Self-service analytics community is a group of people who share the common interest about self-service analytics and common value about data-driven decision-making culture.

Why people are motivated for the internal self-service community?

The self-service community motivations are as followings:

• Empowerment: Self-service stems from – and affects – a wider macro trend of DIY on one hand, and collaboration on the other hand: content builders are taking the lead for services they require, and often collaborate with others. The key is to offer the members empowerment and control over the process, so they can choose the level of services they would like to engage in, thus affecting the overall experience.
• Convenience: The benefit of community self-service is obvious – they can get fast access to the information they need without having to email or call IT or a contact center. According to Forrester, 78% of people prefer to get answers via a company’s website versus telephone or email.
• Engagement: It is their shared ideas, interests, professions that bring people together to form a community. The members join in because they wish to share, contribute and learn from one another. Some members contribute, while others benefit from the collective knowledge shared within the community. This engagement is amplified when members induce discussion and debate about tools, features, processes and services provided and any new products that are being introduced. The discussions within the community inform and familiarize people with the new and better ways of getting things done – the best practices.

How to start creating an internal user community?

When you start creating an internal user community, you need to keep in mind that a lot of community activities are completely dependent on intranet. So you need to ensure that the community is one that can be easily accessed by the maximum number of people. Below is the checklist:

• Determine a purpose or goal for it. One example: The place you find anything and everything about self-service analytics. Community is the place of sharing, learning, collaborating….
• Decide who your target audience will be. Most likely audience should be those content developers and future content developers. Mostly likely the audiences are not the server end users.
• Design the site keeping in mind the tools for interaction and the structure of your community.
• Decide upon the manner in which you will host the community.
• Create the community using tools available within your organization.
• Create interesting content for the community.
• Invite or attract members to join your community. Try to find out who has the developer licenses and send invitation to all of them.
• Administer it properly so that the community flourishes and expands. It is a good practice to have at least two volunteers as moderators who make sure to answer user’s questions timely and close out all open questions if possible.

Who are the community members?

The audiences are all the content builders or content developers from business and IT across organization. Of course, the governing body or council members are the cores of the community. It is a good practice that council members lead most if not all the community activities. The community audiences also include future potential content builders. Council should put some focuses to reach out to those potential content builders. The end information consumers, those who get dashboards or reports, are normally not parts of the community, as end information consumers really do not care too much tools, technology or processes associated with the self-service. All end information consumers care is the data, insights and actions.

What are the community activities?

The quick summary is in the below picture. More detailed will be discussed later on.

• Intranet: Your community home. It is the place for everything and everything about your self-service analytics. The tool, process, policies, best practices, system configuration, usage, data governance polices, server policies, publishing process, license purchasing process, tip, FAQ, etc.
• Training: The knowledge base at community intranet is good but is not good enough. Although most of the new self-service tools are designed for easy of use, they do have a few learning curves. Training has to be organized to better leverage the investment.
• User Meetings: User summit or regular best practice sharing is one must have community activity.
• License Model: When a lot of business super users have dashboard development tools, what is most cost effective license model for dashboard development tools? Do you want to charge back for the server usage?
• Support Process: Who support the dashboards developed by business super users? What is IT’s vs. business’ role in support end users?
• External Community: Most self-service software vendors have ver active local or virtual or industrial community. How to leverage external community? How to learn the best practices?

Key takeaway: Build a strong community is the critical piece for success self-service analytics deployment in enterprise.

Please next blogs for Multi-tendance strategy

When business super users are empowered to create discovery, data exploration, analysis, dashboard building and sharing dashboards to business teams for feedback, business is taking a lot of more responsibilities than what they used to do in traditional BI & analytics environment. One of the critical self-service analytics governance components is to create a clear roles and responsibilities framework between business and IT. This is one reason why the governing body must have stakeholders from both business and IT departments. The governing body should think holistically about analytics capabilities throughout their organization. For example they could use business analysts to explore the value and quality of a new data source and define data transformations before establishing broader governance rules.

A practical framework for the roles and responsibilities of self-server analytics is in following picture.

Business owns

• Departmental data sources and any new data sources which are not available in IT managed enterprise data warehouse
• Simple data preparation: Data joining, data blending, simple data transformation without heavy lifting ETL, data cleansing, etc.
• Content building: exploration, analysis, report and dashboard building by using departmental data or blending multiple data sources together
• Release or publishing: sharing the analysis, report or dashboard to information end consumers for feedback, business review, metrics, etc.
• User training and business process changes associated with the new reports & dashboard releases.

IT owns

• Server and platform management, licensing, vendor management, etc
• Enterprise data management and deliver certified, trustworthy data to business, build and manage data warehouse, etc
• Create and maintain data dictionary that will help business super users to navigate the data warehouse.
• Support business unit report developers by collaborating to build robust departmental dashboards and scorecards, converting ad hoc reports into production reports if necessary.
• Training business to user self-service analytics tools

It is a power shift from IT to business. Both IT and business leaders have to recognize this shift and be ready to support the new roles and responsibilities. What are the leader’s roles to support this shift?

• Create BI/Analytics Center of Excellence: Identify the players, create shared vision, facilitate hand-offs between IT and business
• Evangelize the value of self-service analytics: create a branding of self-service analytics and market it to drive the culture of analytics and data-driven decision-making culture; run internal data/analytics summit or conference to promote analytics
• Create a federated BI organization: manage steering committee or BI council, leverage BI& Data gurus in each organization, and encourage IT people to go from order takers to consultants.

Please read my next blogs for Community.

How to govern the enterprise self-service analytics? Who makes the decisions for the process and policies? Who enforces the decisions?

In the traditional model, governance is done centrally by IT since IT handles the entire data access, ETL and dashboard development activities. In the new self-service model, a lot of business super users are involved for the data access, data preparation and development activities. The traditional top down governance model will not work anymore. However no-governance will create chaos situation. What will be needed for self-service environment is the new bottom up governance approach.

In the new self-service analytics model, since super business users do most of dashboard development, the more effective governance structure is to include representatives of those super business users.

In the picture, the blue box in the middle is the self-service analytics governing body for enterprise. It consists of both business and IT team members. The self-service analytics governing body members are self-service analytics experts & stakeholders selected by each business unit. You can think of the governing body members are the representatives of their business units or representatives of the entire self-service analytics content builder community. The charter of this governing body is as followings:

• Define roles and responsibilities between business & IT
• Develop and share self-service best practices
• Define content release or publishing process
• Define analytics support process
• Define data access, data connections and data governance process
• Define self-moderating model
• Define dashboard performance best practices
• Helps on hiring and training new self-service analytics skills
• Communicate self-service process to entire self-service content builder community and management teams
• Enforce self-service analytics policies to protect the shared enterprise self-service environment
• Make sure that self-service process and policy alignment with enterprise process and policy around data governance, architecture, business objectives, etc

Should business or IT lead the governing body? While there are times when a business-led governing body can be more effective, do not discount an IT-led governing body. There are many good reasons to consider the IT-led governing body.

• IT understands how to safely and accurately exposes an organization’s data and can standardize how data is exposed to self-service super users.
• IT has a centralized view of all analytics needs from all functions of the organization, which can help the enterprise develop streamlined, reusable processes and leading practices to help business groups be more efficient using the tool.
• IT can also centralize functions such as infrastructure, licensing, administration, and deeper level development, all which further cut down costs and mitigates risks.

What are the key skills and expectations of the head of governing body or leader of the center of excellence team? Different organizations use very different titles for this person. But the person at the helm of your of governing body or leader of the center of excellence team should have the following skills:

• The passion about self-service analytics and related technologies
• The ability to lead, set strategy, and prioritize objectives based on needs/impact
• An in-depth understanding of self-service tool, the business analytics space, and the analytics needs of the business
• The ability to align self-service analytics objectives with corporate strategy and direction
• Comfort in partnering and negotiating with both business and IT stakeholders
• A talent for navigating the organization to get things done

Please read my next blogs for roles and responsibilities

Organizations committed to improve data-driven decision-making processes are increasingly formulating an enterprise analytics strategy to guide the efforts in finding new patterns and relationships in data, understanding why certain results occurred, and forecasting future results. Self-service analytics has become the new norm due to availability and simplicity of newer data visualization tool (like Tableau) and data preparation technologies (like Alteryx)

However many organizations struggle to scale self-service analytics into enterprise level or even business unit level beyond the proof of concept. Then they blame tools and start to try different tools or technologies. It is nothing wrong to try something else, however what many analytics practitioners did not realize that technologies along were never enough to improve data-driven decision-making processes. Self-service tools alone do not resolve organizational challenges, data governance issues, and process inefficiencies. Organizations that are most successful with self-service analytics deployment tend to have a strong business and IT partnership around self-service; a strategy around data governance; and defined self-service processes and best practices. The business understands its current and future analytics needs, as well as the pain points around existing processes. And IT knows how to support an organization’s technology needs and plays a critical role in how data is made available to the enterprise. Formalizing this partnership between business and IT in the form of a Center of Excellence (COE) is one of the best ways to maximize the value of a self-service analytics investment.

What are the key questions that Center of Excellence will answer?

1. Who is your governing body?
2. How to draw a line between business and IT?
3. What are the checks and balances for self-service releases?
4. How to manage server performance?
5. How to avoid multiple versions of KPIs?
6. How to handle data security?
7. How to provide trustworthy data & contents to end consumers?

The ultimate goal of the center of excellence is to have governed self-service in enterprise. The governance can be classified as six areas with total 30 processes:

Governing body

• Governing structure
• Multi tenant strategy
• Roles & responsibilities
• Direction alignment
• Vendor management

Community

• Intranet Space
• Training strategy
• Tableau User CoE meeting
• Tableau licensing model
• Support process

Publishing

• Engagement process
• Publishing permissions
• Publishing process
• Dashboard permission

Performance

• Workbook management
• Data extracts
• Performance alerts
• Server checkups for tuning & performance

Data Governance

• Data protection
• Data privacy
• Data access consistence
• Role level security
• Data sources and structure

Content Certificatio

• Content governance cycle
• Report catalog
• Report category
• Data certification
• Report certification

Please read my next blogs for each of those areas..