An enterprisewide culture change towards a more data-driven, evidence-driven attitude, decisions and actions cannot be achieved simply through top down mandates and technology spends. One key strategy to achieving this change is to implement a series of user “advantages” that progressively reduce the time and effort required by users to be data-driven and analytical.
Jenga – the game analogy for an enablement culture
Similar to the game of Jenga, where the intent is to build a high quality tower that increases in height while being structurally sound, enterprises should aim to enable a culture of analytics that is foundationally sound but flexible and extensible. To ensure consistency, uniformity and governance in the use of data, end users of data need to be provided with curated data sets that they can build upon, extend and customize analytics required for their specific use cases. This ensures that the organization runs on a consistent and common version of the truth. As different analytical use cases build upon on this common foundational layer, they still are able to maintain their fidelity and quality in the resulting analytics. Each successive extension of the data set is directly tied to the foundational version of the truth through clearly tracked, verifiable lineage.
The above setup has two clear advantages. First, end users who are not data curation experts do not have to deal with wrangling with raw, dirty and incomplete data. This frees them to focus on the analytics required for their immediate decisions and actions. Secondly, the organization is assured that any two different users who each arrive independently to build similar analytics will be very highly likely to get the same results from their independent analysis. This ensures that collaboration through analytics is seamless as all parties’ assumptions and hypothesis are founded on the same data and insights.
The triple advantage strategy to a culture change
The triple advantage strategy to a culture change is composed of an infrastructure, data and analytics advantage for both producers and consumers.
The 'infrastructure' advantage
Another requirement for driving an analytics culture change is the availability of infrastructure that enables data and analytics. Analytical infrastructure should serve the needs of three types of users making them more productive, efficient and agile. The three target personas are the data engineers and scientists, the business analysts and the end users/consumers of analytics. The availability of appropriate infrastructure is the key difference between data driven organizations and others without a coherent data and analytics strategy.
The 'data' advantage
Enabling the collection of disparate data and ensuring that curated data sets that describe and represent different events and entities from the organizations, its partners and users are available for search, discovery and analysis is key to a culture change. In any large, complex enterprise that has grown organically through years of systems and processes changes, mergers and acquisitions and changing business models, data is often hidden and locked and this inertia of access is one of the strongest hurdles to usage of data on a day to day basis for decision making and actions.
The 'analytics' advantage
The third part of the advantage strategy is the availability of pre-built, out of the box analytics designed to deliver key business insights such as site visit flows on a website or a marketing funnel or to deliver application specific insights or the key KPIs to measure the efficacy and issues for a specific application or process or to deliver user specific analytics designed to deliver on the data needs of a specific user role. Offering such pre built analytics custom designed for specific insights, application or users removes the need for the user or an intermediary to build these from scratch thus reducing the time and effort it takes to derive value from the data and analytics system.
The key to enabling a culture change is to hand hold the end users and ensure that they can find and use the analytics they need to perform their jobs successfully. Culture change is not helped by mandates to create and consume analytics. However, when analytics are presented as an enabler for the activities and decisions they need to do/make for their jobs. Ensuring that users are able to get accurate analytics when they need them which enables them to do their job leads to an exponential increase in confidence in the infrastructure, data and analytics value and consequently, usage of data and analytics for collaboration, decisions and actions seeps into the cultural fabric of the enterprise.
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