Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model

To boardrooms and sustainability: the changing nature of segmentation

Market segmentation is the process by which customers in markets with some heterogeneity are grouped into smaller homogeneous segments of more ‘similar’ customers. A market segment is a group of individuals, groups or organisations sharing similar characteristics and buying behaviour that cause them to have relatively similar needs and purchasing behaviour. Segmentation is not a new concept: for six decades marketers have, in various guises, sought to break-down a market into sub-groups of users, each sharing common needs, buying behavior and marketing requirements. However, this approach to target market strategy development has been rejuvenated in the past few years. Various reasons account for this upsurge in the usage of segmentation, examination of which forms the focus of this white paper. Ready access to data enables faster creation of a segmentation and the testing of propositions to take to market. ‘Big data’ has made the re-thinking of target market segments and value propositions inevitable, desirable, faster and more flexible. The resulting information has presented companies with more topical and consumer-generated insights than ever before. However, many marketers, analytics directors and leadership teams feel over-whelmed by the sheer quantity and immediacy of such data. Analytical prowess in consultants and inside client organisations has benefited from a stepchange, using new heuristics and faster computing power, more topical data and stronger market insights. The approach to segmentation today is much smarter and has stretched well away from the days of limited data explored only with cluster analysis. The coverage and wealth of the solutions are unimaginable when compared to the practices of a few years ago. Then, typically between only six to ten segments were forced into segmentation solutions, so that an organisation could cater for these macro segments operationally as well as understand them intellectually. Now there is the advent of what is commonly recognised as micro segmentation, where the complexity of business operations and customer management requires highly granular thinking. In support of this development, traditional agency/consultancy roles have transitioned into in-house business teams led by data, campaign and business change planners. The challenge has shifted from developing a granular segmentation solution that describes all customers and prospects, into one of enabling an organisation to react to the granularity of the solution, deploying its resources to permit controlled and consistent one-to-one interaction within segments. So whilst the cost of delivering and maintaining the solution has reduced with technology advances, a new set of systems, costs and skills in channel and execution management is required to deliver on this promise. These new capabilities range from rich feature creative and content management solutions, tailored copy design and deployment tools, through to instant messaging middleware solutions that initiate multi-streams of activity in a variety of analytical engines and operational systems. Companies have recruited analytics and insight teams, often headed by senior personnel, such as an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such personnel out-weigh posts for brand and marketing managers. Far more companies possess the in-house expertise necessary to help with segmentation analysis. Some organisations are also seeking to monetise one of the most regularly under-used latent business assets… data. Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a Chief Data Officer. This emerging role has also driven the professionalism of data exploration, using more varied and sophisticated statistical techniques. CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of value propositions and the need to look after core customers; CFOs because segmentation leads to better and more prudent allocation of resources – especially NPD and marketing – around the most important sub-sets of a market; COOs because they need to better look after key customers and improve their satisfaction in service delivery. More and more it is recognised that with a new segmentation comes organisational realignment and change, so most business functions now have an interest in a segmentation project, not only the marketers. Largely as a result of the digital era and the growth of analytics, directors and company leadership teams are becoming used to receiving more extensive market intelligence and quickly updated customer insight, so leading to faster responses to market changes, customer issues, competitor moves and their own performance. This refreshing of insight and a leadership team’s reaction to this intelligence often result in there being more frequent modification of a target market strategy and segmentation decisions. So many projects set up to consider multi-channel strategy and offerings; digital marketing; customer relationship management; brand strategies; new product and service development; the re-thinking of value propositions, and so forth, now routinely commence with a segmentation piece in order to frame the ongoing work. Most organisations have deployed CRM systems and harnessed associated customer data. CRM first requires clarity in segment priorities. The insights from a CRM system help inform the segmentation agenda and steer how they engage with their important customers or prospects. The growth of CRM and its ensuing data have assisted the ongoing deployment of segmentation. One of the biggest changes for segmentation is the extent to which it is now deployed by practitioners in the public and not-for-profit sectors, who are harnessing what is termed social marketing, in order to develop and to execute more shrewdly their targeting, campaigns and messaging. For Marketing per se, the interest in the marketing toolkit from non-profit organisations, has been big news in recent years. At the very heart of the concept of social marketing is the market segmentation process. The extreme rise in the threat to security from global unrest, terrorism and crime has focused the minds of governments, security chiefs and their advisors. As a result, significant resources, intellectual capability, computing and data management have been brought to bear on the problem. The core of this work is the importance of identifying and profiling threats and so mitigating risk. In practice, much of this security and surveillance work harnesses the tools developed for market segmentation and the profiling of different consumer behaviours. This white paper presents the findings from interviews with leading exponents of segmentation and also the insights from a recent study of marketing practitioners relating to their current imperatives and foci. More extensive views of some of these ‘leading lights’ have been sought and are included here in order to showcase the latest developments and to help explain both the ongoing surge of segmentation and the issues under-pinning its practice. The principal trends and developments are thereby presented and discussed in this paper

The impact of strategy on business analytics success

Business analytics systems are an important strategic investment for many organisations and can potentially contribute significantly to firm performance. In this paper we develop a theoretical model, based on the resource-based view, that explains how business analytics capabilities lead to benefits. We argue that the type of strategy, represented as enterprise architecture, moderates the benefits achieved. Two case studies are then presented, each with a different type of strategy, and we explain how and why benefits were achieved from business analytics systems in each. We then identify the similarities and differences between the two case studies and discuss these using five dimensions that emerge from the case studies: strategic alignment, governance, people, organizational culture and data and technology infrastructure.<br /

Increasing the Level of Customer Orientation - A Big Data Case Study from Insurance Industry

The paper positions Big Data as a challenge of information integration into existing analytical infrastructures. The presented arguments have been derived by means of a case study. The case is selected from the domain of insurance industry that intends to leverage the potential of Big Data for the purpose of increased customer orientation. Particularly the application of advanced analytics on a broader information base, i.e. include data that has been collected by the distributed sales force, promised to be a fruitful approach. Yet, we can mainly learn from areas in which the project initially failed. It will become obvious that the ability of a cross-functional process alignment is prerequisite to providing a consolidated view of customer information. It also seems to be essential for integrating external data sources. As a key take away, this paper will provide first heuristics and drafts a maturity model on how these challenges of integration will manifest themselves when applying Big Data techniques

Enabling data-driven decision-making for a Finnish SME: a data lake solution

In the era of big data, data-driven decision-making has become a key success factor for companies of all sizes. Technological development has made it possible to store, process and analyse vast amounts of data effectively. The availability of cloud computing services has lowered the costs of data analysis. Even small businesses have access to advanced technical solutions, such as data lakes and machine learning applications. Data-driven decision-making requires integrating relevant data from various sources. Data has to be extracted from distributed internal and external systems and stored into a centralised system that enables processing and analysing it for meaningful insights. Data can be structured, semi-structured or unstructured. Data lakes have emerged as a solution for storing vast amounts of data, including a growing amount of unstructured data, in a cost-effective manner. The rise of the SaaS model has led to companies abandoning on-premise software. This blurs the line between internal and external data as the company’s own data is actually maintained by a third-party. Most enterprise software targeted for small businesses are provided through the SaaS model. Small businesses are facing the challenge of adopting data-driven decision-making, while having limited visibility to their own data. In this thesis, we study how small businesses can take advantage of data-driven decision-making by leveraging cloud computing services. We found that the report- ing features of SaaS based business applications used by our case company, a sales oriented SME, were insufficient for detailed analysis. Data-driven decision-making required aggregating data from multiple systems, causing excessive manual labour. A cloud based data lake solution was found to be a cost-effective solution for creating a centralised repository and automated data integration. It enabled management to visualise customer and sales data and to assess the effectiveness of marketing efforts. Better skills at data analysis among the managers of the case company would have been detrimental to obtaining the full benefits of the solution

Big data in radio broadcasting companies: applications and evolution

The radio broadcasting industry is facing a process of profound digital transformation throughout which, over the last 20 years, the strategies to preserve the traditional business model have prevailed. The consolidation of platformization and datafication in the economic management of the media requires adaptation of the radio broadcasting sector’s structures, management models, and corporate culture. Through an exhaustive bibliographic review, nonparticipant observation, and in-depth interviews conducted with heads of the systems, sales and marketing, content, and digital and innovation departments of the three leading Spanish companies (Prisa Radio, Grupo COPE, and Atresmedia Radio) and the state public broadcaster (RNE), we seek to identify the functional areas of the radio broadcasting company in which big data (BD) has a greater potential for application, trying to establish the differences in its utilization in the analogue and digital business model. The results revealed that the degree of BD implementation in the Spanish radio broadcasting industry was significantly different between the private sector –which within the last 2 or 3 years has begun to introduce, very incipiently, big data management, applied primarily to the analysis of digital audiences, these users’ consumer behavior, and business management– and the public sector, which so far has not adopted these technologies on a systematic basis

An analysis of the use of market intelligence data by senior business leaders – the development of a new model (ICSAR) for the identification and implementation of specifically focused data

Big data, analytics and data science are terms that have come to represent a growing focus on decision making built on the foundation of market intelligence data. The enthusiasm for this form of evidence-based decision making has grown with the ability for businesses to better track their customers, competitors and market. Strategy firms such as McKinsey and company have also added to the discussion by highlighting the potential for data to improve business efficiency. News headlines such as 'Big data: The next frontier for innovation, competition, and productivity' (McKinsey and Company, 2011) and 'Data Scientist: The Sexiest Job of the 21st Century' (Harvard Business Review, 2012) are two examples illustrating the optimism for data use in business activities. The ability to better track customer and markets has resulted from the development of technology and the transition to more digital services. For example, a growing number of businesses offer their services and products based on a subscription model through the internet. Software-as-a-Service is one example of this. With many products now available in the digital space, there has been a corresponding increase in the volume and variety of data sources available to business leaders. For example, software services hosted in the digital space mean enhanced customer behaviour insights because digital forms and ‘clicks’ can be monitored and analysed. Marketing departments now have an enhanced ability to conduct rapid testing of video marketing content through social media that is faster and cheaper than testing two different television commercials. The move to more digital and mobile-based services is a phenomenon that has occurred in all industries and has given business leaders access to more data sources than ever before. In theory, this should support better decision making because the amount of information has grown rapidly. However, academic studies have shown that overwhelming levels of information resulted in poorer decision making ability. Industry analysts have also extensively commented that the large variety of data sources have made it more difficult to know which data sources to use when making decisions. These points raised questions about how business leaders were selecting from the growing variety of data sources and what factors influenced that selection process. From there, the question was raised about how data was being used in decision making. Answering these questions holds significant potential for businesses. Understanding limitations to data use and applying this knowledge in a structured way has the potential to ensure data is used objectively and holistically in decision making. The result is that businesses are better able to take advantage of market intelligence and extract the greatest value from its organizational knowledge. This research studied what data sources were used by business leaders, how the data was used in their day-to-day projects and what factors led to the selection of a data source over another in the decision making process. The research was an exploratory approach using a mixed methodology that included in-depth interviews, a survey and a case study. The research deliberately focused on senior business leaders to ensure the research participants were at the level that was most likely to be in a position to make decisions. The research found that there was a varied approach to data use with multiple factors being involved in how data was used. The first finding was that most business leaders used a variety of data sources. However, data sources were selected based on a hierarchy that was specific to each individual business leader and data sources were not used consistently. The hierarchy was subjective and was based on several factors shown in the second finding. There was not a standardised approach to the use of any single data source meaning a data source like surveys could be used for behavioural tracking by one business leader and for logo feedback by another, for example. This highlighted the need for organisations to educate business leaders on the best data source for answering different business questions and to put structure around how data sources were used. Second, the research showed there were four types of influence involved in selection of data sources. Those four influence types were organisational demographics, personal experience with a data source, time-based needs and project requirements. These four factors led to the subjective selection of data by business leaders. For example, a business leader was more likely to use a familiar data source even if there was a non-familiar data source that would have been more accurate. Additionally, business leaders were found to forgo accuracy in favour of a data source that was available more quickly. This highlighted the need for a framework that minimised the subjectivity involved in choosing a data source and encouraged objective data use. The third finding was that there was mix of data maturity levels and that most organisations did not have an integrated approach to data use. The possible cause of this was that many organisations lacked data leadership to ensure that data use in decision making was structured and holistic across the business. Instead, this study found silos between teams that resulted in duplicated or contradictory use of data and individual data sources used inconsistently. This highlighted the gap between the potential of market intelligence and the lack of organizational structures to support effective data use. It also showed the need for organisations to invest in data use structures and frameworks to complement data collection investments. These findings showed that businesses seeking to capitalise on the growing number of data sources needed to examine whether business leaders were using data effectively. The finding that there was a degree of subjectivity in the selection of a data source suggests businesses needed to invest in a decision making framework that ensured a data source was used objectively and based on its ability to meet the project needs. This led to the final section of this research which was the development of the ICSAR model for data use. The ICSAR model was designed by the research author as a five step framework that provides business leaders with a structured approach to selecting and using data objectively in decision making. The model was created based on the research findings in order to support business leaders to enhance their data use and to avoid the subjective influences. The design also promotes objective data use by tying research insights to organisational learning and is cyclical to ensure insights are continually developed