. Failing to manage the data in an organization is not a failure in the tools used to manage that data – it is a failure of management itself.
Put another way, management has no one else to blame if big data projects fail. To understand this, it’s worth clearing a few common but critical misconceptions.
Your databases are full of valuable data. Nope. Not even close. Most databases are filled with transactional data, in effect the ghost signatures of events that have taken place in the past. Some of this can be valuable – especially time series data where you have specific metrics that change over time – but much of it is intended to support applications, there is a great deal of redundancy within the data, and because each database is a world unto itself, synchronizing these databases with other databases can be a complex and expensive process that reduces the return on investment of such data analysis efforts.
Your databases are well designed. The bulk of database development occurs before the first actual piece of data is ever entered into a database. The configuration of tables, columns and keys that a database uses is called its schema, and if you dig deep enough into your IT department you will no doubt find a a block and line diagram that looks like a cork board on steroids, typically called an Entity Relationship (or ER) diagram.
Yet once that ER diagram gets printed, the reality begins to diverge from it. New tables are added because certain features weren’t anticipated, columns get deprecated in favor of other columns, your database admin leaves and a new one takes over, with his own ideas about data modeling. A database schema gets transferred from one system to another with no one understanding why certain structures were chosen, leading to even more complexity.