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How to Scale your Analytics in a Maturing Organization

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Alyson Murphy is the in-house Senior Data Architect at Moz. She works with stakeholders to build a data solution that help Moz make data informed business decisions. Sean Work runs the blog at KISSmetrics.com. He’s been with the team since 2010.
We consolidated key data that was routinely used for analysis onto one reporting server. We then funneled key pieces of data into our web analytics solution so that there were fewer places to have to look for data when it was time to do an analysis.
We used to use email to get and prioritize projects. We shifted to Trello which allows us to have templates to ensure request quality and to be transparent about when certain projects will be worked on.
Where to Focus 2
Then we may branch out into orange and other colors.
But that’s all it is, a series of colors.
It’s not until much later that we start to see the entire picture. Where to Focus
In reality, your ball probably looks like this.
Goal 1: Build the Minimum Viable Ball
Components of a Data System There are 6 main areas of focus for building a successful and scalable data system.
Data Infrastructure Consolidating your data sources will make analysis easier and quicker which is important when you start adding people to your team.
Data Integrity Data Infrastructure and Data Integrity are perhaps the most important places to start because decisions in these areas waterfall into the other areas of your Data System.
Data Access and Visualization Data Access and Visualization is key as your company starts to grow. The goal is to make the data as easy to access as possible for people who have the skills to fish for their own data..
Infrastructure Change Process When you are in startup mode, everyone might have access to do what they need to do quickly to implement the changes they need to make. In a small organization this works out because everyone knows what everyone else is working on.
Goal 1: Build the Minimum Viable Beach Ball
To optimize the system, you may have to sub-optimize the subsystem.
Over COMMUNICATE what sections you are working on (helps with buy-in)
Ways to get buy-in
Pre-Research Buying Decisions
Data Infrastructure Data Integrity Data Access & Visualization Components of a Data System Infrastructure Change Process People in your Org Data Utilization Process Changes
In order to re-evaluate the KPI’s we look at, we had a collaborative meeting where each of the groups came up with a dashboard. We then looked for areas where we needed to create alignment. After that, we started building.
Data Infrastructure Data Integrity Data Access & Visualization Components of a Data System Process Changes Infrastructure Change Process People in your Org Data Utilization Process
Minimum Viable Product (MVP) vs. All-in-one Do you want to ship as little as possible as soon as possible and learn and add versus shipping a totally finished product all at once.

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How to Scale your Analytics in a Maturing Organization

  1. 1. Scaling your Analytics in a Maturing Organization December 11, 2014 Alyson Murphy
  2. 2. Join the conversation on Twi!er @seanvwork #KISSwebinar
  3. 3. Join the conversation on Twi!er @AlysonMurphy #KISSwebinar
  4. 4. Alyson Murphy - Moz - @AlysonMurphy Alyson Murphy is the in-house Senior Data Architect at Moz. She works with stakeholders to build a data solution that help Moz make data informed business decisions. Sean Work - KISSmetrics - @seanvwork Sean Work runs the blog at KISSmetrics.com. He’s been with the team since 2010. He loves working from Southern California where he can surf, snowboard and camp under the bright starts with no blankies.
  5. 5. Table of Contents 1 Section One – Some Context 2 Section Two –Where to Focus Data Infrastructure Data Integrity Data Access and Visualization Infrastructure Change Process Data Utilization Process People in your Organization 3 Section Three – Lessons Learned Ways to Get Buy-in Ways to Ship Solutions Ways to Leverage and Empower Others
  6. 6. WATCH WEBINAR RECORDING NOW
  7. 7. Some Context 1
  8. 8. Introduction Some Context I work as an in-house data analyst and architect at Moz. We sell inbound marketing so"ware to help people do be!er marketing. Moz is B2B so there will be many B2B examples but all of what I am talking about can be applied in a B2C context.
  9. 9. Some Context We have been growing a lot We had just broken 100 employees when I started less than two years ago and now we are around 150. Scaling so quickly is exciting but can present challenges as well.
  10. 10. And Changing a lot We re-branded and changed our name to align with the changes in the industry. We released a new flagship product to serve the shi"ing needs of the industry. Some Context
  11. 11. Our data has been changing too Some Context We consolidated key data that was routinely used for analysis onto one reporting server. We then funneled key pieces of data into our web analytics solution so that there were fewer places to have to look for data when it was time to do an analysis. Tool Usage & Subscription Info Moz.com Production Data Moz Analytics Campaign Data Moz email Moz Local data Reporting Server Web Analytics Tool
  12. 12. Some Context Our Process has Been Changing as Well We used to use email to get and prioritize projects. We shi"ed to Trello which allows us to have templates to ensure request quality and to be transparent about when certain projects will be worked on. Email Trello
  13. 13. Some Context We aren’t there yet though We’ve learned a lot of lessons se!ing us up to scale. Some were things we just needed to struggle with, but there were some tips I knew two years ago. Hopefully some of them will help you and your team.
  14. 14. Start Your Free KISSmetrics Trial LOG IN WITH GOOGLE
  15. 15. Where to Focus 2
  16. 16. Imagine a ball Where to Focus
  17. 17. Get close Where to Focus
  18. 18. Where to Focus That’s a lot of Green!
  19. 19. Where to Focus This is how we start. Tactical, in the weeds.
  20. 20. Then we may branch out into orange and other colors.
  21. 21. But that’s all it is, a series of colors.
  22. 22. It’s not until much later that we start to see the entire picture. Where to Focus
  23. 23. In reality, your ball probably looks like this.
  24. 24. Goal 1: Build the Minimum Viable Ball
  25. 25. Where to Focus Components of a Data System There are 6 main areas of focus for building a successful and scalable data system. All parts are required with equal focus to make the system work. Data Infrastructure Data Integrity Data Access & Visualization Infrastructure Change Process People in your Org Data Utilization Process
  26. 26. Where to Focus Data Infrastructure Consolidating your data sources will make analysis easier and quicker which is important when you start adding people to your team. You want your team to be as efficient as possible. The data infrastructure is tied closely with Data Access and Visualization. The backend choices you make will affect the front end options that you have. Tool Usage & Subscription Info Moz.com Production Data Moz Analytics Campaign Data Moz email Moz Local data Reporting Server Web Analytics Tool
  27. 27. Where to Focus Data Integrity Data Infrastructure and Data Integrity are perhaps the most important places to start because decisions in these areas waterfall into the other areas of your Data System. Spot Check at Time of Analysis All Systems Comprehensively Checked And Maintained
  28. 28. Where to Focus Data Access and Visualization Data Access and Visualization is key as your company starts to grow. The goal is to make the data as easy to access as possible for people who have the skills to fish for their own data. You want to be able to have business users be able to answer simple questions on their own so that your time can be be!er spent on more complex business questions. Types of Business Problems you Need to Consider • Simple business questions for researching upcoming projects • Complex business questions for researching upcoming projects • Health Dashboards for monitoring to ensure nothing is broken • Dashboards and Reporting for product/website releases and achievement of Key Results.
  29. 29. Where to Focus Infrastructure Change Process When you are in startup mode, everyone might have access to do what they need to do quickly to implement the changes they need to make. In a small organization this works out because everyone knows what everyone else is working on. When you start to grow though, this becomes less sustainable because not every does not know about everything that is going on so they will not understand the complete ramifications of their actions. Therefore a point person will need to be introduced to sign off on changes.
  30. 30. Where to Focus Data Utilization Process When you are in startup mode, everyone uses whatever data they can get their hands on. Everyone is an analyst. They might aggregate information from a dashboard and an analysis that was used to answer a different business question in an effort to be as lean as possible. However when you scale, it becomes more efficient to have analyst do the analytics work and pass on the results to the business person. This is because the work will be done more quickly and completely. It is also the best use of business person’s time to allow the analyst to do the work so they can focus more on strategizing and managing their area of the business.
  31. 31. Where to Focus People in your organization As your business grows, there will be more of a need to level-up people in your organization so that everyone can access the data and draw sound conclusions. Level of Complexity Strategic Tactical Analysts Business Users Developers Research potential projects through analysis. Leverage complex analyses to make good business decisions. Leverage the work of the analysts in a Production environment. Builds and QA’s tools so others can access data. Pull basic requests correctly. Implement and maintain the technical-side of the Web Analytics Solution. Wrangles tools to access data. Ask Analyst to pull data. Structure Databases for Production Use.
  32. 32. Lessons Learned 3
  33. 33. Goal 1: Build the Minimum Viable Beach Ball
  34. 34. To optimize the system, you may have to sub-optimize the subsystem.
  35. 35. Generating change is a PEOPLE PROBLEM (even if the change is technical)
  36. 36. Over COMMUNICATE what sections you are working on (helps with buy-in)
  37. 37. Ways to get buy-in
  38. 38. Lessons Learned Data Infrastructure Data Integrity Data Access & Visualization Components of a Data System Infrastructure Change Process People in your Org Data Utilization Process
  39. 39. Lessons Learned Data Infrastructure Data Integrity Data Access & Visualization Components of a Data System Infrastructure Change Process People in your Org Data Utilization Process
  40. 40. Lessons Learned Data Infrastructure Data Integrity Data Access & Visualization Components of a Data System Infrastructure Change Process People in your Org Data Utilization Process Buying Decisions
  41. 41. Pre-Research Buying Decisions
  42. 42. Lessons Learned Buying Decisions Below is the typical process one might go through to pitch why a new tool should be bought. 1 Define problem 2 Lay out possible solutions 3 Research solutions 4 Make decision on solution HAVE MEETING HERE!
  43. 43. Lessons Learned Buying Decisions For the group, a 22 slide deck and 30 minutes was all it took to make the decision to buy a new web analytics tool. This is because a lot of research went into those slides. We made a well informed decision quickly.
  44. 44. Lessons Learned Data Infrastructure Data Integrity Data Access & Visualization Components of a Data System Infrastructure Change Process People in your Org Data Utilization Process Process Changes
  45. 45. Co-create Process and Content Changes
  46. 46. Lessons Learned Process and Content Changes Below is the typical process one might go through to pitch why a new process should be changed or why we should look at KPI’s differently going forward. 1 Define problem 2 Lay out possible solutions 3 Research solutions 4 Make decision on solution HAVE MEETING HERE! HAVE MEETING HERE!
  47. 47. Lessons Learned Process and Content Changes In order to re-evaluate the KPI’s we look at, we had a collaborative meeting where each of the groups came up with a dashboard. We then looked for areas where we needed to create alignment. A"er that, we started building.
  48. 48. Lessons Learned Data Infrastructure Data Integrity Data Access & Visualization Components of a Data System Process Changes Infrastructure Change Process People in your Org Data Utilization Process
  49. 49. Empower Leveling-Up
  50. 50. Lessons Learned Leveling Up 1 Be intentional about the resources you give people. 2 Provide in-person training for people who should use data everyday. 3 Provide optional training for all.
  51. 51. Ways to Ship
  52. 52. Lessons Learned Minimum Viable Product (MVP) vs. All-in-one Do you want to ship as li!le as possible as soon as possible and learn and add versus shipping a totally finished product all at once. Phase and Benefit Minimum Viable Product All-in-one Planning Phase Must plan theoretical phases of implementation, thought that may all change as you go. Must do more detailed planning around what you are shipping since you are shipping more stuff. Implementation Phase You may ship a dashboard in 5 phases with a step to re-evaluate the rest of the plan between each phase. You ship it all at once. There will still likely be optimization but it will be small tweaks as opposed to huge feature changes. Benefit Allows you to ship sooner so people have something to work with. Allows for a smoother rollout to a larger group because they will not be lost in where in the implementation phase the tool is. A hybrid exists where you have only key stakeholders involved in the MVP-style iteration then do an All-in- one rollout to the rest of the interested parties.
  53. 53. Create dashboards people live in.
  54. 54. Lessons Learned Ship your Minimum Viable Product and Learn Plan, then ship an MVP in an environment that allows for quick creation, update manually for a few updates where you learn what needs to change, change it, then automate in a more permanent solution. Plan 1. Ship MVP and Learn 2. Automate 3.
  55. 55. Ways to Leverage and Empower others
  56. 56. Lessons Learned Create a Transparent Process Put all requests in a public place. Have teams self-prioritize the list to save you time so all you have to do is work on the projects on the top of the list. List I used to Prioritize Lists Teams Prioritize
  57. 57. Lessons Learned Enable Teams to Use a Framework You may not have time run all of the processes that an analyst would in a larger organization. Create a framework that the teams use to self-govern. This may not be a long-term solution but it will help free up time to allow you to optimize other areas of your Data Analytics System.
  58. 58. Table of Contents 1 Section One – Some Context 2 Section Two –Where to Focus Data Infrastructure Data Integrity Data Access and Visualization Infrastructure Change Process Data Utilization Process People in your Organization 3 Section Three – Lessons Learned Ways to Get Buy-in Ways to Ship Solutions Ways to Leverage and Empower Others
  59. 59. Goal 2: Expand the Beach Ball
  60. 60. Key Take-Away’s 1 Build the Minimum Viable Beach Ball 2 Pre-Research Buying Decisions 3 Co-Create Processes and Content Changes 4 5 Empower Leveling-Up Expand the Beach Ball
  61. 61. THANK YOU Alyson Murphy @AlysonMurphy

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