Co m pl im en ts of Democratizing Analytics Compete and Win by Empowering Your People with Data Melissa Burroughs & David Sweenor REPORT Democratizing Analytics Compete and Win by Empowering Your People with Data Melissa Burroughs and David Sweenor Beijing Boston Farnham Sebastopol Tokyo Democratizing Analytics by Melissa Burroughs and David Sweenor Copyright © 2023 O’Reilly Media, Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. 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Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. This work is part of a collaboration between O’Reilly and Alteryx. See our statement of editorial independence. 978-1-098-14543-9 [LSI] Table of Contents Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 1. Data and Analytics Challenges in Modern Organizations. . . . . . . . . . . 1 The Issue: Insights Not Available Where Needed A New Approach: Democratizing Analytics 9 10 2. How Democratizing Analytics Improves Business Outcomes. . . . . . 13 Case Study: Chick-fil-A Analytics Maturity: The Hidden Key Performance Indicator Driven by Analytics Democratization Concrete Benefits of Democratizing Analytics 13 14 15 3. Democratizing Analytics and Data Governance. . . . . . . . . . . . . . . . . . 19 Case Study: UBS Group AG Learnings: UBS’s Category-Driven Approach Case Study: BT Group 20 22 23 4. Best Practices for Analytics Democratization. . . . . . . . . . . . . . . . . . . 27 Case Study: Phillips 66 Engage Experts for Guidance Gain Executive Support Start Small Demonstrate Benefit Train the Trainers Make Analysts the Foundation Establish an Analytics Council Keep the Process Moving 28 30 30 31 31 31 32 32 33 iii A. Eight Steps to Getting Started with Analytics Democratization. . . . 35 Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 iv | Table of Contents Introduction The world is constantly changing around your organization. The increasingly rapid pace and massive scale of the online world are already forcing companies to evolve, but many other external factors also impact organizations across the globe. Economic uncertainty, geopolitical instabilities, persistent inflation, and strained supply chains are now the new normal. How does your organization stay ahead of all this ever-evolving change? Perhaps a better question is: what is it that allows successful companies to be flexible, adaptable, and resilient when the business environment around them shifts dramatically? How are they able to identify challenges in their busi‐ ness and adapt quickly to minimize impacts? The simple answer is: information. Successful organizations fuel decisions with informa‐ tion derived from analyzing data. It is not difficult to understand why. An organization cannot suc‐ ceed without understanding what brings it business or removing obstacles that hinder progress. Successful organizations do this based on the data that their operations, customers, prospects, sup‐ pliers, and partners generate each day. But what takes an organiza‐ tion from surviving to thriving? How can it utilize data analytics to capitalize on success and leapfrog its competitors? What steps must a company take to transform from merely reacting to anticipating future demands and opportunities? v Industry leaders achieve success by implementing a solid busi‐ ness strategy, advancing their organizations’ analytics maturity, and leveraging a strategy called analytics democratization. Analyt‐ ics maturity is a measure of how far a company has progressed toward integrating analytics into its culture, processes, and business decision making. One step toward analytics maturity is analytics democratization (also referred to as data democratization), which makes organizational data and analytics capabilities accessible to all employees within the organization who can use it to improve operations, efficiency, and financial performance. Democratizing analytics doesn’t mean opening all data to everyone throughout the organization. This doesn’t mean opening all data to everyone throughout the organization. Rather, it’s empowering employees in a moderated and structured way to better assess and analyze data appropriate to their role in the business. It’s giving them power to derive insights from data to optimize and augment business processes with analytics at all levels within an organization in order to improve efficiency, save costs, and gain profits. To understand how analytics democratiza‐ tion is beneficial, it’s important to compare the practices of industry leaders who’ve adopted analytics democratization to those who lag in competitiveness. Organizations that democratize analytics have a higher level of ana‐ lytics maturity compared to those that do not. Those businesses that rise to the challenge of democratizing analytics gain sustainable competitive advantages across many business and financial metrics. Democratizing analytics provides competitive advantages. For example, analytically mature organizations outperform compet‐ itors in three-year and five-year operating income as well as many other financial metrics. Even small changes in analytics maturity pay huge dividends. Businesses that leverage analytics to drive decision vi | Introduction making pull in nearly six times more revenue than businesses that use analytics only for reporting!1 Evolving into an analytically mature organization provides many benefits and opportunities, but how does a company get there—and what obstacles must it navigate along the way? 1 International Institute for Analytics, “Analytics Maturity Powers Company Performance”. Introduction | vii CHAPTER 1 Data and Analytics Challenges in Modern Organizations Accessing and analyzing data presents difficulties for every organi‐ zation. In most organizations, data follows a path from creation to insight that is both delayed and restrictive. The flow is usually something similar to Figure 1-1. Although businesses may follow different processes, common fric‐ tion points exist. For example, any juncture where data is collected or shared (across applications, business units, repositories, etc.) introduces pressure to translate that data in some way to ensure it becomes (or remains) actionable for the recipient (system, team, etc.). These data transformations can introduce errors and delays, which add up as the system’s complexity increases. The pain of this friction is keenly felt in the amount of time it typically takes for transactional data to become actionable insights. Imagine a retailer trying to make as many sales as possible during a heavy holiday season. Things are going well, and the retailer sells most of its local inventory during the course of the day. The data is typically stored in transactional systems and is transferred with some frequency to a centralized warehouse for analysis and reporting. 1 Figure 1-1. Simplified Business Process: This example product sale flow demonstrates the path data takes across business functions in a modern organization. Unfortunately, for this retailer, data extraction and loading into its warehouse usually occurs nightly, meaning this valuable insight won’t be available until the next day. Even if the data is available immediately, however, a process or person is typically involved to derive and interpret insights from the raw transactional data. All of these delays mean that the retailer might be unaware until much later in the day that its local storefront needs additional stock, potentially missing out on a large volume of sales. For almost all organizations, the data input at the transactional point of business is raw and constantly changing. Furthermore, in some cases data is manually inputted by a person, which can introduce errors and delays in the process. This means that ingesting, cleaning, analyzing, and presenting the data for insights creates a significant delay in business decisions and action. Time isn’t the only concern, though. The complexity of the data itself strains organizational expertise. Different areas within the business have different analytics needs. What answers important questions for the sales department will likely not suffice for the 2 | Chapter 1: Data and Analytics Challenges in Modern Organizations shipping and receiving division. Furthermore, the nature of the data (as well as the related insights) varies wildly depending on where in the data path one performs analysis: inventory data, customer data, and production data have entirely different features, formats, and relationships between entries. As a result, data in the organization will be siloed and require subject matter expertise specific to each area (and the data will often sit in corresponding organizational silos). However, these professio‐ nals typically lack the analytical skills and tools needed to derive sophisticated insights from the data. So, they turn to data experts: data engineers, data analysts, and data scientists who conversely lack insight into the unique needs of each line of business. This leaves the organization with a large gap between business expertise and analytics expertise. This “analytics chasm” delays insight generation and wastes analytics expertise on business-as-usual questions. Technology choices also create difficulties unique to each organiza‐ tion. Recent years have seen a dramatic shift in data management, analytics, and business intelligence technologies and practices with the advent of remote storage and computing. Organizations are rapidly transitioning from locally run servers to cloud-based infra‐ structure. This means that the data path shown in Figure 1-1 is evolving. New technologies, such as machine learning and artificial intelligence, are being developed to interact with these cloud-based systems. As technology becomes more advanced, industries struggle to employ and retain expertise to keep up with these trends. Addi‐ tionally, many of these technologies require significant investments of time, money, and labor to implement. With these concerns in mind, examine how the traditional approach to the data path in Figure 1-1 hinders productivity, delays insights, and prevents the business from competing effectively. Figure 1-2 illustrates several places within the flow of a product sale that are affected by the inability to directly access the information needed to make intelligent decisions. Each friction point impacts the costs and revenue generated by the company. Together, they make the difference between a company being just functional versus industry leading. How might giving access to analytics help these areas? Let’s dissect this diagram from the perspective of the individ‐ uals involved to better understand how these friction points impact Data and Analytics Challenges in Modern Organizations | 3 the business. At the same time, let’s also look at how access to analytics improves performance. Figure 1-2. The Data Flow of a Product Sale: Inefficiencies and missed opportunities in data flows become pain points. However, opportunities exist to improve analytics delivery. 4 | Chapter 1: Data and Analytics Challenges in Modern Organizations The following list takes a deeper look into how the pain points in Figure 1-2 interrupt your business flow and examines how access to data and analytics could lessen that impact: Suppliers A great many places in your business rely on data from other areas. This is especially true for those outside your organization on whom you depend. Before a product can be made or a service rendered, your business needs resources. How do you determine which materials you need and how many? You must not only communicate your needs to the supplier but also estimate how other factors could influence those needs. The ability to analyze stock, supply, and demand data enables those responsible for ordering your resources and those providing them to you to monitor and quickly react to changes in the supply chain. This prevents over- and understock issues and may reduce the impact that supply chain problems have on your other business processes. Production The people who create or provide your company’s goods and services also need analytics. They must know not only how much demand exists for your product but also how much time, effort, and resources are required to meet that demand. Access to analytics tools and on-demand data would allow them to address business needs more proactively, manage employees more nimbly, and better prevent potential issues from impact‐ ing their work. Analytics tools would also deliver insights into how well a production line runs and how it might be improved. Marketing This is the leading edge of the customer-facing side of your business. Without data analytics, marketers have few tools at their disposal to understand customer behaviors, determine what customers want across various digital channels, assess which marketing campaigns are effective, and determine how to adapt your product’s pricing, packaging, positioning, and place‐ ment to remain competitive in the marketplace. They also need data insights to understand and react to regional preferences and customer demographics. Providing your marketing team with analytics tools and access to data helps ensure that your business appeals to customers, provides the right goods or serv‐ ices, and reaches the necessary markets. The marketing team Data and Analytics Challenges in Modern Organizations | 5 should have access to behavioral, regional, and demographic data to provide insights back to the company as to what is in demand and how competitive your business is within the marketspace. Sales The people who facilitate your customers’ purchasing of your product or service are invaluable. In most organizations, these individuals are given access to transactional systems, allowing them insights into stock numbers, pricing, and local revenue. Is this enough? A sales team with access to analytics could look at buying behaviors, patterns, and shifts in the local store or terri‐ tory. They could adjust strategies around an anticipated product stock-out or prepare for upcoming demand due to changes in products, services, pricing, or promotions. Finance Your finance team requires data to determine how to distribute funds throughout your business and monitor revenue sources. Data insights need to be derived across all areas of the busi‐ ness—from supply chain to employee pay rates and benefits to revenues from sales. These data points coalesce to tell the story of the success or shortcomings of your business. Without analytics tools, financial decisions are typically based on sum‐ mary values. While this approach meets the basic needs of the business, it does little to anticipate or preempt change. Analytics tools help a finance team go from looking at the now to looking at the patterns leading up to now. The team needs the ability to deeply understand past performance and simulate potential futures to be ready when a financial challenge presents itself. Executives and shareholders The leaders of your business need data insights to make deci‐ sions on how to move the company forward. Insights delivered to these leaders take time to create, and these delays can have impacts on the decisions made. Moving decisions closer to when events happen is part of the democratization process. It’s a key step to becoming proactive, rather than reactive, with data. Improved analytics capabilities, combined with faster data delivery and cleanup, mean that insights get to leaders sooner. 6 | Chapter 1: Data and Analytics Challenges in Modern Organizations Customers and prospects Even your customers and prospects need access to data and analytics. They want to know what items are in stock, how much a given item costs, what delivery or pickup options are available, and alternative options in case their item of interest is out of stock. They want visibility into order status, when it will ship, and, ultimately, when it will arrive at their doorstep or be available for pickup. Today’s customers also expect organiza‐ tions to reach them on their preferred digital channel of choice with information on upcoming sales and purchasing opportuni‐ ties. In the experience economy, customers demand that you know what they want, when they want it, and their preferences. Data also needs to break free from silos within an organization. Cross-functional data access opens the opportunity to generate insights that improve business performance. It’s easy to see how finance units need insights from HR, supply chain, and sales teams, but other areas benefit as well. Sales teams need to know when mar‐ keting campaigns influence their business. Marketing teams need to understand the success of these campaigns from the sales teams. Production teams rely heavily on the demand indicated by the sales teams as well as the available resources from the supply chains. Minimizing waste and absorbing demand spikes (as opposed to the more general bottom-line improvement) require your business to leverage multiple (often disparate) data sources to develop holistic insights about critical business processes. We can dive deeper into how access to data analytics affects your organization by looking at things from an employee’s role. What level of data access does each of your employees need? How knowl‐ edgeable are they in the full process of the business, and if they were offered access to analytics, how much skill would they have interpreting the results? Table 1-1 breaks this down for us to exam‐ ine. It also helps us understand where gaps exist between analytics needs, business knowledge, and analytics tool expertise. Data and Analytics Challenges in Modern Organizations | 7 Table 1-1. Employee data needs and skill sets across a typical organization Employee role Executive Data and analytics needs Aggregate view of the organization; relies on others to generate reports Business knowledge Top-down view of the organization with an eye to the overall success of the business Analytics tool knowledge Likely is able to view analytics reports, but is probably not familiar with analytics or reporting tools Middle More granular view, Strong business May have some analytic management usually specific to the knowledge in its knowledge but still relies area of the business it respective areas on others to generate manages needed insights Business Need detailed reports to Experts in how the May have some analytics subject matter dig into how the business functions knowledge but still rely experts business is functioning on others to generate needed insights Data subject Need full access to data; May have some business Strong analytics skill set, matter experts should have the skill set knowledge but usually usually with the ability to to analyze gather requirements for drill deeper into the data organizational data analytics from those in order to provide closer to the business insights Information Needs the ability to Familiar with the business Strong technical skills technology (IT) support the gathering, enough to ensure that with installing and storage, and IT needs are met but maintaining analytics maintenance of data and probably not integrated tools but may or may not analytics tools in the actual lines of be an expert in how to business use them Lines of Data is needed to Strong business Probably little to no business understand how the knowledge, though likely knowledge on analytics business is functioning localized to their tool usage and may or and make more localized particular role may not have access to changes the data itself Data scientists Data is everything they Focus is on data Strong analytics tool deal with, so they need and digging deep into expertise, with emphasis access and multiple it, but may not be on artificial intelligence analytics tools knowledgeable on the and machine learning nuances of the business As we can see from Table 1-1, each role has differing data needs, business knowledge, and analytics-tool skill sets. Those in the busi‐ ness who make decisions and need to derive insights from the organization’s data typically lack the depth of technical skill neces‐ sary to generate results using analytics tools. Similarly, those with strong analytical and technical skills typically lack the deep knowl‐ edge of how the business runs. 8 | Chapter 1: Data and Analytics Challenges in Modern Organizations The Issue: Insights Not Available Where Needed At the root of this complex predicament in which modern organ‐ izations find themselves is a simple question: who has access to data, analytics capabilities, and insights? Business leaders are the typical recipients of analytics reports in an organization, but they may not be versed in the skills needed to interpret the results or question the report’s validity. They rely heavily on the strengths of their data teams to collect, analyze, and deliver insights for them to react. From there, the results may trickle down to middle managers but usually never reach the frontline employees. As Figure 1-3 illus‐ trates, there is an “analytics gap” in modern business between those who have the questions and those who have the skills and tools to answer them. Figure 1-3. The Growing Divide: A chasm exists between those who have analytics expertise and those who have questions. Additionally, data for many companies is a commodity that is often restricted and protected. Does it all need to be? Security typically assigns permissions based on a mentality of prove that the user needs access rather than prove there is a reason to restrict access to this data. As a result, many functional areas within the company that could benefit from analyzing data are restricted from accessing data in the first place. The Issue: Insights Not Available Where Needed | 9 What does this restriction of data access mean for your company? According to Dresner Advisory Services, 50% of all businesses have some sort of lag in their data path, and at the same time, 50% claim they make data-driven decisions.1 There is a correlation between these two facts. The faster the data path, the more likely a company is to make data-driven decisions. Dresner also indicates that 77% of companies expect their data frequency requirements to increase in the future. So, if your company wants to be a leader in your market, you want to get ahead of this need. If you want to make real-time decisions based on data, then you need the capability to analyze this data as close to real time as possible. The typical manual processes used by most organizations make this scenario appear inevitable. Is there any way to enable all layers within an organization to utilize data effectively and safely? This is the vision of analytics democratization. A New Approach: Democratizing Analytics Democratizing analytics means making both data and analytics capabilities available at all levels of the organization. To achieve this, organizations will need to look for intuitive, easy-to-use analyt‐ ics platforms that are accessible to any skill set—from novices to experts. In short, it’s data, and the ability to draw insights from it, for anyone at any level of the organization—from leadership to marketing to sales. This removal of obstacles preventing the deliv‐ ery of insights from data makes the company more nimble and faster to react to growing change and increasing competition in the marketplace. Analytics democratization is made possible through a combina‐ tion of process and technology innovations. On the process front, democratizing analytics requires implementing a standardized, gov‐ erned approach to sharing data and analytics capabilities with those who could benefit from it. This doesn’t mean that data just goes to everyone haphazardly. But it does mean that we need to shift our mindset and approach from a data-hoarding to a data-sharing mentality. Regarding technology, democratizing analytics requires 1 Dresner Advisory Services, “Data Latency Remains a Challenge as Data-Frequency Requirements Increase”, December 14, 2022. 10 | Chapter 1: Data and Analytics Challenges in Modern Organizations user-friendly, flexible, analytics tools with reusable and repeatable workflows to enable the new data beneficiaries. The ability to analyze data at any level of the organization means that employees at the middle management and business operations levels are able to draw and interpret insights from data. Being closer to the business operations means that these professionals can com‐ bine data-driven insights with functional knowledge of the business. Combining these two things generates opportunities for continuous strategic improvements at small and moderate scale. It also allows those employees closest to the customer to improve the customer experience with data-driven insights. Broader analytics access means faster insights and more innovation. When companies provide analytics capabilities and data access at all layers of business, they open the doors to innovation, faster insights, and freedom to develop advanced analytics to make more intelligent business decisions and be better prepared for the future. These organizations that democratize analytics position themselves to become leaders within their industry. A New Approach: Democratizing Analytics | 11 CHAPTER 2 How Democratizing Analytics Improves Business Outcomes We’ve seen that democratizing analytics can improve your business performance, but what does that look like in a real-life example? How can enabling employees with access to analytics tools provide a business with success—and, more important—what types of success is it able to achieve? Let’s take a look at a company that democratized analytics to see how enabling employees with analytics allowed them to overcome challenges and achieve success. Case Study: Chick-fil-A Chick-fil-A is an $11.3-billion quick-service restaurant company that operates close to three thousand stores across the United States. It has grown significantly over seven-plus decades, and this growth has affected Chick-fil-A’s relationship to its data. Chick-fil-A wanted to better understand its customers and personalize offers via a loy‐ alty application, but the company’s expansion generated pockets of siloed data, and collecting data to report holistically became challenging. Chick-fil-A’s analysts struggled to get access to data and relied on IT support. Even when analysts did get the access they needed, the data was often so immense that it was prohibitive to query. Additionally, many of the analysts were familiar with reporting tools but lacked the technical skills to access, retrieve, and analyze the data effectively. 13 The hardships Chick-fil-A faced were hardly unusual: large amounts of data were available from which to derive insights, but access was limited to a select few who lacked the necessary tools and skills. The company’s supply chain manager sought a solution that would allow analysts to dive into the data and provide insights to employees across business units. Chick-fil-A needed analytics democratization. Chick-fil-A turned to Alteryx, an analytics automation platform, to help access, prepare, blend, and analyze data across the organiza‐ tion. With Alteryx, the Chick-fil-A team documented and simplified once-arcane queries and joined disparate data sources to provide deeper and more informative insights. These new capabilities deep‐ ened the company’s understanding of customer needs and led to the development of a new loyalty application. With user-friendly work‐ flows and the flexibility to examine its data in new ways, Chick-fil-A is now able to plan for the future and better understand customer preferences instead of just reporting on the past. Analytics Maturity: The Hidden Key Performance Indicator Driven by Analytics Democratization Chick-fil-A’s approach highlights how a company can go from strug‐ gling with analytics to leading or disrupting its market with analyt‐ ics innovations. This is an example of an organization advancing its analytics maturity. Analytics maturity is an objective measure of how well an organization employs analytics and data throughout its operations. Analytics maturity is an important key performance indicator (KPI), and those businesses that fail to measure and track it may find themselves trailing their industry competitors. Multiple independent researchers study and rank industries and businesses by analytics maturity, and the results typically break down into five levels of maturity:1 1 Alteryx, “The 5 Stages of Analytics Maturity”, INPUT by Alteryx (blog), July 2022. 14 | Chapter 2: How Democratizing Analytics Improves Business Outcomes 1. Beginner These organizations are just starting to adopt analytics. They usually have multiple siloed stores of data and lack execu‐ tive support for implementing additional data and analytics resources. 2. Localized Organizations at this level have started using analytics within a few specific departments. They use analytics insights to make some decisions, but investment is minimal. 3. Aspiring These organizations are using analytics more broadly. They are beginning to experience the benefits of being data driven. 4. Analytical This group of organizations fully embraces analytics and uses it extensively in decision making. High analytics competence drives corresponding corporate performance, as these compa‐ nies set the standard for optimal data usage in their industry. 5. Competitor This final group not only fully embraces analytics but also has taken the next steps to leverage analytics for competitive advan‐ tage in their industries. Analytics opens new operating models and revenue streams for these organizations, enabling them to leapfrog peers or disrupt the marketplace entirely. These five levels form a measuring stick for how far your com‐ pany has traveled toward analytics maturity. The levels not only show you where your company falls on the maturity scale but also illustrate what needs to be done to reach the next tier. Increas‐ ing your business’s analytics maturity is critical to operating more efficiently, making better-informed decisions, and competing more effectively—ultimately setting your business on a path to industry leadership. Concrete Benefits of Democratizing Analytics Sharing data and analytics more broadly benefits your company as a whole as well as its constituents and stakeholders. Take the perspective of those directly affected by this improved access and empowerment, as shown in Table 2-1. Concrete Benefits of Democratizing Analytics | 15 Table 2-1. Examples of the beneficial impact of democratizing analytics for your employees, suppliers, partners, and customers Individuals Executives Shareholders Middle management Analysts Employees Customers Suppliers Partners Impacts of democratizing analytics Less time between asking a question and getting a data-derived insight Insight into the business without waiting for reports Insight into area-specific data analysis and the ability to make insight-driven decisions to improve business success Less time generating reports and more time focused on value-added diagnostic and advanced analytics Granular-level insights inspiring innovation and improvement in business processes Faster response times and better customer service Better understanding and adaptability to meet your company’s demands Cooperative understanding and competitive edge against competitors in your market These compounding benefits affect the performance of your com‐ pany as a whole. As data analytics becomes more deeply integra‐ ted into your organization, its usage provides new insights into the challenges and opportunities you face. These insights help you make smarter decisions at an executive level and throughout your organization. Faster insights mean issues and opportunities can be identified and addressed more quickly—top to bottom. As employees adopt easy-to-use analytics tools and resources, they can ask and answer questions specific to their roles. Achieving these granular insights improves localized performance and increa‐ ses frontline success. For example, improved access to analytics improves purchasing supplies, providing insights into shipping and component costs. It gives insights into online transactions, improving interaction times, user experience, and customer satisfaction. Customer data offers demographic and purchase tendencies and trends, allowing you to better provide the goods and services that customers desire. Taken together, these kinds of data-driven improvements drive down costs, increase revenue, reduce risk, improve customer satisfaction, and enhance your competitive posture—all of which impacts your bottom line. 16 | Chapter 2: How Democratizing Analytics Improves Business Outcomes At this point, the benefits of democratizing analytics within your organization are likely evident. However, some functions may make you feel uncomfortable with opening data access to more users— often the case with IT. Let’s take a look at how you can address con‐ cerns about data security, privacy, and governance while continuing to move toward analytics maturity. Concrete Benefits of Democratizing Analytics | 17 CHAPTER 3 Democratizing Analytics and Data Governance Is it possible to provide greater access to data across your business while still maintaining high security and privacy standards? After all, sensitive or personally identifiable data, such as employee data and customer data, typically has restrictions on who can access it, for what purpose, and when. Sensitive information faces increased risk of theft, loss, or exposure when more people have access. Pro‐ viding additional access means you must undertake more frequent and broader audits to ensure that sensitive data is kept secure. To understand how to implement analytics democratization appro‐ priately, let’s quickly review the principles of data governance: Accountability Security is typically relegated to one department, such as IT; however, everyone in your organization must understand the value and risks involved with data access to ensure that the data is used properly. To do this, accountability has to cross all areas of your organization and is best represented by a governing body. Regulation Your organization needs well-developed, well-defined rules for data usage and access. These rules should be flexible enough to apply to all levels of the organization but restrictive enough to ensure the security and privacy of the data being accessed. 19 Stewardship Each area within the organization needs expertise around its data. These data stewards understand the data, its security con‐ cerns, and its nuances. They provide the basis on which the regulation of the data can be built. Quality Data is useless if it is incomplete or incorrect. Standards related to accuracy and completeness need to be established. In addi‐ tion, the data areas need a rigorous testing process to ensure that as new data comes in, it is as complete and error free as possible. How do you gain the benefits of democratizing analytics without increasing the risks to data security and privacy? Let’s look at how companies can maintain solid data governance and why it works hand in hand with analytics democratization. Case Study: UBS Group AG UBS Group AG (aka Union Bank of Switzerland or UBS), a global banking leader, needed data insights throughout its organization. With more than 70,000 employees and offices in more than 50 countries, the size and spread of the company presented a huge challenge to any solution applied across the entire organization. Like other companies, it was concerned with making data and analytics tools available while also maintaining governance over the data. It needed a repeatable, easy-to-use, and easy-to-maintain system to provide analytic insights across the organization. To move forward quickly, UBS broke the solution into three key parts. First, it utilized a self-service method for data access and preparation via the Alteryx analytics automation platform. This applied not only to existing data sets but also across data sets. It developed self-service templates using Alteryx no-code workflows to gather, clean, transform, and store data. By using workflows, it made analytics capabilities not only dynamic and repeatable but also easily shared and auditable. Once a data-preparation algorithm was established and validated, others in the organization could reuse it for analysis of their own. The second part of the UBS solution integrated machine learn‐ ing. It empowered employees to use existing workflows to gather, 20 | Chapter 3: Democratizing Analytics and Data Governance clean, and provide reliable data, and it provided an opportunity for employees to test and manipulate the data based on theoretical changes in the business. This meant that users could not only use analytics automation to present information on what had happened within the company but also simulate new scenarios and improve business performance. The final step in the UBS process addressed data governance. The workflows generated within the organization were broken down into types based on what they did and who interacted with them: End user processes These workflows are owned and governed by the organization as a whole and contain automation. They are created by indi‐ viduals with business knowledge throughout the company (not members of IT). Examples of these workflows include revenue forecasting, risk ratings, and portfolio management. IT-managed applications These are more complex workflows that involve in-house or ITrelated coding languages like SQL, Python, or R. These work‐ flows are managed by IT via a formal software development lifecycle. Examples of these workflows include pricing and valu‐ ation, transaction monitoring, and fraud detection. Automated applications These workflows run on a regular basis or are triggered by other events. These types of processes include analyzing customer activity and other outward-facing interactions like chatbots. Additional workflows Workflows that do not fit neatly into the previous categories are classified in a separate group typically containing non-businesscritical or ad hoc analysis. They are usually developed at the line of business for quick analysis. Examples of these workflows include equity and economics research as well as discounted cash flow analysis. These classification guidelines each come with requirements created by the company. They are shared with users to help them define their workflows. These guidelines give the company the ability to add, remove, or change restrictions based on data security and privacy, and they leave freedom for users to develop workflows that meet their analytics needs. This approach empowers users to Case Study: UBS Group AG | 21 correctly classify their workflows as well as govern them to meet the standards required by the organization. Any analytics workflows developed within the organization are regularly run through the governance process to determine that they function correctly and generate reliable results. UBS’s best practices dictated that each workflow must have a sponsor, an owner, and a validation team led by a head validator. Together with a well-defined method of registering workflows, this ensures that workflows are kept up to date and in compliance with organizational requirements.1 Even though banking is one of the most heavily regulated industries, UBS was able to democratize data analytics to its employees through a thoughtfully controlled development process combined with easyto-use, easy-to-share, and easy-to-repeat analytics workflows. UBS implemented analytics democratization in a way that was both well governed and impactful to its business. Learnings: UBS’s Category-Driven Approach The UBS study highlights key challenges you may face and strategies you can employ in the process of democratizing data in your own business. First, the study makes clear that IT cannot be there to do every‐ thing involving data. IT is generally fully occupied keeping the transactional systems functioning. Additionally, IT typically lacks the necessary business knowledge to accurately create and maintain business workflows for insight generation. So, UBS empowered its business users to develop, maintain, and help govern their work‐ flows, freeing IT to focus on technology-specific aspects of the business. Second, UBS highlights the benefits of empowering business users. Governance was decentralized, engaging the owners of the work‐ flows. Rather than requiring a single group to figure out what was happening within tens of thousands of workflows across the com‐ pany, each workflow was managed and maintained by the user who created it and governed by a localized validation process supported by well-documented company-wide classifications. This approach 1 Alteryx, “UBS AG: On Governance of Models and Workflows in a Regulated Environment”, video. 22 | Chapter 3: Democratizing Analytics and Data Governance ensures that even the smallest localized workflows are governed and maintained. Additionally, since the data is analyzed within the business units, far more granular insights become possible, as the analysis is infused with users’ intimate knowledge of their products, customers, and business functions. Finally, UBS illustrates the power of analytics workflows. By com‐ bining data collection and cleanup processes into a workflow, the company overcomes three significant issues. First, classifying the workflows means that they can easily be shared and reused by others within the organization based on the type of flow rather than the data it accesses. Second, sharing analytics workflows means that data once siloed to a particular area can now be accessed and utilized by others who may benefit from similar analytics insights. Last, using workflows provides process transparency on how areas of the organization utilize and manage process-specific data. So, over the course of five years, UBS went from having one analytics user to having more than three thousand analytics users around the world on every continent. The company runs more than six thousand workflows each day and uses analytics for fraud detection, capital planning, and forecasting.2 Case Study: BT Group Let’s consider another example of how governance and democratiza‐ tion work together. BT Group (aka BT and British Telecom) is a telecommunications and network provider in the United Kingdom, serving more than 180 countries. As part of providing a wide range of services related to voice, mobile, and broadband, the company is required to meet strict standards in reporting while also being competitive in its market. This leads to serious obstacles not only with collecting and combining all data but also with doing so in the correct order. Before BT embarked on analytics democratization and automation, it ran a process it called the “Cascade.” In simple terms, it was a huge process linking data from more than 140 spreadsheet models together sequentially to generate necessary reports. It was cumber‐ some, error prone, and time consuming to run. It often took up 2 “Alteryx (AYX) Q2 2021 Earnings Call Transcript”, The Motley Fool Transcripts, August 4, 2021. Case Study: BT Group | 23 to four weeks to execute and required multiple runs before the necessary reports were successfully completed. Obviously, the Cascade was not sustainable. Not only did it illustrate the difficulties of working with multiple restricted data sources, but it also highlighted the obstacles of 1,500 unique data sources and methodologies. No one was able to comprehend and maintain the overall workflow, as it was simply too complex, with each area requiring its own unique set of rules and processes. Knowledge transfer was difficult, if not impossible, and annotation was chal‐ lenging within the format of the spreadsheet data. BT worked with PwC (PricewaterhouseCoopers) to find a solution that would render the Cascade obsolete. The reporting requirements still needed to be met, and the individual areas within the organiza‐ tion still needed to function to meet their own regulatory require‐ ments. How could the annual reporting combine all these disparate data sources to produce the necessary reports without the need for months of data preparation and development? PWC helped BT move its process into analytics workflows. Rather than each portion of the company generating data that required manual manipulation and weeks of transformations and joins, the workflows introduced automation to simplify the process. The workflows allowed the analytics process to be accessible at all levels of the organization. This analytics democratization gave each team the access and ability to manipulate area-specific data to meet the requirements of the annual reporting process. BT’s intelligence analysts worked with PWC to establish guidelines for tool usage, data paths, and conventions across the company. The governance guidelines they provided meant that each of the distinct business units had tools and strategies to work cooperatively with one another without having to worry about developing a gov‐ ernance model itself. Additionally, these guidelines meant that once the individual analytics workflows were complete, they were trans‐ parent and easily reviewable through an audit process to ensure that the calculations were complete and accurate. The final aspect of introducing analytics workflows was the doc‐ umentation embedded directly within each workflow. Each busi‐ ness area was provided with the necessary tools to document its workflows, ensuring a smooth knowledge-transfer process among company personnel. The teams also leveraged simple innovations 24 | Chapter 3: Democratizing Analytics and Data Governance like color coding and links to further organize and chronicle their processes. This documentation made audits, reviews, and transfers of the workflows essentially seamless. All of these strategies employed by BT and PWC replaced the Cas‐ cade. Now, BT utilizes organizational strategy and analytics auto‐ mation to accelerate the report-generation process. The efficiency of the annual report process has improved by more than 75%. Additionally, the added transparency into the process benefits gov‐ ernance by providing new insight into the processes and calcula‐ tions. Lastly, automation and reduced redundancy save BT money in several ways. Instead of spending hours running the Cascade, employees now have time to focus on removing unnecessary pro‐ cesses and improving data workflows.3 These examples illustrate how analytics democratization harmonizes with analytics governance. Sharing data and analytics empowers your whole organization. Democratized analytics provides insight from the executive level through the customer, leading to improve‐ ments in business process efficiencies, decreasing obstacles and issues, and resulting in higher quality customer service. Democratized analytics drives efficiencies, decreases obstacles, and raises quality. Data governance and security is a way of life in modern business, and by using self-documenting analytics workflows, training, and decentralized governance models, you allow your company to take full advantage of analytics without compromising data security and privacy. These guiding principles provide the basis for successful analytics democratization. Now let’s explore how your organization can get started on the path to democratized analytics. 3 Alteryx, “How BT Is Automating Regulatory Compliance with Alteryx and PwC”, Alteryx Customer Story. Case Study: BT Group | 25 CHAPTER 4 Best Practices for Analytics Democratization The journey to democratizing analytics and advancing your organi‐ zation’s analytics maturity is both challenging and rewarding. You may encounter one or more of the following trials along the way:1 Data quality Many organizations have an overwhelming amount of data. That data is typically decentralized, low in quality, and prohibi‐ tive to connect to. As such, when blending in additional data sets or producing insights, some doubt may be cast on the accuracy of the results. Enterprise adoption Analytics democratization aims to drive adoption throughout the organization. This means that buy-in is needed from ana‐ lysts, managers, leaders, employees, customers, shareholders, and more. It’s challenging to create a shared desire across so many constituencies to understand and utilize data and analytics to make analytics-informed decisions in the business. 1 David Alles, Research Brief: “Competing on Analytics by Industry”, International Insti‐ tute for Analytics, 2020. 27 Leadership support For a democratization effort to gain traction, the leadership of the company must understand the benefits and be suppor‐ tive of the initiative. It needs to understand that analytics democratization is not opening the organization to vulnerabil‐ ity but rather providing valuable insights into—and improv‐ ing—organizational processes. The involvement of leaders ensures that the move toward democratization is strategic and, importantly, funded. Technology Shifting to an analytically-driven organization requires the right technology. When democratizing analytics, it’s not complex analytical software that you need, but rather an easy-to-use, easy-to-repeat, and easy-to-share method for providing analyt‐ ics capabilities and data access to those with fewer technical skills. This approach also requires that data be stored in a responsive, easy-to-access location and made accessible to those who need insights from it. Analysts and trainers Disseminating new skills and knowledge to the workforce requires support from technical experts and trainers. Their roles will be not only to understand the analytics solutions provided but also support the rest of the company as employees learn to use new analytics capabilities to generate insights. Let’s look at how a leading global organization navigated these bumps in the road. Case Study: Phillips 66 Phillips 66, an energy company, faced all of these challenges as it worked to build a data-driven culture. Phillips had a dedicated analytics team that reached out to employees across the company to understand their analytics use cases and skill levels. What the team discovered was that many users were looking for a way to automate analytics processes managed in spreadsheets. The analytics team determined that a new approach would be beneficial. As Phillips 66 already leveraged Alteryx within the analytics team, it decided to expand usage of the platform beyond the analytics team environment. It hoped that by providing analytics tools directly to 28 | Chapter 4: Best Practices for Analytics Democratization frontline workers (rather than making them rely on the analytics team and wait to receive information), the organization would real‐ ize the benefits of analytics democratization. The analytics team started small, with a group that was already somewhat fluent in analytics. The strategy was to leverage word of mouth to encourage other employees to get involved in the analytics process. The training was successful, and soon many employees out‐ side the initial group started asking to be involved in analytics. This led to a new complication for the analytics team: personally training everyone was no longer a viable option. Instead, the team would need to develop a self-sustaining culture to nourish this newfound appetite for data analytics. The analytics team at Phillips 66 approached these training and data challenges by cleverly solving two problems at once. To encourage frontline workers to adopt analytics processes and improve under‐ standing of how the organization functioned, the analytics team began requiring prospective trainees to do two things to gain admit‐ tance to training courses: 1) share their own data obstacles; and 2) get an Alteryx license. This strategy ensured that the training would solve real data problems that trainees faced while providing hands-on experience with the new analytics software. The analytics team continued to provide training for new analytics users, building relationships with those interested in learning more about their data. This ongoing practice not only introduced the new analytics users to analytics experts but also provided the analytics team with insights into who, how, and where analytics was being applied at Phillips 66. At the same time, the analytics team fostered a broader analytics community across the organization. This commu‐ nity supported itself through knowledge sharing, innovation, and documented, repeatable analytical techniques. As such, the analytics support model organically evolved from relying exclusively on tech‐ nical experts to being community driven. These organizational shifts created another opportunity for the analytics team. By illustrating the benefit of providing analytics capabilities to employees throughout the organization, the analytics team demonstrated the value of a data-driven approach to Phillips 66’s senior leadership. The grassroots model quickly and effectively convinced executive leadership that a data-driven culture was not only beneficial but also required to move forward in the industry. Case Study: Phillips 66 | 29 Phillips 66 has evolved into a data- and analytics-driven organi‐ zation. It now utilizes analytics at all levels of the business and relies on analytics insights to drive the company. The analytics team within Phillips 66 still holds regular training sessions on how to use data and analytics as well as the software; however, the communitybased support structure that the analytics team established allows it to spend more of its time focusing on Phillips 66’s most challenging data questions. How does Phillips 66’s experience in data democratization help you in your own company’s transformation? Let’s look at some of the steps that Phillips 66 took. Engage Experts for Guidance The democratization of analytics often starts with those most famil‐ iar with data analysis: the analytics team. The individuals in your organization who deal with data and analytics questions understand the benefits of their skills and the insights they provide. The chal‐ lenge for those experts lies either in finding a method to quickly distribute actionable insights across the business or in developing a solution to help more employees generate their own insights. Engage with your analytics team, and they may illuminate a path forward for your business. Gain Executive Support A data-driven culture encourages growth, efficiency, high perfor‐ mance, and innovation-driving product and service improvements as well as cost savings for the company. Identify and measure those relevant KPIs in your organization, and estimate both where quick improvements can be made and where the most ROI is expected. Analytics democratization increases analytics maturity, and thirdparty research shows that analytics maturity impacts an organiza‐ tion’s bottom line and competitive position in its industry. The broader organization will need to understand the benefit of investing time and resources in analytics initiatives. One way to do this is to encourage executives to model the new analytics-driven behaviors desired by leveraging data and analytics as part of their leadership process. Encouraging leadership to adopt analytics into its workflow not only provides it with valuable decision-making 30 | Chapter 4: Best Practices for Analytics Democratization insights but also demonstrates for the rest of the organization the benefits and outcomes desired from the analytics democratization process. Start Small Analytics democratization doesn’t happen overnight. You must first demonstrate value and build from there. This can be done with a proof-of-concept trial within the organization. Engage candidate “power users” or a small, motivated group within the company and provide them with new analytics software capabilities and training. Aim to help them solve a well-known or long-standing problem with analytics. As more and more members of the organization see their workloads simplified thanks to analytics, they become advo‐ cates for the transformation. Demonstrate Benefit It’s common for individual contributors, mid-level managers, and senior leaders to resist change. So, you must make clear the benefits of analytics democratization to each constituent—from the frontline worker who will need to learn new tools and techniques to the busy executive who will need to consume insights in a new way. You may want to consider constructing incentives to encourage behavior change. Phillips 66 did this by requiring employees to bring problems to solve in order to get an analytics automation software license and training. By solving a localized problem for an individual or a team using automated analytics, trainers provide not only a solution to the problem but also an incentive to utilize the new analytics capabilities further. Train the Trainers The goal of analytics democratization is to deliver analytics capabil‐ ities to anyone and everyone who would benefit from them. For larger organizations, this can be a challenge. The team responsible for training in the use of analytics software may quickly become inundated with requests for support. In this case, find in-house power users to enable and empower to train others in analytics software as well as best practices. Train the Trainers | 31 Create a center of excellence by pulling together the analytics exper‐ tise within your organization and providing them with the tools to distribute support and training of analytics across the organization. Enable them to explore technologies that manage, store, and deliver clean, consistent data that supports multiple departments, end-user questions, and diverse use cases. Encourage them to build a com‐ munity around data analytics by leveraging tools such as blogs, hackathons, and social media within the organization. Teaching employees to support one another eases the burden on dedicated analytics and technical teams, so they can focus on delivering not just training but also other value-add work. Make Analysts the Foundation Your analysts can catalyze increased analytics maturity for your business as they provide the hands-on training, demonstrations, social content, and reference materials needed to support your ana‐ lytics democratization initiative. Ensure that they want to stay and keep the company moving in the right direction. As your company develops an analytics culture, these dedicated analytics experts act as internal thought leaders. They form the foundation from which a self-supporting community can grow, fostering the exchange of ideas and practices. Confirm that these analysts are empowered to leverage and experiment with new data. Establish an Analytics Council Create a cross-functional group of leaders that maintains oversight of analytics resources and activities across the business to ensure engagement in analytics, enable data governance, and identify and address issues. Establish agreement among teams regarding data security, quality, and auditing as well as analytics provisioning and user management. The analytics council should also establish mech‐ anisms for cross-team collaboration and the sharing of best practi‐ ces across the organization. 32 | Chapter 4: Best Practices for Analytics Democratization Keep the Process Moving Democratizing analytics generates returns quickly. Be sure to highlight the benefits to leadership, shareholders, customers, and employees. Use your initial analytics democratization victories to generate the momentum needed to drive the program forward and advance the organization’s overall analytics maturity. When obstacles appear along the way, break them into their con‐ stituent parts and prioritize those issues that appear to be quickly addressable. Use tangible examples of your success in one busi‐ ness area to promote analytics democratization to those who are more reluctant to change. When you create tangible localized improvements, you spark analytics democratization to spread fur‐ ther through your company. We have provided a checklist in Appen‐ dix A to help you get started on your journey. Empowering your whole organization with analytics creates mul‐ tiplying positive impacts. The ability to make ground-level datadriven decisions without delay helps ensure your business will withstand global, economic, and industry change. Withstand global, economic, and industry change with data-driven decisions. Analytics democratization provides you and your team with insights into how the company is performing and where gaps in your pro‐ cesses can be filled. Although there can be challenges in implement‐ ing a new approach to analytics in the business, you can surmount them by strategically building and embracing a supportive, adaptive, and data-minded community within your organization. With these strategies, you can lead your organization to greater analytics matur‐ ity and a position of industry leadership. Keep the Process Moving | 33 APPENDIX A Eight Steps to Getting Started with Analytics Democratization Use the guidelines provided in this article to start your analytics democratization journey: ☐ Determine where your organization currently falls on the ana‐ lytics maturity scale.1 Alteryx provides a free analytics matur‐ ity calculator developed in partnership with the International Institute of Analytics. ☐ Identify a set of candidate projects that are aligned to your strategic goals. Prioritize these projects with a cross-functional team. Select one to get started. ☐ Identify the project team who will work with you on democra‐ tizing analytics. Create a charter and define roles and responsi‐ bilities for the team. ☐ Obtain allies within your organization who will adopt and champion the analytics democratization process with you. ☐ Converse with leadership and your support teams to start the process and secure funding and resources for the project. 1 Preethika Sainam et al., “How Well Does Your Company Use Analytics?”, Harvard Business Review, July 27, 2022. 35 ☐ Identify the obstacles that may stand in the way of analytics democratization, and address them with your analytics commu‐ nity and leadership. ☐ Share this article with others to help them understand the ben‐ efits that analytics democratization and analytics maturity will generate for your organization. ☐ Celebrate successes early and often at all levels across the organization. 36 | Appendix A: Eight Steps to Getting Started with Analytics Democratization Acknowledgments We want to express our immense gratitude to the magnificent peo‐ ple who helped bring this report to life. A big thank you goes to Kalyan Ramanathan and Keith Pearce for their support on this project. Thank you to Steve Brodrick and Alan Jacobson for the enlightening conversations. Also, thanks to Christopher Gardner for his fantastic contributions and maintaining good humor in the face of unremitting feedback rounds and a continual flow of new ideas and information. Much appreciation to our technical reviewers Mattia Ferrini, George Mount, and Tobias Zwingmann. Finally, we want to thank the members of the Alteryx community who generously shared their experiences, best practices, and success stories. You inspire us with your passion, your ingenuity, and your skill in using automated analytics to spin raw data into meaningful business outcomes for your organizations. 37 About the Authors Melissa Burroughs is the Director of Technology Solutions Market‐ ing at Alteryx, where she crafts messaging and positioning strategy for the Alteryx portfolio in the context of multivendor solutions. She is an accomplished technologist, writer, and public speaker, bringing a passion for communication together with diverse experi‐ ence in science and analytics. With over 15 years in the software industry, Melissa has originated RAD development, professional services, and product marketing for a number of early-stage technology firms. Her prior work also includes accelerator physics research, medical data analytics, and other academic positions. Follow Melissa on Twitter @MelBuzz and connect with her on LinkedIn. David Sweenor is the Senior Director of Product Marketing at Alteryx, a top 25 analytics thought leader, an influencer, interna‐ tional speaker, and acclaimed author with several patents. He is a specialist in the business application of AI, ML, data science, IoT, and business intelligence and is responsible for the go-to-market strategy for Alteryx’s analytics portfolio. With over 20 years of hands-on business analytics experience, Sweenor has supported organizations including SAS, IBM, TIBCO, Dell, and Quest in advanced analytic roles. Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.