A maturity model for big data and analytics

by Chris Nott
CTO Big Data & Analytics, IBM UK & Ireland, IBM

According to a recent IBM Institute of Business Value (IBV) study, 63 percent of organizations in 2014 realized a positive return on their analytics investments within a year. That study also noted that 74 percent of respondents anticipate that the speed at which executives expect new data-driven insights will continue to accelerate.

These results indicate a challenge for organisations as they seek to drive deep engagement with their customers together with innovation and process improvement for competitive advantage. The challenge is to avoid creating a new generation of siloed systems for gleaning insight to meet specific business needs that apply to today. Delivering these systems by taking a coherent approach across the organisation—one that is business-driven and able to adapt to evolving business objectives—is a well-suited and durable approach.

This big data and analytics maturity model considers not only the technology to lay out a path to success, but more importantly it also takes into account the business factors. The maturity model comprises six categories for which five levels of maturity are described:

  • Business strategy: The first step with any advanced technology capability is to recognise that its use needs to be business-driven. While underpinning technology is needed to acquire data and execute analytics, business expertise is necessary to derive meaningful insight and use it to differentiate outcomes. Differentiation can be achieved by enriching customer engagement and driving operational improvements. It demands the organisational capacity to explore data for new opportunities and an ability to construct quantified business cases. Mature organisations are able to harness available data and apply analytics to it to innovate and create new business models.
  • Information: Use of data to manage the business is the base capability. However, highly mature organisations recognise that data is a first-class, strategic business asset. It comes not only from existing transactional systems—the systems of record—but also from systems that support individual—the systems of engagement—and external data sources. Furthermore, mature organisations provide governed access to data wherever it resides in the organisation and are able to give it meaning and context.
  • Analytics: Mature use of analytics optimises the business. Organisations will already be reporting to show their financial performance and to demonstrate regulatory compliance, but analytics is necessary to understand why something has happened or to predict what is likely to happen. The resulting insight helps improve customer engagement and operational efficiency. Analytics is used to make data-driven decision making pervasive in an organisation, and it requires timely insight in context.
  • Culture and operational execution: Access to data and using analytics to derive insight builds no business value in and of itself. The organisation realises benefits when its people and systems have a desire to seek out and make use of insight as it operates. Trust in insight is essential, as is an ability to visualize, share and provide feedback to learn and improve. A mature organisation can offer rich data and analytics services that are aligned to and evolve with business priorities.
  • Architecture: An overall, coherent technology approach to big data and analytics is essential to establish durable capability in an organisation. It enables ease of access by end users, agility in the capabilities required to address current business needs and a managed approach to accessing required data. A mature architecture caters for all four characteristics of big data: volume, variety, velocity and veracity. It accommodates these big data characteristics through the creation and systematic reuse of architectural patterns, assets and standards—including operational models to fulfil service levels and security requirements—as well a consistent use of data models.
  • Governance: Information governance is a critical success factor for big data projects. Policies need to be established and enforced to a degree of confidence in information and so that resulting insights are understood and reflected in decision-making efforts. Policies also need to span provenance, currency, data quality, master data and metadata, lifecycle management, security, privacy and ethical use.

Niall Betteridge, executive IT architect at IBM Australia, and I developed the big data and analytics maturity model. The following table reflects the top level of the model.

Maturity Model
Using the maturity model can provide understanding of the current state and help form a view of what level of capability is required to support the priority use cases, thus indicating gaps that need addressing.

From the outset, it is important to appreciate that prioritised use cases have different capability requirements. One use case, for example, might be a management dashboard; another might be real-time analysis gleaning very specific insights for a particular group of end users.

For each prioritised use case, step through the maturity model. Certainly different maturity needs may arise for each use case, but the model encourages consideration of the overall architectural patterns that supports all use cases.

Typically, the assessment is conducted through workshops and interviews with key personnel across both the business and information management teams.

Having a view of the gaps and an understanding of the effects on business strategies allows development of a coherent action plan and roadmap. Such a roadmap and its associated plan must take into account all prioritised use cases to provide durability, harnessing early investments. It encourages the creation of an information governance regime and a singular approach to the architecture, for analytics initiatives often become a new generation of silos when insufficient consideration is given to cross-enterprise sharing and reuse.

If you need more information on the model, including in-depth detail on each maturity category and how it can be successfully applied in your business, we would love to discuss this with you.

Share this post: