The Insight-Powered Enterprise Why its better and how to become one

A Thought Leadership Series

2 | The Insight-Powered Enterprise

Thanks to new analytics capabilities, almost every facet of our day-to-day life is subject to digital disruption. Analytics has become ubiquitous in politics, sports, social fields, academia, and across every industry and business domain. Political campaigns are now able to predict specific sets of individuals who are the most persuadable in changing their voting outcome, by analyzing voter data and building uplift models.1 This allows campaigns to become much more precise in their outreach efforts. Instead of simply identifying who would respond positively to an outreach effort, campaign teams are able to differentiate between voters who are a sure thing (that is, planning to vote for a given candidate regardless of the outreach effort) and those voters who might be on the fence and persuaded to vote for a candidate based on targeted outreach.2

These insights allow campaigns to optimize who they target, how they contact them, and the message that is conveyed. Many campaigns at all levels are leveraging analytic techniques such as this in an effort to get the best voting outcomes for the lowest cost.

In the sports world, many professional (and even amateur) sports teams, have employed analytics to improve their rosters and performance for years. According to the Wall Street Journal, the Golden State Warriors® basketball team has recently revolutionized the game by adopting a new strategy based on analytics and in the process set the NBA® single season record for wins.3 The Warriors looked at their data and developed a new insight: NBA players made roughly the same percentage of shots from 23 feet as they did from 24 feet, but because the 3-point line ran between them, the value of the two shots was very different.4 The focus became optimizing the metric of Average Value per Shot Attempt,

which meant taking more 3-point shot attempts during the game.5 The Warriors then fundamentally changed their roster, coaching, and game plans to support the strategy of optimizing Average Value per Shot Attempt.6 This concept of applying analytics in the sports world extends beyond basketball and is used by soccer, American football, baseball, rugby and even in shaping the fan experience.7

In the same way that political campaigns changed the way they target voters, and sports team changed the way they build a game plan, a Brazilian manufacturer changed the way they measure performance in an effort to double their business in a five-year span. Data analysis indicated that the company’s product consumption in Brazil was significantly less than in comparable countries. To increase volume, the company targeted doubling the number of points of sale. To support this, they had to look at everything differently, including point-of-sale type,

sales route, and distribution route. Similar to the way politics and sports teams changed their focus, the manufacturer changed their key question from “how do we optimize sales in an outlet” to “how do we optimize performance of a sales route.” This question, and the changes it required, resulted in the ability for a daily sales route to grow from 10 stores per day to 60 stores per day, while significantly reducing the cost of the route. Sales in the modified territories have more than doubled, with a substantial increase in margin.

As shown in these examples, companies can use data and analytics to not only improve performance, but change the way the game is played and how business is done, in the process transforming themselves into what we call insight-powered enterprises. An insight-powered enterprise is able to drive its strategy with data-driven insights and inform its actions through analytics.

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We have identified five key development points to help companies transition into becoming insight-powered enterprises:

1. Measure what matters

2. Optimize decision processes

3. Harness the power of new technology

4. Develop an agile operating model

5. Develop an analytics culture

4 | The Insight-Powered Enterprise

The Path Forward: Creating an insight-powered enterprise

1. Measuring what matters to align action with strategy and enforce accountability

Insight-powered enterprises use insights in developing their business strategies to change the game. Marriott International®, for example, has received industry awards for its revenue management capability called One Yield.8 In developing One Yield, Marriott pioneered the use of analytics in the hospitality industry to optimize room pricing, similar to the way airlines use analytics to optimize seat pricing.9 An interesting component of One Yield is that Marriott measures their performance against optimized price potential, not historical prices or competitor prices.10 In this way Marriott is competing against what is possible, not what the industry is doing.11

To measure what matters, companies should focus on the following:

• Leverage predictive analytics to identify key drivers of growth based on strategic priorities, and align on key metrics accordingly;

• Employ a closed loop process to capture the impact of decisions and validate results; and

• Align individual incentives to establish accountability for improving the identified metrics.

Research has shown that high performing companies are twice as likely to achieve specific outcomes, and almost four times as likely to report receiving a significant ROI from analytics.12 This success can only be determined if companies capture the impact of decisions and measure the tangible results of their analytics efforts. A value scorecard, as seen in Figure 1 below, can help track the value realized from analytics capabilities.

Figure 1 | Illustrative Value Measurement Scorecard

1. SPEED TO CAPABILITY

3. VALUE REALIZED

2. PACE OF ADOPTION

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2. Optimizing decision processes to embed analytics and insights into processes at the point of decision

The development of analytics is of no business value if the information is not integrated into the business process where it can inform an action. Our research shows that more than twice as many High Performing companies are adapting and embedding analytics in their decision-making processes as Low Performing companies (79% to 34%).13 Insight-powered enterprises bring analytics into the open by re-designing business processes end-to-end, identifying where analytics should support the decision, and improving how the analytics are presented and consumed by the business user.

In order to optimize decision processes with the power of analytics, companies should:

• Redesign business processes to infuse data-driven insights in support of decisions;

• Sustain agile decision processes by learning to fail fast;

• Leverage the app concept to enable end-to-end workflow for specific business problems; and

• Minimize information overload by only providing the end user with the information that supports the next best action.

As shown in Figure 2 below, decision processes in many fields can be optimized by augmenting the role of the human

with automated intelligence in the form of a role-based decision cockpit.

As an example of how to optimize decision processes with analytics, a large consumer goods manufacturer implemented the concept of next best action by developing an actionable insights mobile app for their sales organization. The company integrated sell-in data with sell-through data and consumer demographics in a repeatable process delivered to the sales representative on a mobile device that provided insightful visualizations and ranked opportunities based on predictive modeling. This process allowed the sales representatives to focus on only the needed information and make better decisions about the offers to provide to their customers.

Figure 2 | Optimize decisions by augmenting knowledge workers with right-time insights

Farmers can determine how much nitrogen and irrigation to apply to their fields in order to optimize crop yield

Door-to-door salespeople can predict likelihood to purchase in order to determine which neighborhoods and even which homes on a street to target

Insurance underwriters can leverage machine learning algorithms to predict fraud likelihood at first notice of loss with an insurance claim

Doctors can rapidly determine the right treatments and medications needed in the ER to save patients’ lives

Farmer Salesperson DoctorInsurance underwriter

6 | The Insight-Powered Enterprise

3. Harnessing the power of new tools to provide insights at the pace of business that are easy to consume

Technology is more powerful than ever and more accessible as the cost of storage is dropping precipitously. Among other things, this has led to the volume of data within companies increasing at an exponential rate. Within the next four years, for example, there are predicted to be an installed base of over 20 billion connected devices14 (such as oil pipelines, smart cities, connected homes and businesses) producing real-time data streams. Tools which increase the speed of data processing by up to 100 times over other tools, provide enormous potential to quickly leverage this data to produce game changing insights.

Despite these continued technological advancements in the analytics space, the reality is that most companies will be living in a hybrid world, with both relational and big data technologies, for the foreseeable future and it is important to use the right tool for the right purpose. The pace of technology innovation is so fast that companies should focus first on data architecture rather than on application architecture as these will be

increasingly “plug and play” or delivered in a SaaS model. Maintaining the right technology focus, while keeping the inherent complexity of data and analytics behind the curtain will allow decision makers to obtain needed insights without getting lost in the mechanics of how such insights are generated.

In order to harness the power of new tools, companies should:

• Embrace and deploy the right technologies and agile approaches in order to stand up capabilities faster;

• Prototype capabilities with an interactive lab and scale successes;

• Leverage different mechanisms such as machine learning to generate new insights from data, while keeping the complexity behind the curtain for the end user; and

• Provide business users access and training on data visualization tools, while taking caution to ensure that the appropriate operating principles are well established.

As shown in Figure 3 below, an airline cockpit serves as a relevant analogy for how technological capabilities are rapidly advancing, but also how much of this complexity is hidden from the end user.

A large energy company demonstrated the business value of harnessing the power of new technology by deploying a cloud-based big data analytics environment and leveraging machine learning techniques and advanced visualizations to identify new insights. This company, with over 300,000 locations in nearly 100 countries, sought to tap into the value of vast quantities of sensor data being collected at high speed. The company established a Hadoop-based environment within 36 hours and, over a ten-week proof-of-concept period, identified $35 million in annual savings just from excessive equipment idle times. The program was extended another seven weeks at the client’s request and identified an additional $74 million in savings in areas such as crew utilization.

Figure 3 | Keep technology complexity behind the curtain from the end user

Keep complexity behind the curtain

Adapt

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4. Deploying an agile operating model to redefine how analytics is governed and deployed across the enterprise

Insight-powered enterprises employ agile operating models to act quickly upon key insights. Through our research in Launching an Insights-Driven Transformation, we found that organizations with low analytics maturity tend to have fragmented capabilities focused on point solutions, are challenged with scaling priority capabilities, and often struggle with sourcing and retaining analytics talent.15 These companies often leverage traditional waterfall delivery methodologies and struggle to keep pace as the business needs evolve. In fact, only 15% of low maturity companies believe they have robust methods and processes to support smarter business decisions compared to 67% of mature companies.16 We believe that analytics delivery must be a blend of rapid insight, test and learn, and scaled proven solutions. To accomplish this, companies need to move away from lengthy processes and complex, layered hierarchies, and haphazard talent sourcing.

To deploy an agile operating model as illustrated in Figure 4 below, companies should take the following steps:

• Establish an insights lab to take a data-driven approach to the key strategic questions facing the business;

• Leverage an industrialization team to scale proven solutions across the broader enterprise and embed within key decision processes;

• Deliver proven, recurring services in a repeatable and efficient manner while still monitoring and maintaining the service quality;

• Implement a governance model oriented on speed to capability, pace of adoption and value realization;

• Position IT as a key enabler and change agent in the analytics transformation; and

• Follow a structured approach to analytics transformation to build a sustained analytics edge;

In order to successfully sustain an agile analytics operating model, many leading companies are rightfully elevating analytics by creating a center of gravity led by a Chief Data and Analytics Officer (CDAO) and focusing on sourcing and retaining key talent. The CDAO is often responsible for establishing the goals and strategies to support the analytical needs of the company. Interdisciplinary pod teams of analytics talent must then support the CDAO in executing against these strategies. Pod teams include a mix of roles and skills, including: data science and analytics modeling, data modeling and integration, visual literacy, storytelling, and software development or coding, among others. Our research has shown that leading companies are sourcing these key roles from academia and industry, partnering to quickly fill talent gaps, crowdsourcing, and investing in retention and training programs to improve the analytics IQ of their existing workforce.

Figure 4 | An agile delivery model will allow companies to quickly identify and act upon insights

Business Stakeholders Agile Analytics Organization

Business / Functional Teams Delivery Governance

Technology & Enterprise Data Management Focus: architecture and data assets

Focus: business need• Identification of use cases• Prioritization of use cases• Insight-driven decisions

Focus: strategic problems• Data acquisition• Data integration• Analytics modeling• Visualization• Prove value

Insights Lab Industralization Team Ongoing Delivery

Focus: scaling proven solutions• Automate capabilities• Model tuning• Scale solutions• Independent certification

• Value realization• Use case defintion

• Cross functional governance• Branding & communications

Focus: repetitive reports/analytics outputs• Infrastructure operations• User support• Triage and break fixes• Application operations

Insights & Analytics Requests

Analytics and Recommendations

Sales

Marketing

Innovation

Strategy

Supply Chain

Corporate Functions

The Insights Marketplace improves access to existing data assets by acting as the single source of truth globally, integrating all data under one brand.

Key Features:• Access existing tools through

a single access point

• Access unified reports across geographies

• Search and browse available data

The Insights Marketplace takes an experience design based approach to personalize interactions, improve functionality and increase collaboration amongst users.

Key Features:• Design a custom user dashboard

• Build and shape reports directly

• Explore, visualize, drill down into data, and export to multiple formats

The Insights Marketplace transforms workflows to standardize common tasks, improve e ciency and ensure users take action on meaningful business decisions.

Key Features:• Guided discovery features to create

simple and holistic views of data

• Intuitive data analysis and visualization

• Collaboration and workflow

The Insights Marketplace

Enhance User Experience Accelerate Global InsightsIntegrate Assets Globally

8 | The Insight-Powered Enterprise

5. Developing an analytics culture to fundamentally change how analytics is used to drive decisions from facts and insights.

Metrics, analytics, insights, agile processes, data and technology will only help a business if the business adopts them. To drive adoption, insight-powered enterprises take a coordinated, enterprise-wide approach to managing change and developing a culture of analytics. They build a culture in which data-driven decisions are part of the fabric that holds the organization together, where people look for analytics to make a decision, rather than making a decision and then looking for data to support it. Recent research supports this point, as High Performing organizations are much better at embedding analytics in fact-based decision making to develop a competitive advantage (95% of High Performers vs. 44% of Low Performers).17

Effecting this type of change is not easy, as many companies and employees pride themselves on gut decisions. Sports executives adopting analytics initially faced opposition from their internal talent scouts and coaches, and also from fans

and the media. Challenging the status quo is never easy, but nothing breaks down barriers like success.

In order to develop an analytics culture, companies should:

• Set the tone from the top by forming strong executive sponsorships to communicate and endorse analytics goals;

• Take a design-led approach to the user experience through intuitive analytics technologies like an Insights Marketplace;

• Continually adjust the operating model, processes and technologies based on learnings to maximize value;

• Drive adoption on the analytics tools and processes and reinforce new learnings through individual performance goals; and

• Adapt the organization’s decision-making processes through targeted branding and communication efforts.

In order to build an analytics culture, one large multi-national organization recently decided to redefine the face of analytics for the business users and decision makers. The company worked with a design and innovation consultancy to take an experience design-led approach to build an Insights Marketplace, the features of which are shown in Figure 5 below. The Insights Marketplace included all data, reports, and analytic tools that end users required to do their job and it was organized based on how the users worked on a day-to-day basis. It incorporated workflow and collaboration capabilities so that users could easily interact, rate, and evaluate content. By placing the end user at the center of the development process, the Insights Marketplace changed the way people worked and made decisions, thereby increasing adoption across the enterprise.

Figure 5 | The Insights Marketplace

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Faced with decreasing sales volumes due to a shrinking market and increasing competition, a large brewing company recognized the need to invest in its analytics capabilities to better understand consumers, partners and market opportunities, and steer the business towards success.

The brewer implemented an agile analytics operating model with a strategic analytics team at the center, led by a Vice President of Insights and Analytics. The new team prioritized high value use cases and sought to redesign decision processes to equip the business to make more informed decisions.

A flexible cloud-based platform was leveraged along with an inter-disciplinary pod team focused on rolling out the priority use cases. One of these use cases focused on modeling the incremental sales impact of a new product launch or marketing initiative, and identifying the most suitable geographies to be targeted based on sales drivers. In addition to the new platform and tools, agile operating model and process redesign, the brewer placed a large

emphasis on measuring what matters right from the start of their journey.

A value case was established with a projected benefit of $16 million in year one, a benefit which is projected to grow to $43 million at steady state (compared to $5 million platform and delivery costs over a two-year period). Templates and scorecards were developed to measure this value over time. The brewer understood that the value would not be achieved without changing the status quo, and, as a result added incentives for adopting the new analytics capabilities to people’s annual goals. By taking a focused and balanced approach, the brewer is experiencing early success in its journey to become an insight-powered enterprise.

Case Study

A large brewing company’s journey to becoming an insight-powered enterprise

10 | The Insight-Powered Enterprise

In the world of advanced analytics, companies no longer have the luxury of time. With rapidly changing market conditions, they need to move quickly to not only defend, but differentiate their organizations by deriving insights from their data to inform their business strategy.

They should align on a common set of metrics and continuously measure what matters. They should change business processes so that analytics are integral to the output, not part of an ad-hoc review if there is time. Companies should organize to deliver analytics as a core competency, not a specialized art practiced by a select few. The operating model should facilitate, not hinder analytics. Companies should embrace new technologies and not allow the sunk costs of existing solutions to prevent them from keeping pace with their industry. And they should develop a culture of analytics where business is driven from insights, not tradition or hunches.

The journey to analytics maturity is long and complex, but it provides rewards along the way, and as has been shown within campaign politics and sports, game changing analytics has the ability to differentiate and disrupt. With the power of today’s technology and a receptive culture, results can be seen in weeks, rather than in months or years. Analytics has moved from being an integral part of an organization to being ubiquitous. Each analytics win makes change easier and builds momentum toward the ultimate objective of creating an insight-powered enterprise.

Rethinking the JourneyWhere to go from here?

References1 Daniel Porter, Keynote Address at

the Predictive Analytics World (PAW) Conference (June 22, 2016).

2 Ibid.

3 Ben Cohen, “The Golden State Warriors Have Revolutionized Basketball,” http://www.wsj.com/articles/the-golden-state-warriors-have-revolutionized-basketball-1459956975, (April 6, 2016).

4 Ibid.

5 Ibid.

6 Ibid.

7 RBS 6 Nations® Championship, Accenture: https://www.accenture.com/gb-en/company-rbs-six-nations

8 Tom Davenport and Jeanne Harris, Competing on Analytics: The New Science of Winning (Boston: Harvard Business School Press, 2007).

9 Ibid.

10 Ibid.

11 Ibid.

12 David Simchi-Levi, Jyo Gadewadikar, Brian McCarthy, and Lynn LaFiandra, Winning with Analytics (New York: Accenture, 2015). https://www.accenture.com/us-en/insight-high-performance-analytics

13 Ibid.

14 Gartner Forecast: Internet of Things – Endpoints and Associated Services, Worldwide, 2015. https://www.gartner.com/doc/3160223/forecast-alert-internet-things-

15 Brian McCarthy, Robert Berkey, and Chad Vaske, Launching an Insights Driven Transformation (New York: Accenture, 2015). https://www.accenture.com/us-en/insight-launching-insights-driven-transformation

16 David Simchi-Levi, Jyo Gadewadikar, Brian McCarthy, and Lynn LaFiandra, Winning with Analytics (New York: Accenture, 2015). https://www.accenture.com/us-en/insight-high-performance-analytics

17 Ibid.

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Insight-Powered Enterprise Thought Leadership Series

The insight-powered enterprise is defined by a culture where data-driven insights are leveraged to support decisions at all levels within the enterprise. To build this type of culture and become an insight-powered enterprise, companies have to change the way they make and measure the impact of decisions, the way they are organized, the way they use technology, and the way they relate to people – not just their customers, but also their own employees. This point of view is part of an ongoing thought leadership series from Accenture’s Analytics Advisory practice that explores various aspects of how organizations can become insight-powered enterprises.

About AccentureAccenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With more than 375,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com.

About Accenture Digital Accenture Digital, comprised of Accenture Analytics, Accenture Interactive and Accenture Mobility, offers a comprehensive portfolio of business and technology services across digital marketing, mobility and analytics. From developing digital strategies to implementing digital technologies and running digital processes on their behalf, Accenture Digital helps clients leverage connected and mobile devices; extract insights from data using analytics; and enrich end customer experiences and interactions, delivering tangible results from the virtual world and driving growth. Learn more about Accenture Digital at www.accenture.com/digital.

About Accenture AnalyticsAccenture Analytics, part of Accenture Digital, helps clients to use analytics and artificial intelligence to drive actionable insights, at scale. Accenture Analytics applies sophisticated algorithms, data engineering and visualization to extract business insights and help clients turn those insights into actions that drive tangible outcomes—to improve their performance and disrupt their markets. With deep industry and technical experience, Accenture Analytics provides services and solutions that include, but are not limited to: analytics-as-a-service through the Accenture Insights Platform, continuous intelligent security, machine learning, and IoT Analytics. For more information, follow us @ISpeakAnalytics and visit www.accenture.com/analytics.

Contact usRobert Berkey Managing Director – Accenture Analytics Analytics Transformation +1 917 817 5923robert.e.berkey@accenture.com

Brandon Joffs Senior Manager – Accenture Analytics Analytics Advisory Practice +1 720 359 5466brandon.l.joffs@accenture.com

Chad VaskeManager – Accenture Analytics Analytics Advisory Practice +1 507 360 4464chad.a.vaske@accenture.com

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