DATA ANALYTICS

4 Self-Service Analytics & Business Intelligence Best Practices to Limit Risk

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Rachel Serpa

Director of Content Marketing, Sigma

Self-service BI and analytics has a lot to offer: faster, deeper insights to inform important decisions, without overburdening IT or data teams with limited capacity. What’s not to love? As many benefits as self-service tools deliver, there is risk involved.

Without knowing how to ask the right questions that will lead to the answers they’re looking for, business users may end up with inaccurate or irrelevant information. They may not seek enough data to form a full picture, leading them to misunderstand what the data actually reveals. And security is a constant concern — is opening up access to data warehouses and data lakes even safe?

The risks are real, but implementing four self-service BI and analytics best practices will help significantly limit those risks.

 Choose providers with proven security

Security is a primary concern for every data team, so let’s start there. The architecture of your systems will dictate the types of security you must have in place. But regardless of your specific situation, you’ll need adequate security at every level: IaaS, PaaS, and SaaS. At the infrastructure and application levels, there are several steps to take: make sure virtual machines remain partitioned, implement access controls and permissions, use any available built-in security features, and address API security. Then look for weaknesses and implement additional tools to monitor and bolster security in these areas.

But at the SaaS level, you have little to no control over security — you must rely on your providers’ security. For this reason, you’ll need to learn what security measures your self-service analytics provider has implemented and ask questions about ongoing security monitoring. Here are three specific things to look for.

SOC 2 compliance

SOC 2 is a voluntary program that providers who are serious about security comply with. This program involves audits over time, so providers must prove on an ongoing basis that they’re meeting the security policies and procedures they’ve committed to.

CSA compliance

The Cloud Security Alliance (CSA) offers a Consensus Assessments Initiative Questionnaire (CAIQ) that is designed to help evaluate compliance with the CSA’s Cloud Controls Matrix and best practices.

Compliance with data privacy regulations like GDPR and CCPA

Your provider should be compliant with any data privacy regulations that you must meet at the federal or state level, including industry regulations like GDPR, and CCPA. They should demonstrate and certify compliance.

LEARN MORE

Want to learn more about Sigma’s commitment to security? See the details of how we ensure data security and privacy.

 Develop an onboarding process

Business users aren’t data engineers, so they will need to be equipped with the skills and training to be successful with self-service analytics tools. They need to know how the data warehouse is organized, where they can find reliable data, what tables will be most relevant to their inquiry, and how to effectively query data so they can find the right answers. You may also want to consider providing access to an online training course in data fundamentals for those who are less experienced analyzing data, or wish to increase their data literacy. 

To create your onboarding process, identify the skill sets and knowledge that will be necessary for business users to thrive, and build training around each area. Second to security, your onboarding process will be your best defense against business users inadvertently accessing unreliable data or coming up with irrelevant answers due to lack of competency with data skills.

 Create a flexible self-service BI governance strategy

Self-service BI governance is essential to mitigating risk. But there’s a balance between standardization and flexibility. You absolutely need a governance strategy in place that will ensure compliance with regulations and create procedures that create data skill competency. But if your strategy is too strict, you’ll end up with BI bottlenecks that hold people back and put you back where you started, with too much reliance on IT and the data team.

Instead, aim for a flexible governance strategy that puts IT and data teams at the helm, where you can leverage their expertise without tying down database admins and analysts day-to-day workflows. The role of data experts should be to focus on strategic projects like improving data access  and to provide assistance with complex inquiries that business users may not have been able to handle on their own.

Training is also essential and should be part of your governance strategy. By giving training the weight it deserves, you’ll make sure it’s actually happening, not existing simply as an aspirational project.

It’s also important to consider your policies and procedures. You’ll want to focus specifically on unvetted data sources. Who has access to these? How can they be used? Without parameters, it’s likely that people will end up using data that’s unreliable, leading to incorrect conclusions.

 Build an open data culture

Undergirding the first three best practices is the need for an open data culture. It’s no surprise that data experts will be wary of opening up their domain to business users who don’t have the same level of expertise. Building an open data culture with the following components will help.

  • Data is democratized and curiosity is encouraged. Your people must understand that the company’s goal is to facilitate better decision-making. Smart decisions can’t be made without solid data and answers to deeper questions. When your team understands the “why” behind the initiative, they will be more likely to get on board.
  • Reassure BI experts of their value and define roles. Data experts need to know that they are valued and that the company wants them to focus even more on their areas of value (rather than running ad-hoc reports all day). And some questions will require complex queries, where data experts will need to weigh in. Define the roles of each person in the organization so they understand what metrics they’re being measured against.
  • Take advantage of domain experts’ knowledge. Showcase what domain experts bring to the table: they know the impact the data can have on business and what questions to ask to help the company reach its targets in their areas of expertise. The IT or the data team can’t do this alone. It’s important that people pool their knowledge and viewpoints.
  • Empower domain experts to use technology with training. A strong data culture is based on training, knowledge sharing, and transparency. People must feel confident in their data skills before they will feel empowered to query data and run their own reports. This step can’t be emphasized enough.

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Start building a more open data culture today with Sigma. Sign up to get started for free. 

It’s important to acknowledge the risks involved in self-service. But avoiding risk altogether is impossible and will limit your ability to grow as a company. Implementing these self-service BI and analytics best practices gives you confidence that you’re limiting risk so you can experience the benefits that self-service offers without making your company vulnerable.

Have more questions about self-service business intelligence and analytics? Read our free definitive guide

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