How does compartmentalization affect the capability to take advantage of data insights and analytics?

According to the Merriam-Webster dictionary specialization it is the structural adaptation of a body part to a particular function.

Most organizations evolved considering specialization as tool to achieve a greater degree of efficiency, there is however a downside - looking at it from the perspective of the journey to become data-driven organizations – specialization leads inevitably to compartmentalization, that will create obstacles when managing data as a corporate asset and consequently on the objectives of a data-driven transformation.

How does compartmentalization affect the capability to take advantage of data insights and analytics?

From an organizational perspective, this compartmentalization, makes all sense, each of the units grows into a specialized unit, to assure the highest degree of efficiency, maximizing the resources and results.

This reality was transposed to how data is managed – The data silos.

Data silos were born out of an organization’s growth, the result of years of initiatives driven by business problems, years of different business strategies, multiple technological options, and even resulting from mergers or acquisitions.

From the data perspective, data silos are an abnormality, they impact data sharing, data quality, the costs of data acquisition and preparation, they have a serious impact on the insights that can be driven from data, and more critically they impact on an organization’s competitiveness.

Historically, IT has been responsible for everything data related. It has been the IT departments that have been pushing for introducing new technologies and been responsible for any data related initiatives.

On the other hand, business units focus on their activities, not considering data as their responsibility, not developing the necessary skills, and delegating the responsibility/ownership of their data to IT.

Data strategy is business strategy

The ultimate purpose of an organizations data is to create business value, so any data strategy must be oriented towards the organization's strategic priorities and key business objectives and any data related initiative must be entirely supported on business strategy and objectives.

IT must step back - and allow business stakeholders/units to drive these initiatives. These are the people who know what the business problems are, is needs and objectives.

Business must step up - and take ownership of data, understand it as an asset with a huge potential to contribute in a determining way to their objectives.

Giving the control to the business, building the business case with those willing to defend it, those who can easily identify business pain points, while solve some of challenges usually associated with these processes, as lack of cross organization involvement or resistance to change.

Having business stakeholders that can passionately and effectively articulate the impacts and benefits of a data initiative and that will be eager to defend the project – Transforms a traditional resistance point into an evangelist, with enormous impact on the trust of the insights being produced and the capability to quickly move from insights to actions.

Failing to support any data initiative on strong business cases, anchored on clear business objectives, transforming data initiatives into technological initiatives will impact the success of these initiatives, often seen as just another siloed IT project with no perceived value from the business side.

The role of IT in this process is to find the right technology and support the business units in this journey.