Business analytics capability, organisational value and competitive advantage

ABSTRACT

Business Analytics makes the assumption that given a sufficient set of analytics capabilities exist within an organisation, the existence of these capabilities will result in the generation of organisational value and/or competitive advantage. Taken further, do enhanced capability levels lead to enhanced impact for organisations? Capability in this study is grounded in the four pillars of Governance, Culture, Technology and People from the Cosic, Shanks and Maynard capability framework. We set out to undertake the first empirical investigation to measure if there is a positive relationship between Business Analytics capability levels as defined by Cosic, Shanks and Maynard, and the generation of value and competitive advantage for organisations, and do enhanced capability levels lead to enhanced impact. Data gathered from a survey of 64 senior analytics professionals from 17 sectors provides evidence to support that a strong and statistically significant correlation exists between higher capability levels and the ability to generate enhanced organisational value and competitive advantage. Additionally, a revised definition of Business Analytics is proposed, given that Business Analytics should give rise to organisational value and/or competitive advantage and that for this to occur the necessary capabilities must be in place.

KEYWORDS:

• Business analytics
• capability
• maturity
• organisational value
• competitive advantage

Disclosure statement

No potential conflict of interest was reported by the authors.

A.1. Governance

• G0 There are clearly identified individuals in our organisation who are responsible for making decisions in relation to the planning, implementation and application of Business Analytics.

• G1 There are clearly defined individuals who will provide input into decisions in relation to the planning, implementation and application of Business Analytics.

• G2 Individuals responsible for making decisions in relation to the planning, implementation and application of Business Analytics are held accountable for the resulting actions and outcomes of these decisions.

• G3 What best describes the role of data and analytics in the business strategy in your organisation?

• 1No analytics vision or strategy exists at this time.

• 2Some analytics strategy exists for functions or lines of business.

• 3Analytics strategy is established for the enterprise, but not fully aligned across the business.

• 4Analytics strategy is established and starting to be viewed as a key strategic priority.

• 5Analytics strategy is well established and central to the overall business strategy.

• G4 We prioritise our analytics efforts to high-value opportunities to differentiate us from our competitors.

• G5 Our process for prioritising and deploying our data assets (data, people, software, hardware) is directed and reviewed by senior management.

• G6 Our senior executives regularly consider the opportunities that data and analytics might bring to our business.

• G7 We consider new products and services based on data as an aspect of our innovation process.

• G8 We regularly conduct data-driven experiments to gather data on what works and what does not in our business.

• G9 We evaluate our existing decisions supported by analytics and data to assess whether new, unstructured data sources could provide better models.

• G10 We identify internal opportunities for data and analytics by evaluating our processes, strategies and marketplace.

• G11 We have the ability to reconfigure and leverage the organisations Business Analytics resources and capabilities in order to respond to changes in the business environment in a timely and efficient manner.

• G12 We have the ability to manage human, technological and process impacts across the organisation arising from Business Analytics initiatives.

A.2. Culture

• C0 Senior executives in our organisation utilise data and analytics to guide both strategic and tactical decisions.

• C1 Non-executive level managers in our organisation utilise data and analytics to guide their decisions.

• C2 Users, decision-makers, and product developers trust the quality of our data.

• C3 Which best describes your organisation’s current status regarding the organisation and governance of data analytics?

• 1No organisation exists for data analytics.

• 2Some informal data analytics groups exist in departments or lines of business.

• 3Data and analytics groups are well established in departments or lines of business.

• 4Enterprise-level data and analytics groups are emerging.

• 5Enterprise, department and lines-of-business data and analytics groups exist and are well-aligned

• C4Your organisation is effective at implementing test and learn processes that then impact analytics models and suggested actions?

• C5We use consistent methods/approaches for data and analytics initiative design (projects targeting a specific use case)?

• C6Our senior executives challenge business unit and functional leaders to incorporate data and analytics into their decision-making and business processes.

• C7Our organisation's management ensures that business units and functions collaborate to determine data and analytics priorities for the organisation.

• C8We structure our data scientists and analytical professionals to enable learning and capabilities sharing across the organisation.

• C9Our data and analytics initiatives and infrastructure receive adequate funding and other resources to build the capabilities we need.

• C10We collaborate with channel partners, customers and other members of our business ecosystem to share data content and applications.

A.3. Technology

• T0 We have access to very large, unstructured, or fast-moving data for analysis.

• T1 We integrate data from multiple internal sources into a data (or warehouse) lake for easy access.

• T2 We integrate external data with internal to facilitate high-value analysis of our business environment.

• T3 How does our organisation factor data privacy into a new initiative’s design?

• 1Data privacy generally does not apply to us.

• 2We consider all legal, regulatory, and compliance considerations.

• 3We rely on corporate policies that often go above what is required.

• 4In addition to the above, we consider what we have brand permission from our customers to do with their data.

• 5In addition to the above, we create incentive mechanisms that allow us to share value (pricing, service levels, etc.) with our customers for use of their data.

• T4We maintain consistent definitions and standards across the data we use for analysis.

• T5We have explored or adopted parallel/distributed computing, and/or cloud-based services approaches to data management and processing.

• T6We employ a combination of big data and traditional analytics approaches to achieve our organisations' goals.

• T7We have seamless integration of Business Analytics systems with operational/transactional systems to exploit the capabilities of both.

• T8We are adept at using data visualisation to illuminate a business issue or decision.

• T9We have explored or adopted open-source software for analytics.

• T10 We have explored or adopted tools to process unstructured data such as text, video or images.

• T11 We have the ability to develop and utilise self-service analysis applications (e.g., reports, dashboards, scorecards, and data visualisation technology)

A.4. People

• P0We have a sufficient number of capable data scientists and analytics professionals to achieve our analytical objectives.

• P1Our data scientists and analytics professionals act as trusted consultants to our senior executives on key decisions and data-driven innovation.

• P2Our data scientists, quantitative analysts, and data management professionals operate effectively in teams to address data and analytics projects.

• P3Our data scientists and analytics professionals understand the business disciplines and processes to which data and analytics are being applied.

• P4We have programs (either internal or in partnership with external organisations) to develop data science analytical skills in our employees.

• P5Our Managers have the skills and knowledge to redesign business processes as a result of implementing Business Analytics projects.

• P6Our Managers have the skills and knowledge to prioritise and manage Business Analytics projects.

• P7Our Managers have the skills and knowledge to translate, communicate and sell the potential values and benefits of Business Analytics to Senior Executives.

• P8Our Managers have the skills and knowledge to manage new innovation as a separate activity to continuous improvement.

• P9Our organisation has an entrepreneurial mindset and vision, with the ability to rationally assess risk and benefits, and have a degree of freedom to pursue value-creating actions.

A.5. Value & Competitive Advantage

• VCA0Our organisation has the ability to apply and interpret data in a manner which meaningfully influences our business.

• VCA1How would you describe your current state of competitive ability in data and analytics?

• 1We are well behind our competitors.

• 2We are behind in some areas.

• 3We are generally at parity with competitors.

• 4We are ahead in most areas.

• 5We are market leading.

• VCA2Our organisation has monetised data as a result of our investment and activities in Business Analytics.

• VCA3We have transformed our organisations business model as a result of our investment and activities in Business Analytics.

• VCA4Which best describes how value is measured when demonstrating the impact of data analytics on your organisation?

• 1No visibility into the value created from analytics initiatives.

• 2Definition of business outcomes is typically established upfront, but measurement is often difficult.

• 3Performance of analytics is measured and managed, but inconsistent across functions and lines of business.

• 4Performance of analytics is managed consistently globally using a well-defined set of financial and non-financial measures.

• 5Analytics initiatives are managed as a portfolio with risk-weighted value assessments impacting resource allocation decisions.

Notes

1. Calculated in R using pwr.r.test (n = 64, r = 0.5, sig.level = 0.05).