Written By: Chris Sorensen
Phrases in Analytics circles like “self-service” or “central version of the truth” have been imagined for many years.
How wonderful it would be if our business users could serve themselves, when and how they want from that central source of relevant and reliable data.
Perhaps more realistically, the silent whispers of “here we go again” can be heard under the breath of those that heard that promise one too many times.
As an industry, we have been working towards this goal as far back as I can remember and for the most part, it has never lived up to the expectations. Having said that, I do believe that we are closer than ever to achieving this, and in fact, the concept is starting to gain serious momentum.
Sections
- What exactly is self-service?
- Pros and cons of self-service
- How to overcome self-service challenges
- Benefits of Microsoft Power BI
What exactly is self-service?
At its core, self-service has been about self-sufficiency. There has always been a preference for business users being able to do more on their own, without assistance from IT but this model poses challenges.
The key here is more autonomy, but the question is how much more.
Self-service analytics provides a heightened level of access to data and analysis for non-technical employees. The focus is on removing as many barriers to access as possible.
Instead of spending time filling out IT tickets and waiting for a data scientist or the IT department, employees can access data directly to analyze trends and forecasts. It is becoming more popular in industries across the board, from marketing to finance to human resources.
Gartner states that self-service is often characterized by:
- Simple-to-use BI tools with basic analytic capabilities.
- A simplified underlying data model.
- Straightforward data access.
As you will see below, simple-to-use BI tools with basic analytic capabilities will fall under the dimensions of People and Technology and both simplified Data Model and Access fall under Data and Process.
We are seeing a huge surge in the use of self-service analytics as more businesses are realizing the power that this easy access to data can bring. In 2020, 62% of businesses said that self-service business intelligence is essential for ongoing initiatives and projects.
Additionally, self-service is considered the sixth most important technology and initiative strategic to Business Intelligence (reporting, dashboards, and data integration made the top three).
Pros and Cons of Self-Service Analytics
Easing access to data and training users to take initiative with data can unlock a wide variety of business benefits.
Companies that have adopted a solid self-service strategy are seeing that employees can make accurate and timely predictions uncover issues much more quickly (sometimes before they even become an issue), and leadership can focus on value-added activities.
Here are some of the most common benefits of self-service:
- Independence: BI and IT teams no longer have to create queries, dashboards, and reports. This frees up time for higher-value activities, which can also lower costs.
- Speed: With self-service options, time is less of an issue as teams have access to the data they need in real-time. This access allows them to quickly analyze relevant data and make informed decisions. This ability also relieves bottlenecks that can result from dependence on BI professionals.
- Agility: Business processes are continually improved to provide the most effective and accurate tools and data to maintain competitive advantages.
There are also a few challenges to self-service initiatives:
- Inconsistency: Self-service initiatives are only as good as the quality of an organization’s data. It is key to define terms, business, rules, and other information to ensure consistency throughout every stage in the data cycle.
- Duplication in Effort: Without adequate training and consistent policies, there is a risk of employees working with different versions of data to generate conflicting reports.
- Lack of adoption: Like the quality of the data itself can vary, so can the skills of users. The better training users are, the more likely they will be to adopt new processes. It is also important that everyone understand the tools and processes necessary to maintain utilize accurate data.
How to Overcome Self-Service Analytics Challenges
Many of the challenges surrounding a self-service implementation are due to a siloed approach. For self-service to work, a comprehensive approach is needed.
At Iteration Insights, we promote data-driven culture through the dimensions of People, Process, Technology, and Data across data production and consumption.
The figure below illustrates the life cycle of managing a business process and layers in the interaction of people, processes, technology, and data.
People
To determine this, the first step is to Know Thy Customer. In Wayne Eckerson’s blog series on How to Succeed with Self-Service Analytics, he describes that the dirty little secret of self-service analytics is that one size doesn’t fit all.
Eckerson Group’s User Classification Scheme
We have all seen it. Some users are better at serving themselves than others, and largely this has to do with their skills, position, and role in the company.
In his classification, you will find that the overwhelming majority of users in an organization are not self-service capable.
However, in recent years, a growing number of what he refers to as Data Explorers have been moving into the ranks of the Data Analyst, bringing with them superior data literacy and tech-savviness.
For self-service to succeed in an organization, the focus should be to acquire and develop a data literate team.
Process
Getting access to quality data has always been a challenge. The process of finding data, getting permission to access, understanding, using, and finally surfacing insights has always been a highly inefficient process.
To make matters worse, many people often repeat the same process over and over across departments, whilst all producing conflicting numbers and terms. A whole lot of effort goes into producing conflicting information.
Have you ever wondered why it takes so long to get the numbers or analysis that you want? Peel the onion back a little and you are likely to find a highly inefficient supporting process.
In the diagram below, we ideally want the end-user to spend more time in the Analyze and Present functions and little to no time in the others. Will you get there right away? Not likely but this serves as a guiding star.
To enable users to spend more time in the Analyze and Present functions, they must be supported by:
- A simplified underlying data model as mentioned above
- Straightforward data access
- Center of Excellence model that assists users and fosters organizational cooperation between the business and IT
Technology
The technology supporting self-service has frankly always been sub-par until the latest generation of self-service tools like Power BI and Tableau.
For years users were left to use Excel to manage entire business processes. The classic “when you have a hammer, everything looks like a nail” situation.
Or even worse, companies often provided users with very expensive enterprise BI tools billed as “self-service tools” that were slow and difficult to use.
These tools often turned into overpriced data pipelines to the ever-popular “export to Excel” functionality that was built into most.
With modern business-friendly technologies, users can use tools to their strengths and avoid their weaknesses. For example, Power BI is best for advanced visualizations and reporting, and Excel for business planning and ad-hoc analysis.
Self-service analytics is flourishing now that the proper tools available to organizations.
It is now up to the organizations to ensure that the proper governance is in place and training is provided so that users can effectively apply these tools. Without this, users will default to what is familiar and the tools will not be used appropriately.
Data
Our good friend data. Without good quality data, users will not be able to accomplish with they need on their own.
The good news is that the data is getting better. More organizations are realizing the necessity of accurate data, and they’re willing to invest in it.
Many companies are sitting on massive amounts of data that they use to improve their services and products.
While the inaccurate, outdated data processes of the past weren’t able to effect change in a company, today’s data is used to make accurate predictions based on patterns.
Modern data management processes synthesize all the disparate information into a unified system that lets users analyze and forecast trends.
To build a solid data foundation, the following 6 dimensions of data quality are essential:
- Accuracy
- Completeness
- Consistency
- Timeliness
- Uniqueness
- Validity
Benefits of Microsoft Power BI
The first step to realizing the self-service analytics dream is choosing the best technology for your business. It is imperative to think critically about your business needs, and how various technology options can meet those needs.
Microsoft Power BI is a leader in self-service analytics. It offers impressive data ingestion and preparation without sacrificing usability. Its mobile offerings coupled with compatibility with other Microsoft products make it the program of choice for most businesses that already use the Microsoft stack.
But don’t just take our word for it. Here are some Power BI fast facts:
- Gartner has ranked Microsoft as a leader in analytics and business intelligence platforms for 14 consecutive years.
- Power BI received the highest rating among PC Mag’s best self-service business intelligence tools.
Here are some of the benefits of Microsoft Power BI:
- Easy to use
- Quality data ingestion and preparation
- Highly interactive and navigable reporting
- Excellent sharing and collaboration
- Secure
- Mobile ready
Conclusion
Vendors have been selling the self-service dream for a long time. Usually, the part of the dream that they are selling is the technology with no consideration of your people, process, or data. They just want the tool in and to be sold. You get to figure out the rest. By dedicating ample time and resources to navigating your data journey, you can ensure it is a successful one.
