CIO head-to-head: Data governance versus ownership

CIO head-to-head: Data governance versus ownership

CIOs discuss who should be accountable for data ownership

We ask three CIOs to share their views on whether IT remains the natural owner of data, or if the lines of responsibility are blurring in their companies.

Who should own and be accountable for data in an organisation, and where does a CIO’s responsibility for data and analytics start and stop?

Neill Rose-Innes, CIO, Mortgage Choice

Quite simply, all senior stakeholders should own and be held accountable for data. This naturally includes the CIO and all senior beneficiaries including sales, marketing and finance executives.

There are many layers to enterprise information architecture, with accountability and empowerment being relevant and appropriate to a function’s analytical need. It is no longer correct to assume the CIO has the sole mandate to ensure data is there, complete, accurate and ready for reporting.

Yes, the CIO is accountable for the technology, solutions and information architecture. Further, the CIO should be responsible for the effective design and evolution of an enterprise’s technology and tools, and is the obvious custodian of enterprise-level data.

Of course, to derive real value from the data and transform this into powerful information, senior execs should and must be accountable for the completeness and accuracy of the data they use when executing their responsibilities.

To facilitate this, greater collaboration between the technology team and the functional areas is necessary to properly define future needs, outputs, data requirements and allocation of responsibilities.

My view is each function needs to understand their reporting needs and should be provided with, or be able to source, the tools that enable them to produce the analytics they require, with centralised data made available as both an output and an input to this (often) decentralised execution.

This has the added benefit of stakeholders taking more ownership of their needs, including data entry and standards, in turn becoming less reliant on a central IT function to prioritise, plan and produce the analytics required. Done correctly, this facilitates agility and effective decision making and does away with competing at an enterprise level for priority and resource.

The challenge here is to ensure that as each business area seeks out and develops its own capability, this is done in concert with the IT function and in the context of a strategic technology roadmap.

One of the most important scenarios to limit is the tendency for business functions to “go it alone” if the IT function is unable to deliver to expectations. This is where the CIO needs to take a strong lead, have a thorough end-user understanding, and have in place strategies to ensure these requirements do not remain ad hoc at a functional level, but find their way formally back into the enterprise data dictionary.

Andrew Millingen, head of technology, Swisse Wellness

Read more: CIOs talk: iPad adoption strategies

The technology team should be the guardians of data. Having said that, the decisions on whether data needs to be purchased, or what data should be analysed, could rest with the marketing or sales area.

The CIO’s responsibility absolutely starts at data governance and data quality, then overlooking data sources and core data systems, and in particular, the integrity of data in those systems. It’s a big responsibility for the CIO to ensure there’s alignment of business priorities with the CEO and other functions.

Our IT team manages database admin and monitors data flows, classifying raw data that comes in. The democratisation of data has led the push for global tools that are more user-friendly, such as self-service dashboards and static reporting.

It’s the responsibility of everyone to understand what that data is and why it’s relevant, even if it requires the business intelligence team to publish a glossary of terms. There’s an important education piece for the whole business.

Otherwise, users could misinterpret the information or put out incorrect reports, and a lot of them just need to be fed what they need to know. We have created a handful of powerful business discovery applications so instead of coming back to the analyst for 10 slightly different versions of the same report, people can help themselves to information to suit their purposes.

We deal with both the strategic data analytics behind the high-level business decisions, such as product innovation and development, as well as the day-to-day analytics, such as pricing and promotional decisions with retailers. We are also using analytics for procurement and analysing the supply chain.

Swisse has experimented with centralising and decentralisation data and analytics, and some element of decentralisation is inevitable. So it’s important to have an information hub with a higher level of knowledge that can govern the data and educate people as to what’s out there.

Sarah Harland, GM, technology group functions, ANZ Bank

Data is crucial to our bank’s strategic advantage, more so now we are so digitally focused. Therefore, it is essential strategy and accountability for data lies at the top echelons of the bank to ensure there is the best alignment with business priorities and the needs of individual businesses can be quickly met.

Enterprise data at ANZ is a top 10 CEO strategic initiative. Data strategy, governance and architecture are then centrally shaped by the needs of business units.

The business lines are responsible for how they want to meet their business objectives, and for the quality and use of data in achieving them. For information management, combined business and technology teams work collaboratively to achieve the best outcomes in data-centric projects.

Banks have moved from managing disparate silos of data to enterprise organisations with programs that recognise data as a strategic asset.

However, business involvement is crucial in analytics and modelling. Underlying this is technology, which focuses on sourcing and aggregating data for consumption. Areas of success at ANZ have been where the business has brought clear vision and excellent knowledge of the data, and collaborated tightly with modellers across both business and technology.

The parallel nature of these activities and an iterative approach make it essential for teams to work together to achieve success.

ANZ is successfully using customer data analytics to improve our digital customer experience. This includes increasing the relevance of our marketing campaigns. The business has driven relevant campaigns through digital and social channels from both a time and product perspective, while

IT has sourced and modelled the data used by the analytical engine. We’ve also implemented Hadoop technology for big data to capture information and create business insight from large volumes of structured and unstructured data.

The big data strategy is aligned to the social media strategy. The business provides all the use cases and utilises analytical tools to gain insight, while the technology [group] sources and maintains the data.

To help with this, ANZ has introduced business intelligence tools to improve analysis and reporting across the enterprise.

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Tags anz bankMortgage ChoiceNeill Rose-InnesSwisse WellnessSarah HarlandAndrew Millingen

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