How to Establish a -Driven Culture in the Digital Workplace inShare 09 June 2015 G00275916 Analyst(s): Alan D. Duncan | Frank Buytendijk Summary Growing digital literacy and access to analytic tools is fostering a data-driven culture. This note identifies the baseline characteristics of a data-driven culture and advises business executives and analytics leaders on how to embed data and analytics within the digital workplace. Overview Key Challenges  A variety of factors, including employee skills, complex tools and failure to envision benefits, have inhibited attempts to create a data-driven culture.

 Stakeholder expectations of the outcomes of evidence-based decision making can be unrealistically inflated, leading to disappointment when the anticipated outcomes are not realized.

professionals are typically comfortable with the technical aspects of information delivery, but can struggle to influence business adoption and drive cultural change.

 There can be a mismatch of organizational dynamics, capability and propensity; a desire to be data-driven does not equate to acting upon data. Recommendations Business executives and analytics leaders must:

 Recognize the characteristics of a data-driven culture and identify opportunities to introduce relevant behaviors, in order that evidence-based decision making becomes a core part of the digital workplace.

 Encourage adoption of a data-driven culture through incorporating mindful information practices and making data-oriented behaviors pervasive in people's roles.

 Acknowledge the potential limitations of a data-driven culture and establish suitable mitigation strategies to overcome any associated business impacts. Table of Contents

 Introduction  Analysis o Recognize Data-Driven Culture Characteristics and Identify Opportunities to Introduce Behaviors o Encourage a Data-Driven Culture Through Incorporating Mindful Information Management Practices o Acknowledge Potential Limitations of a Data-Driven Culture and Establish Suitable Mitigation Strategies  Gartner Recommended Reading

Strategic Planning Assumptions By 2017, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis. 1 By 2018, 90% of information governance programs based on "citizen stewards" will fail to meet their declared objectives. 2 By 2017, 25% of large enterprises will have a digital ethics code of conduct, to avoid the abuse of information and ensure consumer value. 2 Introduction Gartner's research communities in information strategy, business analytics and the digital workplace have noticed an uptake of inquiries based on phrases such as "data-driven," "evidence-based" and "data culture." 3 We see this as a sign of organizations taking a more mature approach to information management. The tools can be put in place, the processes can be organizationally embedded, and the governance practices can start to appear. Linking data and analytics to behavioral and cultural change is emerging as the next challenge to addressing the full potential of the digital workplace; Gartner identifies analytic culture as one of the four "pillars" to building comprehensive business analytics programs (see "Why Business Analytics Projects Succeed: Voices From the Field" and "Solution Path for Creating a Business Analytics Strategy" ). Clearly, having a data-driven workplace requires pervasive use of data within the execution of key business processes. Conversely, business processes need to be established in a manner that allows relevant data to be collected. Both strategic and operational decision making are informed by data and analysis. These in turn drive velocity of business response and overall time-to-value, as well as influence transactional execution. Every data conversation needs to be framed in the context of being a business conversation, and every business conversation framed as a data conversation. This also suggests that the concept of being a data-driven organization is more about the journey than the destination, and implies transformational change initiated and monitored with information and analytics. In this research note, we answer the following questions:

 What are the characteristics of data-driven culture?

 How do you build a data-driven culture?

 What are the limitations of a data-driven culture? Analysis Recognize Data-Driven Culture Characteristics and Identify Opportunities to Introduce Behaviors What are the characteristics of a data-driven culture? The most straightforward definition of culture is "the way we do things around here." 4 It is the sum of attitudes, customs and beliefs that distinguishes one group of people from another. Culture is transmitted from one generation to the next through language, material objects, ritual, institutions and art. 5 Applied to data, a data-driven culture describes how data is used to organize activities, make decisions and resolve conflict. It is encapsulated within data policies, process activities and technology tools (although the success of these may either reinforce or inhibit the overall data- driven capability). Through our client interactions, we have observed a number of common characteristics in organizations that call themselves data-driven:

 Pervasive use of data in business processes. By definition, a data-driven workplace requires pervasive use of data within the execution of key business processes, as part of operational decision making. This drives operational process improvement, a 360-degree view of the customer, and adoption of techniques such as formal decision management, prescriptive analytics and algorithmic business processes. Business questions are well- documented and communicated. Processes and systems are modified so that more people have ready access to business information, instead of operating on a need-to-know basis. Expectations for data access are reinforced within organizational data policies and governance methods. Making such changes is often easier than you might think. People typically complain about their lack of data access and then simply move on, whereas some perseverance and collaboration can make all the difference. A more transparent, open information culture is at the heart of a digital workplace.

 Data and analytics at the heart of both strategic and tactical decision making. Information can inform decision making at both strategic and tactical levels. On the tactical level, decisions typically follow data, so the data really drives the decision. However, on the strategic level, more elements than data play a role. In those cases, data may follow the decision. Analysis is performed to confirm (or disprove) the decisions that are taken. Innovation is encouraged and promoted. Different leadership styles display different decision-making processes. All of them can be data-driven.

o Some leaders collect all the data, then make a decision: "The market analysis shows that, over the last two years, there has been an exponential rise in the uptake of devices for personal fitness and quantified self, and a slow but steady decrease in signups for on-premises fitness classes. Therefore, we will establish a service to connect to individuals' fitness data and tailor our personal training and group classes." o Others moderate the decision-making process and make sure all arguments and steps are sufficiently data-based: "The market analysis shows that pay-for-use insurance is rising. We need to model the projected impact on our current single-pay policies and assess whether we are in a position to establish a pay-for-use capability."

o Again, others have a more consultative style of decision making and would ask others what they would read in the data: "Here are the latest figures for usage trends for both online and on-site inquiries to the welfare office. What do they tell us and what should our response be?"

 Treating information as an asset. Although our interactions abundantly show that our clients believe information to be a key business asset, in many organizations the responsibility is fragmented, implicit and often absent. While 80% of CEOs claim to have operationalized the notion of data as an asset, only 10% say that their company actually treats it that way. 6 Gartner suggests that an important reason for this is that it is not common to measure the asset value of information. This is the area of an emerging discipline called "infonomics" (see "Introducing Infonomics: Valuing Information as a Corporate Asset" ). If you can measure the value of information, it becomes easier for people to assume responsibility, and what gets measured, gets done. Our research also shows that information-centric organizations tend to have higher market-to-book ratios than their non-data-driven counterparts (see "Predicts 2015: Information Governance and MDM Will Be Foundational to Improving Digital Culture" ). Other aspects of treating information as an asset include:

o Information aligned with business strategy and identification of business value opportunities. The organization actively curates the inventory of information assets and data holdings — by whom, for whom and for what purpose(s).

o Moving from informational content to stimulus for change. The central expectation is that change drives additional business value, and that analysis is the change stimulus. This is also linked to the expectation that when something will be done differently, outcomes are measurable.

o Established accountability for data within end-to-end business process management, including assigned data ownership and stewardship roles (see "Toolkit: Information Governance Role Descriptions" ).

o Data quality is taken seriously, lest the data-driven culture lead to unintended consequences because of some bad data. Monitoring data quality is linked to an active process of issues management and remediation (see "Toolkit: Assessing Key Data Quality Dimensions" ). o Data gaps are addressed. There are times when the most useful data is the data that is missing — for example, there may be a huge new client base around the corner, but it may not be represented in the current data so may not be found by simply looking at the available data. As another example, if the next great market is China and an organization has only European clients, it won't find new market insights in its extant client data.

 Fostering human relationships using data and analytics as a catalyst for improved trust. Business leaders and analysts must address the human relationship — psychological and emotional engagement — with stakeholders. This is a pioneering role in developing and leading the data and analytics culture, with data used to substantiate business action. For example:

o Taking account of factors such as confirmation bias, cognitive filtering and cognitive dissonance.

o Recognizing information as a proxy for the relationships between actors (supplier and customer; employer and employee; machine to machine). Trust requires investment in the relationship.

o Applying the concept of rhetoric to build argumentation, influence and engagement based upon data-driven analytics findings — observation, context, interpretation, agreement and derivative logic. 7

o Mindful consideration of data protection and privacy issues. (For example, The Consumer Goods Forum just adopted "consumer engagement principles," which might be considered a component of the culture.)

o Achieving a data-driven culture requires recognition that this can be a transformational change. The impact on people and behavior is potentially significant, and not always immediately obvious; the opportunity for unintended consequences is ever-present (see also "Toolkit: Kick-Start the Conversation on Digital Ethics" ).

o Developing data-led narratives to engage stakeholders in a manner that bypasses instinctive responses and engages a more considered and rational thought process — differentiating between "fast" and "slow" brain patterns. 8 This applies at both collective and individual levels (see "Leading From the Heart: Why Emotional Intelligence Is Crucial for CIOs" and "Designing Processes for the Brain-Aware Enterprise" ).

 Ability to confront the brutal facts. If the data tells you bad news, it should act as a stimulus to drive innovation, creativity and a proactive response. However, it may instead encourage people to game the system or hide the data because leaders cannot confront the implications. Is there a risk of confirmation bias, or even outright rejection of data that contradicts a prior set of beliefs? The human, financial and reputational impacts can be devastating. Example cases where organizations have failed to confront the brutal facts include the Veterans Affair scandal, 9 Atlanta teacher test cheating, 10 and the GM Chevy Cobalt recall. 11 Encourage a Data-Driven Culture Through Incorporating Mindful Information Management Practices How do you build a data-driven culture? Aspects of behavioral shift and psychological engagement that can help influence and build a data-driven culture are listed below. Many have an HR component. A strong move to a data- driven culture should include the HR organization as a core stakeholder, with the cooperation of HR and IT departments being a core element of the digital workplace. Ideal facets of the data- driven digital workplace are described below.

 Exhibit example behavior. We recently heard a story from an organization where decision making was cynically defined as "the process of everybody preparing and bringing argumentation to the table, and the highest-ranking executive decides based on his or her pre- existing opinion." That would not be the best way of creating a data-driven culture. First and foremost, a data-driven culture lives and dies by example behavior. In meetings, in presentations, in all daily interactions, executives need to show they are looking for the right data to base decisions on. To lead by example, executive management should stress in their communications how data-driven their decision making has been, and how they should only accept the same from their direct reports. 12

 Hire data-driven people. A data-driven culture will come from hiring or empowering people who have a tendency to being analytical as well as having performance measures and business processes that put value on evidence-based decision making. Having a number of "quants" visibly participate in business leadership and operational roles will encourage a prevalence of research investigations, insightful thinking and innovation, led by the data ("follow the data," not "follow the plan"). The HR group can be instrumental in adjusting hiring practices to emphasize analytic literacy.

 Adopt practical measures to increase transparency. Many business processes and systems are built based on elaborate information access schemes and information governance policies. In order to drive change, it can help to drastically open up these systems and create more internal transparency. For example, we know of one company in professional services with a competitive culture that introduced a "stack ranking" for all of its professionals, based on the quantity and quality of their work. This was resisted heavily by the professionals, who questioned the validity of the stack-ranking formula, but management persisted with publishing the ranking. Professionals benefited from the transparency of the information, and started using it. One of the core tenets of the digital workplace is moving to a more information-centric approach to information access, where "default to deny" and "least privilege access" are rarely used. The approach of rights of data access implies challenging the standard data security protocol of data lock-down and identifying explicit reasons to prevent access. Therefore, data security and governance considerations need to be carefully planned. Data zoning needs to take into account and act upon protection and privacy, end-user understanding, commercial sensitivity, legislative obligations, organizational risk and open data opportunities.

 Ensure data-driven performance reviews and goal setting. Make education of both end users and the analytics team ongoing, and incorporate explicit performance measures for the treatment of data within both process definitions and the job descriptions of individual roles. This should happen both top-down, with the organization's leadership team incorporating data-oriented measures into business KPIs, and bottom-up, with employees in the digital workplace having a place at the table with respect to defining performance metrics. Done well, the shift to a data-driven culture can help boost employee engagement because:

o Learning and skills acquisition promotes engagement, in this case helping employees develop business intelligence (BI) and analytics skills.

o Contribution to business outcomes and recognition of contribution are easily identified when evidence is routinely included in business processes.

o BI and analytic expertise encourages local decision making and employee autonomy in carrying out dynamic work. Acknowledge Potential Limitations of a Data-Driven Culture and Establish Suitable Mitigation Strategies What are the limitations of a data-driven culture? Data and analytics are a means, not an end. The idea of a data-driven culture must then link to a value-driven approach. Most of all — and despite all data's good value — companies should not become data-obsessed. Some specific examples of the limitations that can occur include:

 Unavailable data — In some situations, data is not readily available or may be subject to interpolation or extrapolation (for example, when entering into new markets or looking at future scenarios, or when the data is either inaccessible or too expensive to source). In such cases, other methodologies such as scenario planning and "what if" modeling must be applied. Note too that absence of data does not mean the assertion is incorrect — absence of supporting data should not always be interpreted as a negative proof point.

 When something is simply the right thing to do — Not all business decisions are inherently improved by the use of data. No data is needed to show that supporting a certain cause is simply a worthy initiative for the company to participate in (neighborhood initiative, staff initiative, supporting something that links to the products of the company). Considering ethical matters also does not need a business case or KPIs. To quote Albert Einstein: "Not everything that can be counted counts, and not everything that counts can be counted."

 Becoming too metrics-driven — Measurement and rewards drive behavior, whether measures are explicit (KPIs, financial incentives, promotions and awards) or intrinsic (appreciation, well-being, sense of belonging and fun). Some organizations become overly data-driven, however, focusing on nothing but current business performance metrics, and as a result lose their ability to innovate effectively — the classic "analysis paralysis." There is also a risk of unintended consequences, where an expectations mismatch occurs or explicitly established measures do not correspond with desired employee behaviors. Overall, be flexible and mindful. Moreover, information should be a key component of the innovation cycle. 13 This also requires organizations to actively manage the expectations of evidence-based decision making. Not all decisions can be supported with data, and there will be occasions when evidence is not taken into account. This should not prevent or undermine a sustained commitment to data advocacy (what data and why; where are the business opportunities and measurable outcomes). Evidence-based decision making relies upon an ongoing intent to influence the data and analytic culture for business value. Gartner Recommended Reading Some documents may not be available as part of your current Gartner subscription. "Without Change Management, Your BI Program Could Easily Fail" " Governance From Truth to Trust" "Avoid These Five Organizational Change Pitfalls" "A Bimodal Enterprise Needs Three Subcultures" "Creating a Digital Workplace Execution Strategy" "Digital Workplace Insights From Gartner's 2015 CIO Survey" "(What to Do When) Every Employee Is a Digital Employee" "Create a Business Manifesto for Digital Workplace Success" Evidence 1 See "Predicts 2015: Power Shift in Business Intelligence and Analytics Will Fuel Disruption." 2 See "Predicts 2015: Information Governance and MDM Will Be Foundational to Improving Digital Culture." 3 Gartner Engine for Analytics and Research, as of 16 May 2015: 1,251 "data-driven" interactions, 510 "evidence-based" interactions and 334 "data culture" interactions. 4 C. Hampden-Turner and F. Trompenaars, "Riding the Waves of Culture: Understanding Diversity in Global Business," McGraw-Hill, 1997. 5 Definition of "culture" taken from "The American Heritage New Dictionary of Cultural Literacy, Third Edition," retrieved 13 May 2015 from Dictionary.com . 6 2014 Gartner CEO Survey. 7 S. Toulmin, "The Uses of Argument," Cambridge University Press, 2003 edition. 8 D. Kahneman, "Thinking, Fast and Slow," Farrar, Straus and Giroux, 2012 edition. 9 See "V. A. Ejects Four Executives Over Scandal," The New York Times, 6 October 2014. 10 See "Nine Atlanta Teachers Headed to Jail After Test Cheating Scandal," PBS, 14 April 2015. 11 See "Lawsuit: GM Knew of Chevy Ignition Problem," USA Today, 19 February 2014. 12 T. Davenport, "Competing on Analytics: The New Science of Winning," Harvard Business Review press, 2007 edition — citing the sign on the desk of the CEO of Harrah's Entertainment that quoted W. E. Deeming: "In God We Trust, All Others Bring Data." 13 C. Christensen, "The Innovator's Dilemma: The Revolutionary Book That Will Change the Way You Do Business," HarperBusiness, 2003 edition.