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How to Establish a Data-Driven Culture in the Digital Workplace Inshare 09 June 2015 G00275916 Analyst(S): Alan D

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How to Establish a Data-Driven Culture in the Digital Workplace Inshare 09 June 2015 G00275916 Analyst(S): Alan D How to Establish a Data-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. Business intelligence 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 management 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
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