Data Management Emerging Trends Sourabh Mukherjee Data Management Practice Head, India Accenture
Data has always been an important asset for companies as it is the basis for making business decisions. The speed, volume and variety of unstructured and semistructured data originating outside the enterprise firewall are compelling companies to revisit their traditional data management strategies. The need to enhance the value of master data with intelligence available in social media is leading to the emergence of Social Master Data Management (MDM). Budget optimization considerations are encouraging organizations to warm up to alternate delivery mechanisms such as Software as a Service (SaaS) and Business Process as a Service (BPaaS). Leading MDM vendors are, therefore, investing in cloud-enabled variants of MDM. Finally, to leverage the relationship among all master data within an organization across multiple domains customers, products, suppliers, employees, location to mention a few we are seeing the emergence of Multidomain MDM solutions. This article focuses on key trends within master data management that businesses are exploring to gain competitive edge. Multidomain MDM The business value of information is significantly enhanced by leveraging the relationship among all master data within an organization across multiple domains. Multidomain MDM solutions empower companies to improve operations. They give business users access to not only a single, trusted view of businesscritical data about customers, products, channel partners and employees but also provide a 360 degree view of relationships among domains. Companies may implement single or multidomain MDM solutions, depending upon their focus. If the focus of a company is on cleaning up and standardizing a data domain rather than business processes in general, then it makes sense to invest in a solution that s best suited for that domain within a particular industry. If, for example, the focus is on customer data, then why should a company buy a generic multidomain MDM solution and spend on post-procurement extension and configuration? Or, why should it even risk investing in a very expensive multidomain MDM solution that has not been tested for a specific industry? These are considerations that a company needs to address right at the outset. If, however, the focus is on increasing the effectiveness of business processes, multidomain MDM solutions can be leveraged to capture data in a holistic manner. For example, if a retail company wants to improve the quality of customer interactions, it would need information on which products customers are buying, from which locations, how frequently are they purchasing specific products, and much more. To optimize the supply chain process, the company will need to know about the product range of its suppliers, together with their respective locations. The main aim of MDM is to remove data silos and create a consolidated single version of truth. By taking the single domain route and ending up with a master data hub for every domain (a hub for customer data, one for product data, another for supplier data and so on), we are defeating the very philosophy of MDM. Single domain MDM solutions call for capabilities in specific industries, together with in depth knowledge about domains and data models. By contrast, multidomain MDM solutions facilitate flexible cross domain interactions across industries and are highly configurable. The success of multidomain MDM solutions lies in how effectively a company is able to design orchestrated business processes involving diverse domains and the relationships among them. MDM and big data With numerous sources and types of data from within and outside the enterprise, the data landscape has become very complex. Businesses are still struggling to get a grip on master data, metadata, reference data and huge volumes of transactional data. Adding to the complexity is the fact that while some data originates from enterprise applications, a significant amount of data originates from outside the enterprise firewall. This includes a variety of structured, semistructured and unstructured data that is sourced or syndicated from external data providers. 2
Until recently, master data was considered to be low on velocity, variety and volume. However, today s rapidly changing business environment is compelling master data to become more dynamic and situation-aware. That is because there are plenty of insights that need to be captured from semistructured and unstructured data. These insights are crucial for enhancing the quality of master data and thereby the quality of business decisions that leverage that master data. An MDM hub is not meant to be a repository of big data. However, master data attributes derived from social media information and audiovisual as well as machinegenerated data can be integrated with traditional 360-degree views of customers, products, suppliers and other data domains. Businesses can benefit from the integration as it enhances the master data by providing insights into social relationships, affiliations and customer sentiments towards products or services and customer intent. MDM is a good starting point for many big data analytics projects. For instance, a company may want to find out how the most preferred customers feel about its most profitable products, or analyze their sentiments about the quality of service on its channels. In such a case, it would be very fruitful for the company to identify the relevant product and customer data sets for analysis from the master data hubs. Similarly, for marketing initiatives discounts, tailored packages specific to customers and their circle of influence, for example companies can tap into the specific customer, product and location data along with information about each of the customer s social relationships and affiliations. This is a classic case of symbiosis between master data and big data. Since the MDM hub is where a company already keeps the most complete view of its customers, products, accounts and other domains, it is also the most logical place for generating insights about those domains for big data analytics. With the rapid growth of e-commerce, recommendation engines are gaining currency. These engines typically produce a list of recommendations in one of two ways through collaborative filtering or content-based filtering. Collaborative filtering builds a model from the user s past behavior (for example, items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users, then uses that model to predict items that may be of interest to the user. Popular examples of recommendation engines include, though not limited to: Amazon.com 1 : When users browse for a particular product, the online store recommends additional items based on what other shoppers bought along with the currently selected item. Netflix 2 : Based on the user s previous ratings and selection habits, the online store offers specific genre of movies to targeted users. Facebook, 3 LinkedIn, 4 and other social networks: Based on the existing network of a user, these sites use collaborative filtering to recommend new friends, groups, and other social connections. Content-based filtering approach uses a series of discrete characteristics of an item (for example, music, book or movie) in order to recommend additional items with similar characteristics. It is evident that the design of recommendation engines needs to be based on a sound understanding of user preferences of product features. It also needs to consider user networks and user interaction patterns across channels with the enterprise. This necessitates building a robust foundation of customer and product data which MDM can provide. MDM and cloud Historically, on-premise MDM systems have required heavy investments in hardware infrastructure, software development, customization, maintenance, security and deployment of the MDM solutions. Today, businesses need data solutions that are not only highly flexible, scalable and available, but also offer a flexible cost structure. Businesses expect the solutions to provide high data security and integrate easily with the 1 http://www.amazon.com/ 2 https://signup.netflix.com/global 3 https://www.facebook.com/ 4 http://www.linkedin.com/ 3
enterprise applications as well as with other cloud-based solutions or apps for the mobile platform. Mobile platforms are already redefining data collection and consumption strategies. Through social communities, consumers are influencing product acceptance and brand image, and there is a growing demand for information in real time. Innovation in cloud technologies are creating new business models for MDM Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS). By leveraging these mechanisms, MDM solutions can be rolled out faster as they use tested, audited and certified infrastructure and reusable solution components with relatively low upfront investments. The upfront activities usually take the form of minimal customization for a specific client, integration with existing enterprise applications or external data sources, or client-specific data cleansing and initial data load action. The cloud-based MDM platform integrates more easily with other cloud-based applications or apps for the mobile platform than with an on-premise MDM solution, requiring extensive customizations. Moreover, since vendors generally offer a service level agreement (SLA)-driven service, clients have a predictable cost structure in the pay-as-yougo model. As a result, the upfront capital expenditure for setting up an MDM solution is translated into a more predictable operational expense structure. The cloud-based MDM journey calls for defining a clear MDM strategy. This implies developing a hosting strategy, identifying an MDM hosting partner as well as a SaaS provider and exploring BPaaS options for ongoing data stewardship in collaboration with (but minimal) involvement of business users. It also means having a clear governance strategy, together with data stewardship services and service SLA tracking. Another, equally important aspect of the MDM strategy is to develop an execution strategy for customizing and integrating the core enterprise systems with the cloud-based MDM solution. At Accenture It is critical that master data is accurate, consistent and accessible across the enterprise. A sound data management approach should span the enterprise across multiple lines of business and be driven by business stakeholders with support from IT. The master data management initiatives at Accenture follow a comprehensive approach involving a series of phases strategy development and analysis, people enablement, data preparation, process engineering, technology selection, implementation and change management. Our data management approach encompasses data architecture, data quality, data integration, master-meta-reference data management, data security, data stewardship and governance. This holistic approach isolates the sources of inaccuracies and establishes data management processes and governance structures that deliver one vision of the truth across the enterprise. Accenture s proprietary master data management assets help clients identify opportunities for improving their data management capabilities and generate value from their master data quickly. Our data management framework provides a proven platform for integrating all components of a successful data management strategy, including industry standard data models and business rules, methodologies and best practices around system integration, business process reengineering and data and process governance. Accenture has a dedicated Data Management Practice in India with expertise in a variety of leading technologies Informatica, IBM, SAP, Oracle and TIBCO for data management strategy, implementation and sustenance requirements across multiple industries. Our strong alliance partnerships with product vendors ensure we get exclusive updates on product releases and invitations to their boot camp trainings and other forums. Furthermore, we are also the trusted partner of several product vendors in their go-tomarket initiatives globally. Realizing the full potential of enterprise data Increasing business demand for actionable insights, real-time analysis and one version of truth, together with cloud, big data and analytics technologies are 4
beginning to give us a glimpse of what is possible with data. However, to harvest maximum benefits from enterprise data, it is important for companies to align their traditional data management strategies with an MDM and data quality strategy that leverages emerging technologies. About Accenture Accenture is a global management consulting, technology services and outsourcing company, with approximately 275,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$28.6 billion for the fiscal year ended Aug. 31, 2013. Its home page is www.accenture.com Copyright 2013 Accenture All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. 5