Self-Service Analytics: A New Approach to Modern Data Management

Making sure that data is available when and where it is needed is imperative for teams working in fast-changing environments. Traditional Business Intelligence (BI) solutions, while powerful, are often too slow in producing the required insights for those who need them, now. The solution? Self-Service Analytics. Read on, to understand what self-service analytics is, why it could be the right solution for your organization, and how to go about successfully implementing it.

"Agile organizations have a 70% chance of being a top quartile performer." – McKinsey

As previously discussed, an end-to-end system will cover data collection, storage, and processing. Processing ultimately leads to analytical outputs that facilitate decision-making. In traditional systems, that last step is put together by Business Intelligence (BI) engineers, usually sitting in the IT or data department, responding to the needs of colleagues in operations, risk management, security management, etc. They act as the bridge between their colleagues and the data, delivering analytical products that drive decision-making. 

These roles are in high demand. There are about 1 million such people listed on LinkedIn alone (a quarter of which are in the US) and clearly, it’s not enough. A report by McKinsey hinted at critical shortages in that area in the US. With an average salary for such roles at $102k (Glassdoor), we could estimate that this "bridge" costs more than $25 billion per year to US businesses alone.

There are a few key problems with this. 

• The first is cost, not every organization has the budget to sustain such roles internally, especially in emerging or frontier markets. 
• The second is time, having BI middlemen can cause delays in getting the insights needed and those delays can hurt the bottom line or affect the ability of the organization to respond to crises and minimize harm to staff, assets, and reputation. 
• The third is communication. BI engineers must understand their internal client’s needs to deliver an appropriate solution. Engineers servicing different areas will have to learn the lingo, understand use cases, engage in back and forth to ensure “product-market fit” and that takes time, and time comes at a cost. The third problem, therefore, reinforces the first and second problems in a never-ending loop.

WHAT ARE SELF-SERVICE ANALYTICS?

Self-service Analytics, or Self-service Business Intelligence (BI), is often touted as the answer to achieve agile analytics. While this might be the case, understanding what these concepts mean and how to achieve them should be your first task before deciding to go with an analytics solution.

According to Alan Duncan, research director at Gartner, analytic agility "is the ability for business intelligence and analytics to be fast, responsive, flexible and adaptable."

Gartner defines Self-Service Analytics as a "form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support."

Under Self-Service Analytics, non-technical users or business users are running the show, by using No-Code development.

No-code empowers non-technical users to design and manage data solutions that meet their needs, allowing them to build analytical products that serve their own needs, cutting away the middleman, removing steps in the supply chain, and accelerating their ability to get the insights they need when they need them.

BENEFITS OF SELF-SERVICE ANALYTICS

Investing in Self-Service Analytics not only mitigates the problems listed above, it also unlocks a variety of benefits that can be felt across the organization:

1. Reducing barriers to entry and fostering a data-driven culture. The more people are asking data-driven questions, the more opportunities it creates for positive change in the organization.
2. Single source of truth. Collaborating on a self-service analytics platform facilitates alignment in not only metrics monitoring but also organizational lingo. It becomes a one-stop-shop for understanding the status quo and debating scenarios.
3. Freeing up technical resources. Engineers can move from repetitive, short-term tasks (data processing and report generation) to high-value, longer-term projects with a better return on investment.

SELF-SERVICE ANALYTICS CHALLENGES

However, giving BI ownership to more stakeholders inside the organization raises some challenges that need to be carefully considered.

Ease of use

Removing advanced software and data processing knowledge requirements doesn’t automatically make a self-service analytics platform easy to use. Some “self-service” platforms like Microsoft PowerBI require a significant ramp-up period (some say 30 days) to be proficient. That’s not something most users can afford. It’s important to invest in platforms that are fast to onboard non-technical users, not more than a few hours, otherwise, the significant time investment will prevent the majority of users from participating and this will hurt any aspiration to forming a data-driven culture inside the organization, limiting analytics to only a selected few again.

Data governance

Centralizing data on a shared analytics platform brings many benefits but it also comes at a cost. Security and data governance are of utmost importance here. It’s therefore important to look for systems that facilitate the slicing of data so that only users who should have access to different datasets do. Setting permissions at the dashboard level is not enough. Organizations that are serious about information security should set create, read, update, delete (CRUD) permissions as deep as possible to ensure maximum protection and prevent unwarranted access, leaks (whether accidental or intentional), and other insider threats. 

NECESSARY ELEMENTS TO SELECT A SELF-SERVICE ANALYTICS TOOL

Considering all the above, organizations considering investing in a tool that enables Self-Service Analytics, should look to check these boxes:

• Gives the right people the right set of tools and capabilities
• Can accommodate all internal user roles and skill levels
• Easy-to-use and intuitive
• Focuses on the needs of end-users and business users
• Creates a collaborative environment
• Provides both control and governance over data
  
  

HOW DO YOU GET THERE?

Self-Service Analytics is a critical piece not only of agile analytics but also of agile data management, the end step towards decision-making insights. After all, any data collected that cannot be easily monitored and analyzed by business users is a wasted investment. 

As we open up analytics to more people within the organization it’s important not to lose sight of the big picture. Simplicity is key to avoid analysis paralysis and ensure everyone can truly benefit and collaboratively participate in creating data-driven solutions. Furthermore, data governance and security cannot be overlooked, especially in sensitive operating environments.

If you are interested in exploring how easy and secure Self-Service Analytics can help your operations and your team, get in touch with me or my team using the button below.