Driving a Data Culture

October 1, 2016

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October 2016

By Zaid Kamhawi, CEO , Qarar

 

Let’s face it, for most people data is boring, deeply abstract and highly complex. In a business context it is often only the people within an organization working with data on a day to day basis that find data interesting. So considering these non-endearing qualities, how does today’s management ensure their own organisation’s senior executives to invest in something that is boring, deeply abstract and highly complex? The answer; focus on something that the executives care deeply about, and show how data can drive results for them in this domain.

 

For an organization to invest in analysing its data there first has to be recognition of the value of that data. Recognising that value greatly depends on the perspective that is applied. That perspective is formed through the lens – attached to the value to the data. For example a lens that highlights how data drives operational efficiency; a lens that achieves optimisation of a bank’s liquidity position, or a lens that derives customer experiences and customer value. In an organization that wants to establish a clear data culture, these lenses through which recognition of the value of data is achieved, must be visible to the top management and executive decision makers in the organization. Every board member should be able to articulate their own story of how they see data driving value in the company. An illustration of this is shown by one bank in Europe that holds a ‘’Data Engines Day’’, during which board level executives are invited, to see first-hand, the value of the data that the organization has, what has been done with that data and the measurable successes that have been achieved.

 

Secondly and complementing the recognition of the value of data, is the recognition of the roles that come with effectively utilizing the data in an organization. There are many roles that impact data, starting with what is known as the “Curation of Data” or management of data at source, a very critical and important role that many organizations still struggle with. Recognition of roles requires a clear operating model, one that answers questions with regard to responsibilities and services that are available, including for example, clarity on where to go for analytical support, or who to go to request analysis on customer data or have questions answered on specific customer segments or product lines.

 

Thirdly, for any organization to claim that they have an advanced data culture, there has to be a recognized data architecture, a set of design patterns that the organization is prepared to work towards and able to drive the organization towards. These design patterns start from a high level, going down to a reasonable level of detail and include decisions on the tools that are used for managing a data environment. For example, an organization shouldn’t have multiple platforms to manage its data or multiple solutions to visualize it, it must decide on one. As challenging as it is to drive change management within an organization, to have employees follow the designed data architecture and get them to engage with the analytics team to get the desired results, there does come a tipping point.

 

The tipping point, is the moment at which an organization realizes that it is easier to engage with the services that are available to it, than it is for every department to manage this independently, and an architecture that allows the organization to innovate, make data available and allow the data scientists to apply their knowledge will see the benefits become quickly recognizable.

 

Finally, to achieve the “cultural” side of an advanced data culture, an organization must constantly tell stories about the value that can be driven from the data, it should never stop doing that or it risks data being forgotten. Stories told by the data people, to the non-data people, about non-data related stories, a conversation, not about data but about outcomes, about the things that data can and has helped to achieve. 

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