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A maturity model for big data and analytics

Client Technical Leader for UK Public Sector, IBM UK Ltd.

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 organizations 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 organization—one that is business-driven and able to adapt to evolving business objectives—is a well-suited and durable approach.

The 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:

  • http://www.ibmbigdatahub.com/sites/default/files/flexibility_blog.jpgBusiness strategy: The first step with any advanced technology capability is to recognize 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 organizational capacity to explore data for new opportunities and an ability to construct quantified business cases. Mature organizations 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 organizations recognize 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 organizations provide governed access to data wherever it resides in the organization and are able to give it meaning and context.
  • Analytics: Mature use of analytics optimizes the business. Organizations 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 organization, 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 organization realizes 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 organization 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 organization. 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 fulfill 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.

 

Ad hoc

Foundational

Competitive

Differentiating

Breakaway

Business strategy

Big data is discussed but not reflected in business strategy in which use of data extends simply to financial and regulatory reporting.

The business strategy recognizes that data can be used to generate business value and return on investment (ROI) though realization that is largely experimental.

The business strategy encourages the use of insight from data within business processes.

The business strategy realizes competitive advantage using client-centric insight.

Data drives continuous business model innovation.

Information

The organization uses its historical structured data to observe its business.

Information is used to effectively manage the business.

Information is applied to improve operational processes and client engagement.

Relevant information in context is used as a differentiator.

Information is used as a strategic asset.

Analytics

Analytics is limited to describing what has happened.

Analytics is used to inform decision makers why something in the business has happened.

Analytical insight is used to predict the likelihood of what will happen to some current business activity.

Predictive analytics is used to help optimize an organization’s decision making so that the best actions are taken to maximize business value.

Analytical insight optimizes business processes and is automated where possible.

Culture and operational execution

The application of analytical insight is the choice of the individual and has little effect on how the organization operates.

The organization understands the causes behind observations in business processes, but its culture is largely resistant to adaptation that takes advantage of the insight.

The organization makes limited business decisions using analytical insight to improve operational efficiency and generate additional value.

Decision makers are well informed with insight from analytics, and the organization is capable of acting to maximize resulting business value.

The organization and its business processes continuously adapt and improve, using analytical insight in line with strategic business objectives.

Architecture

The organization does not have a single, coherent information architecture.

An information architecture framework exists but does not extend to new data sources or advanced analytics capabilities.

Best-practice information architectural patterns for big data and analytics are defined and have been applied in certain areas.

Information architecture and associated standards are well defined and cover most of the volume, variety, velocity and veracity capabilities and structured and unstructured data consumption needed for differentiation.

Information architecture fully underpins business strategies to enable complete market disruption with volume, variety velocity and veracity specifications applied.

Governance

Information governance is largely manual and barely sufficient to stand up to legal, audit and other regulatory scrutiny.

Understanding of data and its ownership are defined and managed in a piecemeal fashion.

Policies and procedures are implemented to manage and protect core information through its life in the organization.

The degree of confidence in information and resulting insights is reflected in madding decisions.

Information governance is integrated into all aspects of the business processes.

Stay tuned for upcoming blog posts on the maturity model that describe how the model has been validated and augmented in early use with clients. In addition, see how to use the model to help realize business value. We also welcome your thoughts on the maturity model.

And, if you’re in London, join us for IBM Analytics Live, an in-person event on Thursday, July 2 at 10:00 a.m. BST, The Oval Cricket Ground, Kennington, London. At IBM Analytics Live you will discover how successful leaders are infusing analytics throughout their enterprises, using multiple data sources and systems to drive simpler, smarter and faster decision making. You'll hear how organizations like your own have grasped the opportunity to improve both operational performance and to deliver enhanced customer benefits. You'll not only hear what we think about the power of analytics, but also directly what our customers are achieving today. Register for IBM Analytics Live today