Governed Self-Service Analytics: Multi-tendance (5/10)

Tableau has a multi-tendance strategy which is called site.  I heard many people asking if they should use site, when should use site. For some large Tableau deployment,  people also ask if you have created separate Tableau instances. All those are Tableau architecture questions or multi-tendance strategy.


How do you approach this? I will use the following Goal – Strategy – Tactics to guide the decision making process.screenshot_42

It starts with goals. The  self-service analytics system has to meet the following expectations which are ultimate goals:  Fast, Easy, Cost Effectiveness, Data Security, Self-Service, Structured and unstructured data.

Now keep those goals in our mind while scale out Tableau from individual teams to department, and then from department to enterprise.


How do we maintain self-service, fast and easy with solid data security and cost effectiveness while you deal with thousands of users? This is where you need to have well-defined strategies to avoid chaos.

First of all, each organization has its own culture, operating principles, and different business environment. Some of the strategies that work very well in one company may not work for others. You just have to figure out the best approach that matches your business requirement. Here is some of food for thoughts:

  1. Do you have to maintain only one Tableau instance in your organization? The answer is no. For SMB, the answer may be yes but I have seen many large organizations have multiple Tableau instances for better data security and better agility. I am not saying that Tableau server can’t scale out or scale up. I have read the Tableau architecture white paper for how many cores one server can scale. However they are many other considerations that you just do not want to put every application in one instance.
  2. What are the common use cases when you may want to create a separate instance? Here is some examples:
    • You have both internal employees and external partners accessing your Tableau server. Tableau allows both internal and external people accessing the same instance. However if you would have to create a lot of data security constraints in order to allow external partners to access your Tableau server, the same constraints will be applied to all Tableau internal users which may cause extra complexity. Depends on the constraints you will have, if fast and easy goals are compromised, you may want to create a separate instance to completely separate internal users vs. external users – this way you have completely piece of mind.
    • Network seperation. It is getting common that some corporations have separate engineering network from the rest of corp network for better IP protections. When this is the case, create a separate Tableau instance within engineering network is an easy and simple strategy.
    • Network latency. If your data source is in APAC while your Tableau server is in US, likely you will have some challenges with your dashboard performance. You should either sync your database to US or you will need to have a separate Tableau server instance that sits in APAC to achieve your fast goals.
    • Enterprise mission critical applications. Although Tableau started as ad-hoc and exploration for many users, some Tableau dashboard start to become mission critical business applications. If you have any of those, congratulations! You have a good problem to deal with. Once some apps become mission critical, you will have no choice but tight up the change control and related processes which unfortunately are killers to self-service and explorations. The best way to resolve this conflict is to spin-off a separate instance with more rigors on mission critical app while leave the rest of Tableau as fast, easy self-service.

What about Tableau server licenses? Tableau server have seat-based license model and core-based license model. If you have seat-based model, which goes by users. The separate of instance should not have much impacts on total numbers of licenses.

Now Let’s say that you have 8 core based licenses for existing internal users. You plan to add some external users. If you will have to add 8 more cores due to external users,  your separate instance will not have any impacts on licenses.  What if you only want to have a handful external users? Then you will have to make trade-off decision. Alternately you can keep your 8 core for internal users while get handful seat-based license for external users only.

How about platform cost and additional maintenance cost when we add separate instance? VM or hardware are relatively cheap today. I will agree that there are some additional work initially to setup a separate instance but server admin work is not doubled because you have another server instance.  On the other side, when your server is too big, it is a lot of more coordinations with all business functions for maintenance, upgrade and everything. I have seen some large corp are happy with multiple instance vs. one huge instance.

How about sites?  I have blog about how to use site. As summary, site is useful for better data security, easy governance, employing self-service and distributing administrative work. Here is some cases when sites should not be used:

  • Do not create a new site if the requested site will use the same data sets as one of the existing sites, you may want to create a project within the existing site to avoid potential duplicate extracts (or live connections) running against the same source database.
  • Do not create a new site if the requested site overlaps end users a lot with one existing site, you may want to create a project within the existing site to avoid duplicating user maintenance works

As summary, while you plan to scale Tableau from department to enterprise. you do not have to put all of your enterprise users on one huge Tableau instance. Keep goals in your mind while deciding the best strategy for your business. The goals are easy, fast, simple, self-service, data security, cost effectiveness. The strategies are separate instance and sites.


Please read next blogs about release process.

Governed Self-Service Analytics: Community (4/10)

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?screenshot_20

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

Governed Self-Service Analytics: Roles & Responsibilities (3/10)

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.screenshot_18

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.

Governed Self-Service Analytics : Governance (2/10)

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.screenshot_17

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

Governed Self-Service Analytics (1/10)

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


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


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


  • 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..


How business benefits from IT leadership with self-service analytics

Last week, I presented Self-Service Analytics in a local Silicon Valley meet-up “Run IT as Business” group.  The audiences include some IT executives and a few ex-CIOs. My presentation is well received with some very positive feedback:

  • “Mark gave an excellent presentation that was extremely informative!”
  • “well structured and very informative”
  • “This is one of the more interested presentations I’ve heard lately”

My talk focused on the new theme of BI and analytics – self-service analytics that is white hot and rapid growing. I shared how NetApp’s  change management to have users take ownership on this technology which is the success factor.

Slides for this talk is @

Event feedback details @