Data Analytics: Strategies to Demonstrate Value and Achieve Transformation

Data Analytics: Strategies to Demonstrate Value and Achieve Transformation
Data Analytics: Strategies to Demonstrate Value and Achieve Transformation

Recently, chief information officers, chief data officers and other leaders got together to discuss how data analytics programmes can help organisations achieve transformation, and how that contribution of value can be measured. We joined to share our insights at this CIO Online virtual roundtable event, which included leaders from organisations in healthcare, financial services, utilities, communications and more taking part in the conversation.

The discussion focused mostly on data management issues and opportunities around “supply” – its quality, ownership, access and other matters. Supply and consumption are symbiotic principles that together maximise the value of the enterprise data asset. While “consumption” matters are just as critical, getting data supply right is essential to ensuring that data – and the insights it drives – are available and trustworthy.

Our participants report encountering the view that data analytics programmes don’t justify the effort to implement and operate them — even as companies spend more on big data and analytics every year. These leaders also struggle to set up metrics that demonstrate their programmes’ achievements of transformation objectives.

These two challenges are closely linked: better metrics on data analytics programme value would go a long way toward dispelling the perception that these programmes are not worthwhile. In our conversation, we acknowledged that doubts about data analytics’ worth is a symptom that the business is not equipped to derive full value from the programme. It’s a situation that calls for empowering the business to read, analyse, work and even argue with data – effectively and confidently. This post summarises our conversation and describes some strategies we discussed to derive and demonstrate data analytics programme value.

A community of teams

organisations can support data analytics programme effectiveness by ensuring that all impacted stakeholders (e.g., business, IT, data management, security, risk & compliance etc.) are engaged appropriately in sustained development and management of trusted data and insights. In other words, treating data and the insights it provides like any other critical corporate asset.

Mark described this sustained engagement model as “a holistic, cross-functional approach that starts, and ends, with business value”; a way of looking at modern data and analytics that resonated with our participants.

In addition to consistent cross-functional engagement, senior leadership buy-in and support is paramount to ensure the organisation’s corporate strategy and its data and analytics strategy remain symbiotic in realising business value. This alignment is essential to any data analytics programme because it focuses programme efforts on mission-critical transformation.

Arguing with the data

In successful data analytics programmes, business users absorb, assess and act on the data by reading, working, analysing and arguing with it.

… Arguing with data? When data owners achieve maximum comfort with analytical tools, when they attain maximum literacy with the data itself, then they will use data “to support a larger narrative intended to communicate some message to a particular audience,” in the words of a formative early paper on data literacy. This is when data analytics programmes deliver their greatest value. Empowering the business to argue with data is the highest goal.

Measuring data analytics’ value

Participants shared questions about how to measure data analytics programme value. Metrics fall within the governance domain, which is the purview of owners and stewards together.

Lucas outlined a list of categories to simplify metrics. Considering only these four dimensions can help leaders simplify the seemingly complex problem of demonstrating value:

  • How does our use of data analytics increase revenue?
  • How does the data analytics programme reduce losses for the business?
  • How is data analytics helping us drive down capital expenditure and operating costs?
  • How are we using data analytics to manage enterprise risk?

Measuring along these dimensions dispels doubts that data analytics programmes don’t deliver value. The answers to these four questions help programme leaders gain traction: in the absence of doubt, more gets done. The questions offer an additional benefit when they generate new ideas about further advantages a data analytics programme could deliver.

Gaining momentum through early wins

Early wins provide momentum as well as a foundation from which teams build a sense of community and confidence. As organisations acknowledge that business transformation is not a project, but an ongoing process, the confidence-boosting, competence-building experience gained via early wins provide the basis for ongoing tracking of achievements and corrections to data-driven decisions as needed.

The power of the roundtable

Leaders can help their data analytics programmes deliver value by articulating the data ownership role for the business community. Then, they can measure programme value along the lines of increased revenue, reduced losses, lower costs, and better-managed risk. They can focus on early wins that build team confidence and competence as the foundation for ongoing programme effectiveness.

The ideas we generated together highlight what can happen when a community of leaders discusses barriers to success in a confidential environment. We thank CIO Online for the opportunity to meet with and advise the leaders who joined us for this roundtable.

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