The Definitive Guide to Self-Service BI & Analytics

Devon Tackels

Content Marketing Manager, Sigma

The world is moving to the cloud. If you’re like most companies today, you’re using a variety of cloud-based products to run your business — such as a CRM, marketing platform, document storage, or HR platform. You probably also have data stored in the cloud. But most companies are just now beginning to realize the true potential that the cloud offers. Thanks to advancements in cloud data warehousing and self-service business intelligence (BI) and analytics solutions, the cloud is a realm where data is a democratized asset. Anyone in a company can ask questions of the data and find valuable insights to inform decisions.

The cloud allows decision-makers to glean the information they need when they need it. But only about 25% of a company’s employees (“power users” with BI skills) currently have access to all the data they need for smart decision-making. Although domain experts without BI experience may have access to a dashboard, they typically still rely on IT or business analysts for the deeper insights that lie beyond surface-level reports.

Failure to take advantage of the data-related capabilities of the cloud is a mistake, however. Limiting access to a select few means that decisions are being made in the dark or must get delayed until data becomes available. If you’re still operating according to a pre-cloud paradigm but have simply moved to the cloud, you’re missing out on a competitive advantage.

In this guide, we’ll explore the competitive advantage that cloud-based, self-service BI and analytics offers, look at the many benefits, and outline several best practices to keep in mind as you move toward becoming a truly data-driven organization.

What is self-service BI and analytics, exactly?

Since self-service BI and analytics often gets misunderstood, let’s define what it is. The term describes tools that give business experts (like marketing VPs, sales managers, and customer service directors) the ability to find and analyze data — without having to rely on IT professionals or dedicated data analysts to create reports.

But while these tools have promised democratized access, the reality has been quite a bit different for companies using most of them. Domain experts are often limited to playing in a defined sandbox (with the ability to view only certain metrics via a dashboard). If the user wants to dig deeper or ask followup questions of the data, they must go back to their data engineer or SQL analyst. Because these folks are busy, it could take hours or even days to get the data the user needs.

But the landscape is starting to change as a result of decision-makers demanding timely access to the data they need to do their jobs well — and as companies are beginning to realize the cost of delayed or insufficient data. New tools like Sigma enable true self-service analytics. So self-service BI and analytics is on track to deliver its initial promise.


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Benefits of self-service BI & analytics

Developing a true self-service BI and analytics framework delivers a competitive advantage in a variety of ways. There are many benefits that go beyond the scope of this guide, but here are five of the most significant.

 Allows everyone to explore data

Rather than relying on a limited-view report or dashboard, the new model of self-service BI lets decision-makers get curious about the “why” behind surface-level metrics. Business teams can go beyond consuming a limited data set to uncover causes and significant correlations revealed by the data. Often, these insights are the ones that power a company’s increased profitability.

 Provides immediate answers

Additionally, for many queries, teams can explore data in real-time. Armed with data skills alone (no need to know SQL or be a data engineer), users can find answers to pressing questions. These answers can be found quickly, without waiting hours or days for a BI expert to deliver a report.

 Enables better, faster decision-making

Democratized, immediate access allows decision-makers to gather the insights they need to make smarter decisions. It also enables them to take advantage of opportunities that won’t wait. Companies can be agile, with the confidence that they’re basing decisions on accurate data.

 Frees up IT teams to work on other projects

Nearly every department in a company moves quickly. Opportunities present themselves; prospects need solutions now, and customers demand action. Decision-makers require ad-hoc reporting — they need information promptly. Ad-hoc reporting is a necessity, but without true self-service analytics, it results in data/BI teams living in “report factory hell” as they rush to deliver one report after another. When decision-makers can dive deep into the data on their own, IT staff are freed to work on other important projects.

 Enables collaboration

While departments often operate in silos, your business doesn’t. The customer journey is just that, a flow of experiences that are all connected. When marketing, sales, operations, and customer service fail to collaborate, insights are missed—and problems go unsolved. True self-service BI and analytics give every team member the power to get curious and work together to improve the customer experience and operational efficiency. Self-service builds stronger relationships between departments and people as they collaborate to solve problems.

Because most companies are not yet using true self-service (they’re instead using tools that offer only a limited-view dashboard or require IT or data analysts to provide deeper insights), these benefits provide a significant competitive advantage. With true self-service BI, you can move faster, more strategically, and build a stronger team at the same time, leaving your competitors in the rearview mirror.


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Self-service BI and analytics pros and cons


  • Insights scale as the need for them scales — As a company grows, the demand for insights grows. Self-service BI and analytics are easily scalable, unlike reliance on data analysts and other BI professionals.
  • Companies become more agile — When a company can make faster decisions based on reliable data, they can take action quickly. Teams are empowered to move forward.
  • Priorities reveal themselves — When IT gets inundated with multiple requests for reports and dashboards, everything is labeled “urgent.” With self-service analytics, decision-makers can prioritize which information they need, and when.


  • Self-service still requires a data skillset — Without a strong understanding of how to work with data, people can make mistakes. Complex problems require knowing what KPIs to track, what to query and how, and how to identify correlation vs. causation. But business experts can learn these skills. They are naturally curious, and companies need to help nurture that by addressing the skills gap and working to increase data literacy. The 2018 Gartner Annual Chief Data Officer Survey discovered that 80% of organizations plan to initiate deliberate competency development in the field of data literacy in 2020.
  • Self-service still requires training — While self-service is easy to implement, business experts need training on how the data warehouse is organized, what tables are most relevant to them, and where they can find reliable data that will provide accurate insights. Again, this issue can be solved with instruction from the data team.
  • IT remains involved — Most self-service tools aren’t genuinely self-service. As we explored in the introduction, your tool may require IT to get involved at a significant level. Other tools, like Sigma, dramatically reduce the need for IT staff involvement. The benefits of self-service are dependent upon which solution you’re using.

Self-service BI & analytics best practices

The cons described above make self-service controversial in many companies. The risk of opening up access seems to be high. What if people fail to use the necessary data logic for an accurate answer? What if they end up relying on incomplete data without realizing it? And what happens when the BI professionals no longer have sovereignty over the realm that they are, arguably, best-equipped to handle?

These are legitimate concerns. But you don’t have to choose between an Ivory Tower or Wild West Scenario. To mitigate the cons, you’ll need to implement self-service analytics best practices. These best practices will help limit risk and ensure that you’re not compromising security or accuracy in the process.

Choose a provider with proven security

No one needs to be told how important security is in the modern world. We see reports daily of companies experiencing the negative consequences of data breaches. And while no one can one-hundred percent guarantee data security, there are steps you can take to dramatically reduce vulnerability when democratizing access to your cloud-based data.

To reduce risk as much as possible, you must look at security from a variety of levels. Much of cloud security is focused on infrastructure (i.e. Amazon Web Services, Microsoft Azure). Infrastructure security is vitally important, but it’s not enough. What about application security at the PaaS level? What about SaaS security?

You have a decent amount of control over your infrastructure and application security. You can make sure virtual machines stay partitioned, implement access controls, use built-in security features, address API security, and implement additional tools to monitor and bolster security. But you won’t have much control over security at the SaaS level. The nature of SaaS tools is that the providers take on responsibility.

For this reason, you must choose SaaS providers with proven security. Your providers should be encrypting data, monitoring activity, and implementing processes and procedures for continual protection. Look for vendors that are SOC 2 compliant since compliance means they’ve demonstrated a commitment to secure operations and are actively proving it on an ongoing basis. This includes your self-service analytics tool.


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Work toward an open data culture

Traditionally, data has been the purview of data analysts and BI professionals. The idea that data should remain accessible only to professionals has been passed down in the industry for years. And it makes sense on some level. We wouldn’t allow the financial department of a hospital to have access to the surgeons’ tools armed only with the mandate to use them wisely — it wouldn’t be safe, even though it would eliminate the need for patients to wait for surgery! Self-service is viewed similarly by many admins, and providing access to data without the proper training and experience gets labeled as dangerous.

But today’s technology provides a safety net for business users, and they can learn to use the technology. While domain experts will want to consult BI professionals for particularly complex problems, the fact is that these business users were hired for a reason — they know their domain and the data associated with it better than anyone. By limiting them to a dashboard, you waste their domain expertise. At the same time, democratizing access means that BI professionals can focus their expertise where it truly matters. No more wasting knowledge on simpler reports that could be handled by business teams. Ultimately, this makes BI professionals more valuable to their employers, reduces frustrations, and boosts their careers.

It may take time to build an open data culture. Still,companies should work toward defining and making a case for how such a culture will benefit each member of the team, including data analysts and BI professionals.

Develop an onboarding process

A self-service BI strategy won’t be successful without a thoughtful onboarding process that equips business teams with the skills and training they need to thrive. At the same time, developing a robust onboarding process will help to alleviate the concerns of those who fear opening up the data warehouse.

Teach business teams how the data warehouse is organized, where they can find data “endorsed” by trusted data teams, what tables will be most relevant to their inquiry, and how to query data. As you’re developing your onboarding process, identify each skill that employees will need to uncover relevant, accurate answers to the questions they’ll be asking, and create training to build competency. While self-service onboarding will take time, it is infinitely faster than gaining a degree in data analytics and learning SQL.

Create a flexible self-service BI governance strategy

A company’s view of governance will be a reflection of its data culture. While a weak governance strategy will result in irrelevant data at best and create a serious risk of breaches of regulation at worst, you should be just as cautious of a too-strict governance strategy. Without flexibility, people won’t be able to get the data they need even with self-service, and it won’t be scalable. Here are a few tips to help you as you create a governance strategy.

  • Iterate the role of IT — IT’s role will shift when you implement self-service BI and analytics. Rather than providing report on-demand, their expertise will be utilized where it’s most useful: working through complex data problems, advising and teaching data skills, etc.
  • Aim for ongoing training — Business users, by definition, aren’t as experienced in working with data analytics tools outside of Excel. It’s not part of their daily workflow (although this may change as you create a culture of curiosity around data and encourage your people to ask smart questions and seek answers!). For this reason, it’s a good idea to create a system for ongoing training. This training can be provided by IT or delivered in a video course format. Companies like Sigma even provide robust documentation and training videos to ease the burden. There are many options for training — what’s important is that you offer it on an ongoing basis.
  • Address unvetted data lakes — Business users may need access to a variety of data sources, including data lakes or data marts that may not be currently endorsed by data teams. You’ll need to develop procedures and policies around data lakes and other unvetted data sources.
  • Recognize the balance between standardization and speed — There’s an inverse relationship between standardization and speed. In the quest to ensure complete standardization, you’ll sacrifice your self-service users’ ability to get answers quickly. You’ll need to evaluate the trade-offs and decide what’s the right balance for your company.

Claim your competitive advantage with self-service BI and analytics

It’s rare to find a tool that can deliver a significant competitive advantage. But self-service BI and analytics has demonstrated that it’s an underused vehicle for better decision-making that’s more agile at the same time. Why aren’t more companies taking advantage of these powerful tools?

There are some risks involved in democratizing data. But risks are inherent in any worthwhile endeavor. Fortunately, these risks can be minimized by implementing best practices and a flexible data governance strategy that will also provide guardrails. Ultimately, the companies that dare to lead the way in self-service analytics will be the ones that pull ahead in their industries.

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