Building bridges

Intensive use of customer analysis offers 23 times more chance of acquiring customers, 6 times more likely to retain those customers and 19 times more likely to be profitable. Data is more than fine. This requires a data strategy. In fact, nothing but a strategy that builds bridges between data and daily life.

Instead of instantly focusing on data, organizations should start by developing a data strategy.  A successful data strategy is about which goals organizations want to achieve and how data can assist. This reduces the risk of organizations drowning in data. Only after developing such a strategy, start looking at which data and data scientist competences are available. In order to define which data an organization needs, key challenges should be defined. Only collect and analyze data when it is clear what needs to be answered.

Go organization-wide
Organizations often develop their data strategy to fit specific departments. But instead of such narrowed-down strategies, an integrated, organization-wide data strategy is key to success. Also, data and analytics are not solely an IT matter. Rather than focusing on long-term strategic goals, IT teams tend to focus on data storage, ownership, and integrity. A data strategy should be owned by the leadership team.

Making data-driven decisions
We now know why organizations need a thoughtful data strategy. Research by McKinsey provided 4 key components of successful data strategies:

1. Centralizing enterprise data in a data lake. Data lakes are designed to hold, process and analyze both structured and unstructured data. They are scalable for data collection at high velocity, variety, and volume. Moving to a data lake lowers costs compared to on-premise data warehouses. It also enables a faster workflow due to the storage of raw formats.
2. Data privacy regulations. Any data strategy needs to account for complying with new data privacy regulations, such as the European Union’s GDPR and California’s CCPA. All companies need to comply with these regulations, no matter how local their customer base. 
3. Data-driven workflow. Moving to a data-driven workflow can be seen as a cultural change. Data analysis should not be approached as an experiment or an exercise in processing data. The fundamental objective of collecting, analyzing, and deploying data is to make better decisions.
4. Building bridges between IT and management. Embracing cultural changes is a team effort. To close any communication gaps between your experts, you’ll need colleagues with a talent to translate IT into business language.

A good data strategy is essential for every organization. Are you looking forward to becoming data-oriented?