{"id":495,"date":"2024-01-01T12:27:13","date_gmt":"2024-01-01T12:27:13","guid":{"rendered":"https:\/\/solutionsreview.com\/expert\/?p=495"},"modified":"2024-02-06T13:58:50","modified_gmt":"2024-02-06T13:58:50","slug":"how-to-succeed-with-self-service-analytics-part-1-the-pitfalls-and-paradoxes-of-self-service-analytics","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/how-to-succeed-with-self-service-analytics-part-1-the-pitfalls-and-paradoxes-of-self-service-analytics\/","title":{"rendered":"How to Succeed with Self-Service Analytics Part 1: The Pitfalls and Paradoxes of Self-Service Analytics"},"content":{"rendered":"

Self-service analytics is harder than it seems. There are many unseen landmines and pitfalls, as well as paradoxes worth understanding. This chapter exposes issues that can undermine a well-meaning self-service strategy.<\/span><\/p>\n

Self-Service Pitfalls<\/span><\/strong><\/h2>\n

Self-service analytics seems like a win-win proposition for business and IT, but it can quickly go awry without sufficient governance.<\/span><\/p>\n

Here\u2019s what happens: Newly empowered data analysts create a multitude of conflicting reports and dashboards with unique business logic and data. The data doesn\u2019t add up when shared, and business users no longer know which report to use. Executives get frustrated when they receive conflicting answers to simple questions like, \u201cHow many customers do we have?\u201d<\/span><\/p>\n

At organizations awash in conflicting reports, data analysts refuse to trust any except those they create themselves. Also, business users revert to requesting custom reports from IT, or worse, they stop using data altogether to make decisions. Instead of liberating business users, ungoverned self-service analytics creates data chaos\u2014a proverbial Tower of Babel where people talk but few communicate.<\/span><\/p>\n

Rather than use the term \u201cself-service analytics,\u201d we need to start talking about \u201cgoverned self-service.\u201d Proper governance balances user empowerment with standards to ensure data and business alignment\u2014 something every organization (and ultimately every individual) wants and needs.<\/p>\n

Self-Service Paradoxes<\/span><\/strong><\/h2>\n

Governed self-service analytics reconciles the polar forces pitting freedom against control. This dynamic plays out in many areas of business and technology: speed versus standards; innovation versus operations; agility versus architecture; and departmental needs versus corporate interests.<\/p>\n

Organizations need to balance these dueling forces to deliver an optimal self-service environment for user empowerment (selfservice) and business controls (governance). You can\u2019t have one side of the equation without the other; you need both to counterbalance the excesses of each. Just as self-service without governance creates data chaos, governance without self-service creates a bureaucratic and oppressive operating environment.<\/p>\n

This tension between freedom and control creates interesting paradoxes that companies experience when trying to implement self-service analytics:<\/p>\n

Paradox 1: Self-Service Requires Standards<\/span><\/strong><\/h2>\n

At first glance, self-service allows users to fulfill their own data analytics needs any way they want. But the opposite is true. Without standards, business users have to work harder and longer to get what they want with greater risk of failure.<\/p>\n

Without standards, business users have to work harder and longer to get what they want with greater risk of failure.<\/em><\/p>\n

For example, we all take for granted that we can hop in our cars, drive to the grocery store, and return safely, taking the most direct and efficient route possible. We think we have autonomy and total freedom, but we don\u2019t. The illusion of freedom is created by our conscious (or unconscious) adherence to a multitude of rules, regulations, and standards that govern such a trip: We drive on the right side of the double yellow lines; we stop at red lights and slow down at yellow ones; we obey the speed limit (mostly); we get a driver\u2019s license, and we register the car. Without these and hundreds of other rules and regulations, the roads would be chaotic and dangerous, and we\u2019d risk our lives during every trip to the grocery store.<\/p>\n

In the same way, self-service analytics requires a multitude of standards or it devolves into data chaos. These standards include how to define and calculate metrics, how to store and access data, how to query and publish reports, and how to govern individual privacy and security. Standards are the foundation upon which self-service analytics is built.<\/p>\n

Standards are the foundation upon which self-service analytics is built.<\/em><\/p>\n

Paradox 2: Self-Service Requires Tailoring<\/span><\/strong><\/h2>\n

Self-service means different things to different people. An old school executive thinks self-service involves viewing a report online rather than on paper; a business manager believes it is drilling into a dashboard and saving the view as a \u201creport\u201d that they can view next time with fresh data; a data analyst thinks it\u2019s the ability to find, combine, analyze, and visualize data from any source. And each is right.<\/p>\n

For business intelligence (BI) teams, this places a premium on classifying business users by their information appetites and then providing relevant business views, analytic functionality, training, support, and facilitation. It also means tailoring permissions so users only see the data, metrics, and analytic functionality relevant to their jobs. Permissions maintain the visual and analytical palette by giving users only what they need to do their jobs quickly and effectively.<\/p>\n

Permissions maintain the visual and analytical palette by giving users only what they need to do their jobs quickly and effectively.<\/em><\/p>\n

Paradox 3: Self-Service Requires Hand Holding<\/span><\/strong><\/h2>\n

Self-service implies that a task is easy enough for almost anyone to do without assistance, like pumping your gas at the service station. But becoming self-sufficient takes time, even with easy things. This is especially true for complex tasks, such as creating custom dashboards and analytic views.<\/p>\n

Most BI leaders schedule one-on-one meetings to help business executives become proficient with analytic tools and applications. This is expected for top executives, but it\u2019s equally true for hard-core data analysts. Just because someone has a \u201cdata analyst\u201d title doesn\u2019t mean they know how to use a self-service tool to explore the organization\u2019s data and generate insights that matter. Most business people, data analysts included, need a little help to understand the nuances of the company\u2019s data, its visual design standards, and how to use a new tool.<\/p>\n

Since a BI leader can\u2019t provide one-on-one assistance to everyone in the organization, other BI team members need to chip in. BI \u201crelationship managers\u201d need to schedule one-on-one time with business unit leaders and managers to get them comfortable with analytic tools and output. BI developers, if not embedded in the business, should make themselves available to data analysts and data scientists via office hours or data labs, where analysts can work through problems with specific tools and data sets.<\/p>\n

Paradox 4: Self-Service Requires More Collaboration with IT, Not Less\u00a0<\/span><\/strong><\/h2>\n

Business people love the idea of self-service analytics because it promises to liberate them from the shackles of the IT department and its perpetual backlog. With self-service, business users can create their reports and dashboards without having to wait for the IT department. But the reality is that self-service requires the business to spend more time with IT, not less.<\/p>\n

Self-service analytics doesn\u2019t work unless the business steps up and owns the data analytics program.<\/em><\/p>\n

Self-service analytics doesn\u2019t work unless the business steps up and owns the data analytics program. Ownership takes many forms: a data analytics council that reviews and approves strategy and tactics; a data governance process that defines, documents, and manages key data elements; a data quality process that monitors core data elements for defects; a report governance process that certifies reports and the analysts who create them; a permission structure that defines who can access which data; and an agile process that prioritizes projects and capabilities for BI developers.<\/p>\n

In essence, the business sets the rules and standards that the IT team implements. If the business doesn\u2019t assume ownership and responsibility for data analytics resources, processes, and strategies, then it will never enjoy a robust data analytics infrastructure with easy access to clean, integrated, timely, and relevant data. The business needs to work hand-in-hand with the IT department to make self-service analytics work.<\/p>\n

Paradox 5: The Need for Self-Service is Inversely Proportional to the Quality of Core Business Dashboards<\/span><\/strong><\/h2>\n

Although businesspeople clamor for self-service analytics, most don\u2019t really want it. How can that be? Data is so locked up in many organizations that business people spend nights and weekends finding data and stitching it together using Excel or Tableau to create required quarterly or annual reports for business leaders.<\/p>\n

Not surprisingly, these individuals are desperate for self-service tools that can streamline this process. Yet, if they didn\u2019t have to create the reports at all, they would be ecstatic. They have better things to do than spend their precious free time creating business views of data. Their job is to lead and make decisions, not slice, dice, and prepare data like a data analyst.<\/p>\n

What business managers want is a well-designed, interactive report or dashboard that generates the views they want and can be easily modified. They don\u2019t want to create reports from scratch; they simply want the BI team to understand their needs and create pre-ordained business views that largely align with their needs. Ideally, these dashboards are predictive, showing business people performance to plan and forecast and suggesting next steps.<\/p>\n

As a rule of thumb, each business function needs one or more well-designed, interactive dashboards that address 80% of managers\u2019 and workers\u2019 typical questions. Since these dashboards can be complex, the IT department often creates them, but skilled departmental analysts and report developers can design and build these dashboards as well. If the IT department has limited resources, a self-service initiative might be the only way these standard dashboards get created.<\/p>\n

As a rule of thumb, each business function needs one or more well-designed, interactive dashboards that address 80% of managers\u2019 and workers\u2019 typical questions.<\/em><\/p>\n

The point is that organizations should spend more time implementing robust departmental dashboards instead of pursuing a self-service strategy for its own sake. A well-designed departmental dashboard will obviate much of the demand for self-service.<\/p>\n

Summary<\/span><\/strong><\/h2>\n

Self-service analytics seems like a win-win. The business gets what it wants, how and when it wants it, and the IT department gets to focus on value-added tasks. The reality is that self-service analytics can go awry quickly, creating data chaos with hundreds of conflicting reports and dashboards.<\/p>\n

To avoid data chaos, organizations need to implement \u201cgoverned self-service\u201d where business and IT collaborate to establish standards that define data elements and the proper use of reports and dashboards, establish processes for ensuring data quality, and develop controls for accessing and safeguarding data, among other things.<\/p>\n

The next chapter discusses how to classify business users so you can apply the right level of self-service access and functionality to meet their needs.<\/p>\n","protected":false},"excerpt":{"rendered":"

Self-service analytics is harder than it seems. There are many unseen landmines and pitfalls, as well as paradoxes worth understanding. This chapter exposes issues that can undermine a well-meaning self-service strategy. Self-Service Pitfalls Self-service analytics seems like a win-win proposition for business and IT, but it can quickly go awry without sufficient governance. Here\u2019s what […]<\/p>\n","protected":false},"author":299,"featured_media":650,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[10],"tags":[],"acf":[],"yoast_head":"\nThe Pitfalls and Paradoxes of Self-Service Analytics<\/title>\n<meta name=\"description\" content=\"Wayne Eckerson outlines the many unseen self-service analytics pitfalls, as well as paradoxes worth understanding.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Pitfalls and Paradoxes of Self-Service Analytics\" \/>\n<meta property=\"og:description\" content=\"Wayne Eckerson outlines the many unseen self-service analytics pitfalls, as well as paradoxes worth understanding.\" \/>\n<meta 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