Published: Aug 13, 2021

Enabling the Right Data Culture for Success


Data is the most critical resource for companies in the digital age. This is well established within corporate executive circles. Businesses have moved from the age of recording data, analysing data from data warehouses, centralised or decentralised BI Analytics teams to enable enterprises driven by data. With the age of data-driven organisations, companies need to collect, protect, govern and manage data effectively. However, not all data initiatives are successful in the enterprise. Organisations are struggling to convert data into value. According to a Gartner report, only between 15% - 20% of data science projects get completed. Of those projects completed, CEOs say that only about 8% of them generate value.1 There are many reasons for these trends. One underlying trend in all of this is having a mature enterprise data culture. 

What is data culture, why is it the key driver 

Organisation culture is something developed by leaders its vision, policies and behaviour. Likewise, data culture develops over time on how we treat customer data, record validate and protect our data, and use it in decision-making. A collective effort of many business units, enterprise system design, and operational processes over time creates a culture of data and how it is managed effectively. In a hyper-digital economy, this data culture needs a direction, strategy, vision, and a road map. As with organisation culture data culture is best driven by the C Suite, led by the CEO or COO. According to IDC’s 2020 survey of analytics, AI, and RPA services buyers, 80% of respondents said they were at some stage of AI adoption. Executive focus on data culture is a fundamental enabler of data and success in analytics.

Over time, the role of the chief data officer has expanded to understand the value of data, be a steward for business units, the organisation as a whole and how best to monetise value from data. Every organisation could have a different degree of data analytics need form each department. Some organisations could be very sales driven and may need sophisticated data analytics in sales and marketing as compared to advanced finance analytics in an investment firm or operational data efficiency in supply chain or manufacturing.

Having a shared reverence for data value, quality control, and data treatment regardless of business function or business unit is essential for establishing a common culture. This needs to be laid out as a written down set of principles to be followed adopted in either centralised, hybrid or decentralised analytics organisation models.

Treating data as an organisation asset 

Data generated in any department is an organisational asset and needs to be revered, nurtured, protected and shared. Data from one department or business unit which may or may not have value in its silo when combined with industry and overall organisation data can be of much more value. Responsibility for data is local, but ownership is shared so that all may benefit from the accurate, responsible collection of quality data.

How business trust enables a strong data culture

Business trust is fundamental to unhindered data sharing, access and usage of data for the common good of the organisation. Business unit politics and power struggles of business units with IT departments can hinder getting the best of your data initiatives. Organisation structure for a hybrid data organisation or centralised structure is something for every organisation to consider for what may work best in their individual circumstances. When business units can trust their data with peers, central teams including IT in an environment without detrimental effects of being open should enable success. 

The role of the CEO, COO or at the minimum leadership of the CDO should enable this alignment. Stakeholder alignment at the executive level is essential to facilitate trust, data sharing and good data culture. 

Key considerations to strengthen data culture:

  • Setting a data vision
  • Review and refine a competitive data strategy 
  • Data governance should clearly define a common set of principles to be shared with all functions and business units
  • Review operating structure of Chief Data Officer and Chief Analytics Officer (Centralised, federated, hybrid or decentralised) as what is the most effective
  • Determining a maturity of data by function or business unit and defined road map linked to IT transformation 

NCS has invested to help enterprises in developing their data platforms for the future. With experience of consulting and technology skills we look to make a difference in the industry. If you like to know more about out thinking on setting analytics teams and driving results through data do get in touch with us.

Conrad Braganza
AI data solutions, market lead.
conrad.braganza@au.ncs-i.com

References

  • Gartner Analytics Predictions

  • IDC’s 2020 survey of analytics, AI, and RPA


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