Analytics Services Unit. Operationalization of big data analytics into existing organizational structures

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1 Analytics Services Unit Operationalization of big data analytics into existing organizational structures

2 I. The digital transformation of enterprises makes Data Science & Analytics more accessible yet also more essential Data Science & Analytics has gained increasing attention from companies and private customers in recent years. With regard to the opportunities associated with the digitalized future, four trends in digital transformation have emerged with a direct reciprocal infl uence on companies operating models: 1. Changing business environment due to digital transformation. As with the internet, digitization can extend the reach of organizations, improve management decisions and accelerate the development of new products and services. At the same time, the rapid adoption of technologies can disrupt traditional business models. 2. Growing availability of customer & environmental data. Nowadays, petabytes of information are freely available to improve business performance by intensively using data in the decision-making process The emergence of easy to use tools for visualizing and exploring customer data. Even though companies might own an incredible amount of data, the need to effi ciently turn this data into valuable insights still remains. In addition, many more types of operational and tactical decisions begin to be automated, especially in machine-to-machine communication. 4. Increasing competition and upward pressure on prices. In order to stay competitive in the global market, companies require continuous development of innovative solutions while at the same time maintain low cost levels. Organizations need to effi ciently use resources and continue to drive standardization. These trends are at the core of the ideas presented in this Point of View, which aims to introduce a solution that addresses all associated challenges and opportunities and to integrate them into the given organization and governance. In order to cope with the necessary speed of innovation and deployment, an analytical transformation towards a more agile as well as more standardized way of working is needed. This can be achieved through the Analytics Services Unit. The vision of a platform that leverages the data innovation Analytics can be applied to unfold the potential of data by generating competitive insights and embedding these into business strategy and operations. However, the speed with which companies convert data into insights and insights into action is now a critical differentiator. This gives rise to the need for an effi cient and effective delivery solutions of Data Science & Analytics solution from prototyping and projects towards a deep integration into the line organization. More specifi cally, we envision an Analytics Services Unit which creates business value by developing innovative, easy to use analytics solutions that are distributed within a corporate network in a costeffi cient manner following defi ned standards and quality measures. The mission of the unit is to: Explore data opportunities & innovate Coordinate data projects Defi ne processes & manage change Provide methods and tools Ensure data availability and quality Figure 1: Mission of an Analytics Services Unit EXPLORE DATA OPPORTUNITIES & INNOVATE COORDINATE DATA PROJECTS DEFINE PROCESSES & MANAGE CHANGE PROVIDE METHODS & TOOLS ENSURE DATA AVAILABILITY & QUALITY Develop innovative solutions and initiatives Analyze data and mash up them to find new insights that will help different entities Be proactive to propose new services based on data Evaluate business value of analytics initiatives Ensure a harmonized response to similar needs coming from different entities Contribute to coordinate the data-driven initiatives Contribute to the global governance and be responsible of the consistency between initiatives Define the data management processes and the standards to organize different files, tables of data etc. Animate the data management network to ensure initiatives consistency Be responsible of the deployment of those processes and standards on the defined perimeter Manage relationships and lobbying with external partners Define the global data framework Be responsible for the global data framework Specify requirements in terms of data technologies: storage, statistical tools, data visualization tools, etc. Develop tools and netwoks that will allow internal stakeholders to access files and tables of data Develop a 360 vision of data available within the company: global and exhaustive data cartography, with the quality level associated Be responsible for an ergonomic and easy access to data. Capgemini Consulting Capgemini: IT Trends Analytics Services Unit

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4 II. How to build an Analytics Services Unit with real business impact? Setting the ground requirements and conditions Before designing the structures of an Analytics Services Unit, key requirements and conditions must be adequately addressed and established to make it a valuable partner to business. First, projects taken on by the Analytics Services Unit need to have a business owner who is responsible for the business objectives and profitability of an initiative. This can be a department head or a board member who has spotted a value added Data Science & Analytics solution for the business. Second, data quality, its relevancy to the specific business as well as an adequate handling of a large variety of data in terms of technical and legal consideration is a major challenge. This makes a holistic data mandate governing the access of data indispensable. The data mandate includes data sourcing, storage and deployment. Within data sourcing, trustful data providers from among the seemingly infinite number of vendors have to be selected while considering internal as well as external sources. Unlocked and not yet exhausted valuable data is oftentimes available internally. Additionally, the involvement of external providers oftentimes makes sense to enrich internal data. Although external data is valuable due to their variety, it has to be carefully analyzed in terms of legal and value added aspects. The goal is to design a standardized and reliable purchasing process for data and analytics software licenses. Next, the purchased data must be adequately stored and enriched with existing data to achieve consistency and completeness. A lean quality check has to be established based on pre-determined privacy & security templates to uphold high data quality standards. Data usage and deployment guidelines in the form of access rights need to be laid out, determining the required amount of licenses and the access rights to avoid legal complexity and unnecessary costs. Finally, as a fundamental requirement, an appropriate system/ IT infrastructure must be in place. For instance, the existing IT infrastructures need to be expanded and adjusted to be able to incorporate and handle new data sources such as unstructured data or log streams. Design criteria To establish an efficient and effective design of the Analytics Services Unit it is important to understand and delineate it from the setup and purpose of a business intelligence unit. While a business intelligence unit provides standard ad-hoc reports and drill-downs, a Business Analytics Services Unit is more closely linked to the actual business in facilitating strategic decisions. These two units exhibit a diametrically opposed relationship and their organizational designs are hence not mutually interchangeable. When designing a Business Analytics Services Unit, funding can determine its strategic direction and should be considered carefully. Setting up the unit as a cost center means regarding it as an expense which does add to the overall revenue and profit. The focus here is to keep the overall costs low and restrict the autonomy of the unit. When accounting for the unit as a profit center, it is treated as a separate business within the corporate framework. The unit is then accountable for own revenues and costs with the aim to establish market structures and generate profit for the company. Furthermore, it is important to define from the beginning necessary roles and responsibilities including detailed drafts of role profiles. For instance, introducing a Chief Data Officer (CDO) who is clearly delineated from the role of Chief Information Officer (CIO) should be considered. While both share the same IT infrastructure, the CDO acts as a business innovator and is more involved in setting the direction of the corporate strategy, which then forms the basis for the IT strategy requirements. A CDO can at the same time interfere in the business and the IT strategy. Embedding the new unit into existing structures Governance. An analytical transformation is always accompanied by a change in the organizational governance due to a higher degree of global cooperation, an increasing number of interfaces within the organization and finally a flatter hierarchy to enable innovation. Therefore, defining the type of governance model between business units (BU) and headquarter function management (HQ) is an essential pre-requisite for the roll-out. The first model is governance through business units, which separates the 4 Analytics Services Unit

5 service between business unit and headquarter function management. This allows for a greater proximity while ensuring less resistance from the business unit. However, top management does not have any ownership of the shared services. The second model is governance through headquarter function management, where headquarters provides most of the services that are available to all business units. While each business unit works independently of one another, the model has the potential to create silos and resistance, thus limiting their proximity. The last model describes an independent governance model, where independent governance relies on minimized silos and functional barriers. In other words, the organization functions are independent of the BU and HQ and thus enabling greater focus and higher visibility, which lead to the creation of a top management position. Regardless of the selected model the key lies in having a central governance function that operationalizes the interfaces and is responsible for the control of all processes. For example, the governance body needs to set up frameworks, create service level agreements (SLAs) for orders in terms of productivity, cost and quality. Nevertheless, it would appear that most companies feel comfortable starting to build a governance body through a business unit and then gradually moving towards an independent governance function. Furthermore, in order to manage innovation and the delivery of services within an Analytics Services Unit, it is essential to differentiate between the setups of these two components the Innovation Lab and Shared Services Delivery Unit: Innovation Lab The Innovation Lab initiates the kick-start for requisitioning new data sources and developing novel analytics solutions. It is also the place where initiatives are planned, coordinated and performed in order to transfer Data Science & Analytics solutions to the line organization. As the owner of the innovation process, functionality and partner coordination in the Innovation Lab is key to make the Analytics Services Unit work. Goal and value proposition. The fi rst and foremost goal of the Innovation Lab is to drive pilots with an innovative character to establish an early proof of concept (PoC) resulting in meaningful Data Science & Analytics solutions which can be quickly applied within the organization. This includes technicalities such as model building together with causality and plausibility checks to prove and validate model quality. Furthermore, business understanding of the data at hand is established. Prior to execution, the Innovation Lab defi nes requirements in terms of required data, software solutions and partnerships. Being responsible for the business purpose of a developed solution, the Innovation Lab must also defi ne customer expectations, evaluate requests as well as safeguard acceptance among stakeholders resulting in the design of an effective service catalog. This requires continuous and strong collaboration with the business. Architecture. In order to encourage creativity, the Innovation Lab should be detached from the existing organizational framework and allow for fl at structures, an Figure 2: Governance models Governance through Business Units Governance through Headquarter Function Management Independent Governance Model CEO/COO CEO/COO Supervisory organ CEO/COO Management of Business Units Headquarter Function Mgmt. Management of Business Units Headquarter Function Mgmt. Analytics Service Unit 1 Business Units Headquarter Function Mgmt. BU 1 BU 2 BU 3 Analytics Service Unit 1 BU 1 BU 2 BU 3 Analytics Service Unit 1 Capgemini Consulting Analytics Services Unit is a platform that enables innovative development and standardized delivery of analytics solutions within a company 5

6 independent culture as well as alternative incentive systems. Cross-disciplinary teams consisting of project managers, legal experts, data scientist, mathematicians, and business domain experts who periodically meet with department heads will ensure valuable solutions that are relevant to the business. A strong emphasis should be put on acquiring people with the right skill set comprising business domain expertise, modeling and IT know-how. Typically, those resources are difficult to attract due to the rarity of the skills. Therefore, incentives should be based on qualitative as well quantitative KPIs putting an emphasis on team effort. An important consideration is establishing partnerships with other industry players, scientific institutions or start-ups as these can lead to a faster solution development based on sharing best practices and innovations. Furthermore, project management techniques such as Scrum facilitate an effective product development by providing agility allowing for sequential adjustments of service offerings. Shared Services Delivery Unit The Shared Services Delivery Unit (SSDU) operationalizes the service provision on a transactional level and acts in a standardized way, following line management principles to ensure both high productivity and cost sensitivity. In other words, it consolidates additional support services and thereby extends the scope of traditional support platforms to business processes, providing standardized services for operational functions at lower cost. This will be essential as approximately 60% of all innovations developed in the Innovation Lab will be rolled out in the Shared Service Delivery Unit. Goal and value proposition. Due to continued cost pressures, fragmented global markets as well as a lack of standardization and transparency, the Shared Services Delivery Unit addresses four significant levers. First, it lowers operational costs significantly by ensuring standardization and deriving synergies from a common governance and delivery model. Second, the Shared Service Delivery Unit enables a higher efficiency and productivity by enabling companies to pool resources and to utilize reserve capacities. Third, a reduced complexity achieved by implementing standardized processes and shared solutions will increase flexibility and help in meeting time-to-market business needs. Finally, the generation and sharing of internal knowledge will improve the quality of service and enable a faster progress towards analytics maturity. Architecture. The SSDU has a hierarchical structure and is managed by the analytical service catalog that documents the structure, objectives and requirements of standard analytical services. It represents a menu of services that a SSDU offers, while finding an analytical approach to map business questions to information objects and information systems that the business can understand. Furthermore, the SSDU has different skills requirements compared to the Innovation Lab. Due to the standardized delivery of services, there is a strong focus on performance monitoring and data analyzing. Another factor and advantage consist in the use of rare resources, such as data scientists, that can be deployed more intelligently and efficiently. As a result, fewer of them are needed. In addition, the SSDU offers different incentive systems to achieve different outcomes compared to the Innovation Lab. In fact, the incentive system is based on measureable results and is therefore mostly driven by quantitative transactional KPIs, such as error rates, speed and frequency of service delivery, etc. 6 Analytics Services Unit

7 The Interface between Innovation Lab and Shared Service Delivery Unit The Innovation Lab and the Shared Service Delivery Unit together constitute the Analytics Services Unit. In order to connect both components and to roll out the newly developed solutions, an effective standard interface is required. After the development of a proof of concept (PoC), a live test is conducted in the Innovation Lab. It is critical to identify criteria for evaluating the success of the pilot and consequently the potential for a companywide roll-out of the solution. As soon as the solution is transferred to the SSDU, high quality distribution of the solution across business departments and continuous improvement of transferred solutions are ensured through close performance monitoring by the Innovation Lab. Delivery Network. A key consideration when designing the Analytics Services Unit is the geographical span of the delivery network. A global innovation hub requires a lot of central resources and expertise and should be under close control by strategic decision makers. In addition, local delivery is important in order to be able to tailor services to specifi c regional needs and decrease legal and organizational complexity. Figure 3: Integration of Analytics Services Unit into Operating Model Analytics Services Unit Shared Service Delivery Unit Innovation Lab Requirements Management Additions to Service Catalogue Discovery Lab (Identification of potentials/ ideas) Service Catalogue Service Service Service Service Service Partner ecosystem (internal & external) Service Delivery (Implementing & Roll-out) Signal Library Data Lab (Proof-of-concept & Live-Testing) Solutions/ Recommendations for: Data integration Sales Marketing Finance... Capgemini Consulting

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9 III. Measuring the benefits Understanding the benefits In a longstanding study in cooperation with the MIT Center for Digital Business we could demonstrate that companies with a stronger digital intensity derive more revenue from their physical assets and are more profitable than other players in their peer group. 2 With regard to these opportunities, the Analytics Services Unit aims to strengthen the core business and enable a new analytical transformation: Generation of internal knowledge due to access to scarce, high quality and proven analytics resources Enablement of faster progress towards analytics maturity Marketing of data and data insights through an enterprise-wide approach to data management and analytics Optimization of business processes due to enhanced process excellence and effectiveness of decision making Optimization of offers towards customers Reduction of internal costs by leveraging a shared services platform Quantifying the benefits Prior to developing a new solution. As for every project, a new solution needs a clear baseline on which changes and performance can be measured and where potential benefits to the business-as-usual strategy can be compared. Similar to a venture capitalist approach, employees in the Innovation Lab and from the business need to build a business case into a pilot by defining criteria such as KPIs based on strategic corporate goals. As a matter of course, a solution will only be introduced if the business case meets predefined criteria and shows significant benefits that outweigh the additional costs. After introduction of a new service. Once a service is rolled out through a standard interface in the SSDU, clear and measureable targets related to efficiency, cost control and service quality are defined. As the performance is regularly reported and monitored by customers and providers, different measurements are required. In general, a service level agreement based on customer requirements and responsibilities forms the basis for KPIs and global delivery standards. It serves as end-to-end process governance between customers and the Analytics Services Unit and aims to cover the broad spectrum of the analytics operation model. As an example, the KPI model is typically clustered in five dimensions: Financial KPIs: Metrics related to business benefits Customer-specific KPIs: Metrics related to service recipient requirements Operations-specific KPIs: Metrics related to process productivity & quality Analytical services-specific KPIs: Metrics related to product performance & quality Employee KPIs: Metrics related to people and performance Quantifying the benefits is essential to understand the added value of analytics and promote its advantages to the service customers. In addition, this allows for a benchmarking within and outside of the industry, offers feedback options and enables the tracking of performance over time. 2 Capgemini Consulting: The Digital Advantage - How Digital Leaders Outperform their Peers in Every Industry 9

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11 Case Study Development of an Analytics Service Unit for a leading manufacturing company Situation of the client: The client started different data driven initiatives and pilots which were mostly at a proof-of-concept status The client formed a vision to be a data-driven and data-based decision making company and therefore addressed the set up of an analytics department This department and the Analytics Services Unit needed an operating model structure for this purpose Capgemini`s value proposition: The operating model structure was developed step by step together with the client and accompanied by acceleration workshops The operating model building blocks could already be used in the project phases since the consulting team worked along the roles of the operating model The added value was in its innovation of combining analytical and business requirements in the structure Benefits for the client: Clear structure and defi ned roles to drive the topic further and address change in the organization Room in the model for business innovation (use case generation) and analytical innovation (model enhancement) The inclusion of stakeholder and internal customers of the analytical services and addressed steps for delivery of the analytical services 11

12 Contact: Volker Darius Capgemini Consulting Marina Gertsberg Capgemini Consulting Philipp Wagner Capgemini Consulting Luyi Wang Capgemini Consulting About Capgemini Consulting Capgemini Consulting is the global strategy and transformation consulting organization of the Capgemini Group, specializing in advising and supporting enterprises in significant transformation, from innovative strategy to execution and with an unstinting focus on results. With the new digital economy creating significant disruptions and opportunities, our global team of over 3,600 talented individuals work with leading companies and governments to master Digital Transformation, drawing on our understanding of the digital economy and our leadership in business transformation and organizational change. Learn more about us at About Capgemini With almost 140,000 people in over 40 countries, Capgemini is one of the world s foremost providers of consulting, technology and outsourcing services. The Group reported 2013 global revenues of EUR 10.1 billion. Together with its clients, Capgemini creates and delivers business and technology solutions that fit their needs and drive the results they want. A deeply multicultural organization, Capgemini has developed its own way of working, the Collaborative Business Experience TM, and draws on Rightshore, its worldwide delivery model. Learn more about us at The information contained in this document is proprietary Capgemini. All rights reserved. Rightshore is a trademark belonging to Capgemini.