Is Your Data Management Ready For Systems Of Insight?

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1 Is Your Data Management Ready For Systems Of Insight? by Michele Goetz July, 0 Why Read This Report Systems of insight (SOI) will create a culture where what you do with the data is more than how you manage it. Enterprise architecture (EA) professionals will need to adopt the core values of SOI and put the right practices and competencies in place to manage data with an eye toward actions and outcomes, collaboration, test-and-learn, and agility. This report provides a detailed assessment to help EA pros plan their path toward a data management practice positioned for today s insight needs and tomorrow s intelligent digital insights ecosystem. Key Takeaways Data Is Only As Good As The Insight It Produces The goal of data management isn t to manage the data; it s to get the best insights from the data on which to take action. Position your data management practice to support systems of insight to achieve the business outcomes it deserves. Adopt SOI Core Values As A Framework For Data Management Competency SOI feeds on outcomes, is hypercollaborative, operates at scale, empowers consumers, and is fast. Data management practices must enable each SOI core value within the data as well as across people, processes, and technology. Assess Data Management With Internal And External Perspectives A data management center of excellence (DMCoE) objective is to meet internal expectations, but more ly, what do external stakeholders think? Assess the DMCoE in the context of the business case for data, internal operational effectiveness, and efficiency of data systems. forrester.com

2 by Michele Goetz with Leslie Owens, Boris Evelson, Brian Hopkins, Srividya Sridharan, Gene Leganza, and Shaun McGovern July, 0 Table Of Contents Notes & Resources Systems Of Insight Will Disrupt Your Data Management Practice Seven Dimensions To Assess Data Management Readiness For SOI Business Perception Defines Business Alignment Data Governance Creates The Collaborative Operating Model With Business Partners Process Positions Data Management For Efficiency, Agility, And Effectiveness Organizations Are Built On Skills Fit For Data Management Tasks And Results Technology Extends Across The Entire Data Supply Chain Data Delivery And Use Empowers Self- Service And Satisfies Insight Demands Measurement Reinforces Best Practices And Promotes A Test-And-Learn Culture This report is based on ongoing research into data management capabilities, including dozens of client inquiries and vendor briefings. Related Research Documents The Data Trust Trinity Brings Data To The Business Decision Table Digital Insights Are The New Currency Of Business Do Data Quality The Big Data Way What It Means 0 SOI Prepares Firms For What s Next Artificial Intelligence Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA 00 USA Fax: forrester.com 0 Forrester Research, Inc. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

3 July, 0 Systems Of Insight Will Disrupt Your Data Management Practice Data is only as good as the insights it produces, the actions it influences, and the results it fosters. That s the secret recipe for data management. Although big data dazzles us all with promises of business-changing insights, technology management is still the largest user and benefactor. For enterprise architects to advance the business with data, data management must become oriented toward business outcomes, not technology management outcomes. During research conducted across firms, Forrester found digital businesses, and some enterprises, reinventing their business models to create centers of excellence for systems of insight. What makes these firms different is that they embrace an insight manifesto with five core values: Feed on outcomes. The system is self-reinforcing through continuously planning, executing, and learning from the results of insights introduced into business processes and decisions made. Be hypercollaborative. Insights teams are a cultural part of all areas of the business; these teams create, share, and refine insights in the context of business objectives. Operate at scale. Federation replaces centralization at all data management tiers: organization, process, architecture, and performance. Empower consumers. Everyone s an analyst, with access to data and tools to derive, use, and share insights. Move fast. The data supply chain is frictionless and allows insights to emerge when needed, to be applied in the context of action, and to speed business results. Seven Dimensions To Assess Data Management Readiness For SOI Priorities and competency will determine the EA data management course to support systems of insight. Core technology management competencies (process, organization, and technology) emphasize the foundations of enablement with an eye toward flexibility, agility, and skills. SOI competencies push EA pros to expand their data management practices to emphasize the SOI core values that produce insights to attain business outcomes and renew business models. Business Perception Defines Business Alignment Business decision-makers view big data investments as mechanisms to optimize their resources, i.e., technology management objectives. Yet their expectations are that data will improve customer experience and increase revenue, i.e., SOI objectives. EA pros should transition to an SOI data management model by assessing data management success against business stakeholders perceptions and the tangible outcomes they realize (see Figure ). 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

4 July, 0 FIGURE Put Data Management Practices In Place To Show Business Leaders What s In It For Them How would you rate the importance of the following statements? Not very Somewhat Important Very How well do you execute on the following statements? Not very well Well Very well Extremely well Business alignment Data management is considered a strategic capability within the business by executives and senior management. Data is used for strategic planning at the executive level, within lines of business, and within departments. Developing business cases and investing in data is a wholly collaborative effort with business-area stakeholders and executives. Data is measurably optimizing and improving the effectiveness and efficiency of key business processes across the enterprise. Executives, senior management, and line-of-business managers agree the data strategy is well aligned with business strategy. Business stakeholders feel that information delivered is of the appropriate level of quality to meet their needs. Data management receives the appropriate levels of funding and investment to support business data needs and requests Importance Execution Score Note: Importance minus execution = score Average Data Governance Creates The Collaborative Operating Model With Business Partners Data governance is a process and outcome and is ineffective without the right balance of collaboration among EA pros, business data owners and subject matter experts, and executives. If you re still selling data governance to the business, you don t have data governance; you have a technology management process that will produce technology management outcomes (see Figure ). 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

5 July, 0 FIGURE Co-Lead Data Management And Strategy With Business Leaders How would you rate the importance of the following statements? Not very Somewhat Important Very How well do you execute on the following statements? Not very well Well Very well Extremely well Data governance Data governance policies are tailored to specific data usage scenarios. Data governance actively manages data quality, data security, and information life-cycle management policies and practices. Data governance is lead and managed by business area stakeholders and subject matter experts. Business executives regularly meet for data governance steering committee sessions and make decisions on new policies, resources, and procedures. Business areas are held accountable for complying with data policies for which they are responsible. Communication of data policies and processes is enterprisewide; end users are actively self-serving for standards, rules, and definitions. End users are knowledge about data policies and definitions, adhere to them, and use self-service data governance reference independently. Importance Execution Score Note: Importance minus execution = score Average Process Positions Data Management For Efficiency, Agility, And Effectiveness Data management will become increasingly distributed as it assimilates into SOI insight teams. While centralized data management processes for development will still exist, collaboration with analysts and data consumers will raise complex dependencies across business stakeholder requirements, stretching EA and development resources (see Figure ). Communication and collaboration pathways will be key, not only for the business but also internally for EA and development teams. Additionally, adoption of agile data development will be crucial to scale to the demand. 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

6 July, 0 FIGURE Build Data Management Processes For Business Responsiveness How would you rate the importance of the following statements? Not very Somewhat Important Very How well do you execute on the following statements? Not very well Well Very well Extremely well Process Data management is able to keep pace with changing business conditions and needs. Data management is effective at adjusting strategies, plans, and projects to stay aligned to changing business needs and conditions. Data management is effective at working with business and enterprise architects to align with business capability development. My firm evaluates customer data collection practices across various interaction points, based on the value the data will deliver for the business and the consumer. Importance Execution Score My firm has the ability to integrate a variety of data types (quantitative, qualitative, structured, unstructured, third-party, or open data). Data management is effective at using agile development processes and connecting architecture and data services (e.g., scrum or kanban). Goals, objectives, and expectations for data management are clearly communicated across all levels of the data management organization. We have the right balance between centralized and federated data management processes to meet business area needs. We have standardized architectural approaches and processes for data management. Business area stakeholders and end users understand and work within the established processes to collaborate and develop data capabilities. Note: Importance minus execution = score Average Organizations Are Built On Skills Fit For Data Management Tasks And Results Generalists with deep experience and expertise in what works and what doesn t are needed to formally lead data management; some firms are hiring chief data officers for this. 6 At the same time, enterprise architects need to foster data management teams that have strong specialists across core competencies for data integration and data warehousing as well as engineering, modeling, and semantics. This positions data management for modern architecture for big data, open source, and cloud while delivering on advanced skills to address complex analytic and artificial intelligence technologies coming to market (see Figure ). 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

7 July, 0 FIGURE Source And Train The Best And Brightest To Lead SOI Enablement How would you rate the importance of the following statements? Not very Somewhat Important Very How well do you execute on the following statements? Not very well Well Very well Extremely well Organization Our business area stakeholders know whom they should work with on the data management team for strategic, project, and ad hoc data needs. There are clear product owners for data capabilities. Importance Execution Score Our data organization is lead by a senior level manager responsible and accountable for data strategy and governance. We have the right balance between centralized and federated data management teams to maintain the proper levels of support and subject matter expertise. Our team has the skills and experience to develop and support a modern data management platform. Our team supports the entire data supply chain from data. We ensure our data management teams are up to date on new and emerging technologies and programming skills. We provide training resources for external classes and workshops for our teams to improve their skills. Our teams are up to speed with industry and/or data management frameworks/approaches and certified in data management (CDMP, DMBOK, etc.) Data governance effectiveness is measured by the impact it has on business objectives and outcomes. Note: Importance minus execution = score Average 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

8 July, 0 Technology Extends Across The Entire Data Supply Chain The days of enterprise architects building data-silo fields and application developers building APIs independently to deliver data are over. There is still a significant amount of data refinement and translation needed to go from a system of record to a system of engagement or analytic workbench (see Figure ). FIGURE Let SOI Guide Selection And Adoption Of The Right Modern Data Management Capabilities How would you rate the importance of the following statements? Not very Somewhat Important Very How well do you execute on the following statements? Not very well Well Very well Extremely well Technology The data management platform and solutions meet service levels for scale, performance, agility, availability and governance demanded by the business for: Big data Business intelligence and analytics mobile Business process applications Customer data management Data quality (master data management, metadata management, etc.) Data security Information life cycle management Internet of Things Mobile Product data management Importance Execution Score Note: Importance minus execution = score Average score 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

9 July, 0 Data Delivery And Use Empowers Self-Service And Satisfies Insight Demands Analysts and business data consumers with subject matter expertise demand self-service and will go around technology management if it gets in the way. Rather than fight this behavior and push to enforce the standard, surrender. Work with the best practices and processes of the business rather than trying to change them, which is a losing battle. Put data capabilities in place that make it easier to access and use the data. Introduce tools that assist with the sourcing, blending, cleansing, and sharing of data and insights. Be part of the insights team rather than an outsider looking in (see Figure 6). 7 FIGURE 6 Maintain The Full Data Supply Chain Through Technology And Collaboration How would you rate the importance of the following statements? Not very Somewhat Important Very How well do you execute on the following statements? Not very well Well Data delivery and use Importance Execution Score Analysts and business users are provided with self-service tools to access, prepare, and blend data for analysis. Application developers are provided with a wide variety of APIs and refinement capabilities to build robust data services into applications. An extensive set of customer data sources are provided for our analysis including transactional, CRM, call records, customer interaction data, channel data, social media data, mobile data, third-party aggregate data, digital data, voice and text, etc. We clearly communicate to customers how their data is collected and use through transparent privacy policies. Very well Extremely well We provide streamlined access to consumer data that is appropriate across multiple groups and levels within the organization. My company uses a variety of role, attribute, and usage access models to ensure the security and privacy of customer data. Additionally, the company uses a variety of legal review and compliance audit processes to validate that these policies are observed and meet regulatory, industry, and company standards. Note: Importance minus execution = score Average 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

10 July, 0 Measurement Reinforces Best Practices And Promotes A Test-And-Learn Culture Donald Farmer, VP of innovation and design at Qlik, recommends failing fast and failing often. 8 He makes the point that if you aren t failing, you aren t innovating and businesses need to innovate. SOI thrives on a test-and-learn culture because it makes insights better, which fuels actions with better outcomes. Measurement is the critical success factor. Measure business alignment, data governance, organization, process, technology, and delivery in the context of the influence they have on business outcomes. Then put the results back in to optimize your data management practice (see Figure 7). FIGURE 7 Develop Measures That Train The Data, The Process, And The Business And That Feed Outcomes How would you rate the importance of the following statements? Not very Somewhat Important How well do you execute on the following statements? Very Not very well Well Measurement Dashboards are publicly available that communicate the expected business service levels for data management. Dashboards and reports are publicly available to communicate development and enhancement of data capabilities. Data governance dashboards are publicly available and communicate compliance with data policies. Data governance dashboards are publicly available and communicate contribution to business outcomes. Dashboards are publicly available to communicate investments and budgets of key business data capabilities and data management services. Dashboards are used regularly to manage and optimize data development, management, architecture, and data stewardship utilization. We regularly use enterprise data profiling tools to define and monitor data conditions (data quality, semantics, usage, modeling, etc.) Our data management measurement is aligned without business intelligence measurement (BI on BI). All data management initiatives require a clear business case based on tangible benefits and measurable ROI. Data governance effectiveness is measured by the impact it has on business objectives and outcomes. Very well Extremely well Importance Execution Score Note: Importance minus execution = score Average 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

11 July, 0 What It Means SOI Prepares Firms For What s Next Artificial Intelligence We re entering an age where algorithms aren t just for data scientists but are at the core of all of our business systems. IBM and Microsoft are leading this change by designing commercial platforms and creating intelligent applications that replace our existing data management platforms and business workspaces, not to mention customer experience capabilities. Our relationship with data and data management practices will change from that of a farmer, whose hands are in the data, to that of a parent who oversees the child or a professor who trains the student. Putting an SOI lens on data management that centers on business outcomes and a test-and-learn paradigm prepares EA pros and organizations to be confident and effective at managing data in a highly ambiguous and contextually driven environment. SOI is helping enterprise architects transition from educating the organization with insight to educating the system to deliver relevant insight. This means that EA professionals must assess their data management practices with an eye on today s data delivery as well as what is to come. SOI maturity, and thus success at artificial intelligence, will transform data management through strategic surrendering of: People that manage and govern data. Data management and governance teams today operate in a centralized manner, with dedicated resources to set policies, create rules and standards, manage processes, and implement technology. SOI allows organizations to work with data at scale, making it necessary to bring the rest of the organization into the management and governance effort as data owners and experts. Processes to curate data. Traditional data management efforts seek to govern and control data. This traditional culture pushes processes about what users can t do and what they re required to do to the data. In an artificial intelligence system, data is automatically curated. Thus, data management and governance processes shift to training and educating based on the suggestions and outcomes of the data rather than trying to anticipate all data meanings and scenarios upfront. Technology to manage data. Classification, standardization, and data models are central to data systems today but will be even more so tomorrow. Artificial intelligence systems rely on taxonomies and ontologies to decipher meaning as well as provide insight. Data management technology will transition from integration architecture to insight architecture. This means that data management technology will become more concerned with continual comparison of environmental data to existing knowledge graphs to conform data or evolve the knowledge graph and extend the meaning and value of data for insight. 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

12 July, 0 Engage With An Analyst Gain greater confidence in your decisions by working with Forrester thought leaders to apply our research to your specific business and technology initiatives. Analyst Inquiry Ask a question related to our research; a Forrester analyst will help you put it into practice and take the next step. Schedule a 0-minute phone session with the analyst or opt for a response via . Learn more about inquiry, including tips for getting the most out of your discussion. Analyst Advisory Put research into practice with in-depth analysis of your specific business and technology challenges. Engagements include custom advisory calls, strategy days, workshops, speeches, and webinars. Learn about interactive advisory sessions and how we can support your initiatives. Endnotes Seventy percent of global data and analytics decision-makers whose firms are using or planning to use big data technologies said their IT groups are currently using or planning to use big data. Source: Forrester s Global Business Technographics Data And Analytics Survey, 0. To learn more about how businesses are drowning in data but starving for insights and how you can build a new insights-toexecution operating model, see the Digital Insights Are The New Currency Of Business Forrester report. Fifty-three percent of global business decision-makers whose firms are using big data technologies expect to see a significant increase in effectiveness of resources by using them more efficiently in the next months as a result of using those technologies. Source: Forrester s Global Business Technographics Data And Analytics Survey, 0. An expanded set of data governance principles and data polices that on the surface may appear inconsistent and incomplete needs to be acknowledged and supported across a broad federation of stakeholders. To learn more, see the Data Governance Archetypes Shape The Focus And Fate Of Your Business Forrester report. To address any structural shortcomings, forward-thinking architecture leaders are implementing formal pathways for communicating and collaborating across organizational structures and/or roles. These leaders are formalizing virtual teams of physically distributed information architects, building centers of excellence and networks of CoEs for analytics and BI specialists, and generally creating centrally coordinated groups of similarly tasked distributed resources to share knowledge and resolve issues related to organizational separation. To learn more, see the Information-Related Roles And Processes Are Changing What Are You Doing About It? Forrester report. 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

13 July, 0 6 The explosion of data generation and use within organizations has given rise to a new function, and in some cases, a new role chief data officer. A forthcoming Forrester report, CDO: Here Today, Gone Tomorrow? will discuss the chief data officer role in greater detail. 7 To learn more about how enterprise architects must understand new data preparation tools and the roles they serve to help accelerate analytics, see the Brief: Data Preparation Tools Accelerate Analytics Forrester report. 8 Source: Donald Farmer, The Many Cultures Of Innovation, Wired ( 0 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or

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