Data-Driven Enterprise: going beyond decision-making

Going beyond decision-making: the recipe for future success is seamless collaboration between data, IT and business
 

Nowadays the importance of data and technology goes without saying as companies compete more and more with information-based strategies. Yet being a data-driven company is too often used as a synonym for using data to make decisions. Even though using data to augment and automate decision making is a definite characteristic of a data-driven company it goes deeper than that.
 

“Characteristics of data-driven companies are customer centricity, decision making based on hypotheses and insight and drive to develop end-to-end processes for efficient operations.”

A data-driven company utilizes information systems and analytics to iteratively improve and develop its business and optimize its processes. It also actively collects information about its customers and uses it to guide product development and tailor marketing messages. Data-drivenness could hence be defined as being customer centric, objective and automated. Or simply put, being a learning organization In order to get the learning loop started, just owning data obviously is not enough; having access to quality data that is relevant to business gets you further: when meaning of data is correctly understood, it's properly owned and managed, its value increases significantly. Data is a mass of information that has been shaped by the business environment and history. But without interpretation, it has no intrinsic value. Only by understanding history we can understand collected data and achieve reliable insights and real benefits.

Transforming a company is really, really hard. But without seamless collaboration between business, IT, and data management it’s nearly impossible.
 

Making a tangible impact with data on scale at a large organization is not an easy feat and requires seamless co-operation between business, data and IT. If business lines have too much independence, we have a hard time building company wide data asset and utilizing common tools and platforms for efficiency. If data governance is developed to be the highest priority we might compromise too much business agility and if IT is in charge we run a risk of focusing too much on technology.
 

“The organization must be ready for initiatives aimed at data-driven operations, but not all data needs to be collected and filtered. It is important to understand which data is truly important for the business and how it relates to real-world phenomena. In this regard, the significance of cooperation is emphasized.”

Business needs to be on the driving seat and able to build its own vision of how data can be used to drive benefits in both short and long run. Another way to put his is that building a data driven company without a link from business strategy to data strategy is like being a passenger in a car without a driver. Best way for business to lead the change, is by being the driver. Another way to put his is that building a data driven company without a link from business strategy to data strategy is like driving a car blindfolded.

Pre-requisite to advanced use cases is to build a clear understanding what data is important for their business to run and how their data relates to the “real world” phenomena. Less time spent correcting downstream data errors means more time for development activities. Less time spent discussing which excel contains the “real” data means more decisions based on data. Less time guessing how the customers actually behave means more time on developing customer experience.

In order for this to happen efficiently, business needs support from data governance experts. Having the right governance frameworks in place to empower business to take the ownership of its own data is a great start. Being able put the data governance into practice as enterprise data management is the bulk of marathon. Having the checks and balances in place to make sure that the data in managed in an ethical and sustainable way that takes into account the need to build the data asset from the whole companies perspective is the crucial yet short sprint at the end.

IT organization is in charge of common tools and platforms supporting all the business lines. Being able to develop best practices, frameworks & automation and most importantly help the business to make most of them will be the key to improving the time to market, keeping the maintenance costs down in the long term and ensuring that security, compliance and sustainability considerations are taken into account.

With shared goals and right balance between business, IT, and data, it is possible to ensure that investments made in data yield desired results, also in the future.