Congratulations! By investing in ThoughtSpot, you’ve committed to creating a data-driven organization, better positioned your business to dominate this decade of data, and empower frontline decision makers with actionable insights with Live Analytics.
As a pioneer in the augmented analytics category, we know it takes more than a disruptive technology to deliver value. For people to truly turn insight into action, you will have to instill why data and analytics are key to the mission of your organization and show them what is unique and valuable about ThoughtSpot’s approach. Some may push back on this. Some may be concerned about how this will impact their jobs or fear change in general. Others will embrace this newfound way to see and engage with live data, without waiting weeks or months for a data expert to respond. Overcoming these concerns means creating new habits in working with data and driving culture change.
To successfully drive this change, you as the data and analytics leader need a recipe for executing your vision.
This guide is your recipe. In each section, you’ll learn the critical strategies needed to create transformational impact from your first use case and beyond. You’ll also be able to download the resources in this guide as a Google slide deck or PowerPoint that can be customized and shared to help communicate your data vision. As you refine, develop, and repeat each of the recipe steps, you become that much closer to making a transformational impact across the organization.
Data has long been a critical asset for businesses to understand customers, operate more efficiently, inform go-to-market strategies, and retain your best employees. In a digital world, capturing and creating data-driven insights provides a major competitive advantage for those who can turn insights into action.
The global pandemic has accelerated digital transformation and widened the gap between analytics leaders and analytics laggards. A recent study by Accenture identified how leaders have two to three times the revenue growth of laggards, while McKinsey found those who are data-driven are three times more likely to say their data and analytics initiatives have contributed to earnings. Kearny and Melbourne University further show the profit potential, with leaders having 81% higher profits than laggards. As we start 2023 with continued fears of recession, numerous surveys of business and technology leaders show that data and analytics is not an area they will pull back on because it is essential for business. New Vantage Partners annual CDAO survey finds 93.9% of organizations are planning to increase their investments in data in the wake of potential economic uncertainty.
These industry trends are important for your organization to understand, but they are just a starting point. The next step is to contextualize the ‘why now?’ in terms of your organization’s mission, along with the technology shifts forcing you to modernize.
Consider the following:
Since 2020, telehealth suddenly went from zero to 100 percent with a prediction that a large portion of treatments will continue to be delivered virtually.
Supply chain disruptions continue to upend manufacturing and customer experience.
Employers must now enforce COVID vaccination and testing status for employees, while also looking for early warning indicators of high performers likely to leave.
Established banks are now competing with neobanks (such as Metro Bank or Starling) who are 100 percent digital and use apps and online platforms to support their customers, rather than traditional brick and mortar branches.
The pandemic has been an extreme forcing function to be more agile and efficient and a volatile economy continues to force operational excellence. Data-driven insights created with ThoughtSpot’s self-service analytics solution enable such efficiency, while giving you the agility to respond to the rapid-fire, myriad of new questions.
In crafting your communication plan, ask yourself:
What is the mission of your organization or department?
How do data and analytics support that mission?
What’s changed to increase the importance?
Is it an opportunity?
Competitive threat? New leadership?
Here are some examples of companies with a clear mission and vision for how data and analytics can drive success:
Roche ( mission statement and case study ): We want to deliver twice the medical advances to society at half the cost.
CarTrawler : We want to connect partners and drive customer loyalty in the travel space.
Amazon, Walmart, Target : We want to ensure customers can buy our products anywhere, with maximum flexibility to return or donate unwanted items.
Schneider Electric : We want to empower everyone to make the most of our energy and resources, bridging progress and sustainability for all.
OpenDoor : We want to empower people with the freedom to move and provide Americans with a radically simple way to buy, sell or trade-in a home.
Just as businesses are demanding more from their data, technology shifts are enabling increased adoption of cloud IaaS, cloud data platforms, search and AI, and open APIs. These technology forces together enable greater self-service analytics and insights at the point of impact.
Cloud data platforms allow you to access more data and run more complex algorithms, faster. This encourages experimentation and the ability to answer granular questions. There is no longer a need to create aggregate tables and cubes for fast performance, either for analytics or ingestion.
Search and AI enable non-technical business users to ask and answer their own business questions and uncover hidden patterns in their data. ThoughtSpot is a pioneer in this patented technology. Gartner calls this category “augmented analytics” with 87% of organizations planning to implement or have already implemented in 2022. As augmented analytics nears mainstream buying, visual data discovery and parameterized dashboards built by developers are now legacy.
Open API frameworks, such as the one delivered by ThoughtSpot Everywhere, enables you to choose and connect best-of-breed platforms to get the most value from your data while also linking insights to actions.
ThoughtSpot remains the only augmented analytics platform optimized for the modern data stack with open APIs that enables you to invest in best-of-breed capabilities across the data and analytics workflow.
Tailor this slide or presentation to craft your own WHY and WIIFM (what is in it for me) as part of your communication roadshow.
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While many organizations strive to create a data-driven culture, only 24% of data and analytics leaders say they have, according to a recent survey from NewVantage Partners . Technology is not the main barrier.
Technology is not the main barrier. It is culture, with 62% of business leaders citing this as the main reason they have been unable to execute on their data and analytics vision. Some may argue that culture is really code for lack of leadership.
It’s a valid point. Leaders do shape culture and recruit people that will reflect that culture. But company culture and change, or the resistance to it, can also be driven in a grass-roots way by workers at every level. Changing culture requires changing behaviors, and this can best be done by people at all levels continually reinforcing or “nudging” others toward the new desired behaviors. Several suggestions for how to accomplish this can be found in chapter 5, “Rollout, adoption and people change” .
Organizing your data and analytics team is one of the most important steps you can take to maximize ThoughtSpot’s impact and embed a data-oriented mindset across your entire organization. Notice how in the graphic below, data analysts sit in each line of business with a dotted line to the CDO. There are many ways to model your team based on your specific organization's needs, but we like this hybrid approach because it combines shared resources for economies of scale as well as domain knowledge for each business unit.
Embed data and analytics expertise throughout the organization
Once you’ve organized your team, you’ll want to clearly define roles and responsibilities.. Depending on the size of your organization and the scope of your ThoughtSpot deployment, multiple roles might be filled by the same person. For example the Product Owner and the Analytics Change Manager might be the same person or team. How many people cover each role is highly dependent on your specific deployment and size of organization. What is most important is ensuring every responsibility listed in the next section is covered.
Roles & Responsibilities - Center of Excellence
Analytics change manager
Successfully launching ThoughtSpot into the hands of your stakeholders, business users, or external customers starts with identifying your target use cases.
A good use case should ecompasses a business outcome that can be accomplished by users asking questions against a set of related data.
We recommend taking an inventory of all the potential use cases that could be delivered, then prioritizing them in the order they should be delivered. Some use cases may be simple. Others may require input from multiple stakeholders and have a longer lead time. This is normal and expected.
After organizing your list of use cases, select the one which results in the highest value for the greatest number of people and where the pain point is obvious and acute, but fairly low in complexity. Low complexity means that the data is ready enough, clean enough, and provides a currently unmet need.This will help you get a quick win and jumpstart the transformation you’re looking to achieve. Quick wins are a proven way to build energy and momentum for larger data initiatives.
When starting out focus on the “magic quadrant” of high value and low complexity use cases to kick start adoption and start seeing an ROI.
As high complexity use cases will require more resources and time to deploy, in order to maintain momentum aim for a balance between the high and low complexity.
Creating a use case road map will help identify resource allocation to support the success criteria.
High value may also mean net new analytics for an underserved group; low value may mean competitive tool.
Through this process you’ll assess the readiness of the data, team members who can help deliver it, the user persona and their potential openness to change, and the business outcomes that can be measured. This is discussed in more detail later on.
There are many ways you could roll out your first use case . For example, you could opt for a ‘big-bang’ approach where you rollout to your entire end user group in one go. However, we recommend using a simple two-phased approach, focusing first on power users, and then the wider community.
In the first phase, we recommend rolling out your solution to a smaller subset of the target user group and leveraging this group as power users. Selecting who will be in the initial rollout group is a critical step to ensuring success. Put thought into who these people in your organization should be. They’ll give you feedback on ThoughtSpot and serve as internal ambassadors and enablers as you scale your end user base. Ideally, your power users are those who are inherently curious, early adopters of new technology, and are already comfortable working with data.
In the second phase, you deliver your solution to the masses. After incorporating the feedback from your power users and refining your processes, you are ready to roll out to more business users.
Most users don’t get excited about learning a new analytics tool, even one as powerful as ThoughtSpot, because typical onboarding processes don't focus on “WIIFM”, or “What’s In It For Me,” from the user’s perspective. Learning something new requires effort, and if the benefit isn’t clear, then people will tune out and remember very little from onboarding, resulting in low adoption.
To get people motivated to learn a new tool and become engaged users of that tool, you need to show them something of value; something that will make their eyes light up and make them think (or often even say out loud) “wow, I need this!” This is called an aha moment.
Typically, people will pay attention for at most 15-20 minutes if they don’t see how something is valuable to them. This means rather than approaching onboarding by showing users how to use the tool, you need to start by quickly showing users an aha moment that resonates with them. Some common types of aha moments include one or more of the following:
Focusing on a use case that includes data and insights that users have been asking for but previously didn’t have access to, and walking users through a Liveboard that contains valuable insights into that data.
Showing users how they can drill anywhere in a Liveboard to get deeper insights immediately, rather than needing to ask the analyst team to provide that additional information to a dashboard.
Showing users how they can quickly search for answers to their own questions without needing to learn SQL or always have to ask an analyst for the data.
Showing how ThoughtSpot Sync (if it’s available as part of your license) makes it simple to drive the results of a query directly into the operational systems they use every day such as their CRM, Finance, or Support systems, instead of having to export the data, potentially reformat it manually, and then import it into the operational system.
Asking users to come to an onboarding session with 2-3 of their most important business questions, and showing them how quick and easy it is to get answers to those questions.
Once you’ve motivated your users to learn about ThoughtSpot by showing them aha moments that are meaningful to them, you can now transition to how to use it. But, don’t fall into the trap of only focusing on ThoughtSpot’s functionality without also making sure users understand the data as well.
There’s a big difference between technical literacy (understanding how to use the product) and data literacy. As an industry, we have spent far too much time training people on hard-to-use BI tools (technical literacy) and a woefully insufficient amount of helping people engage with data to answer critical business questions (data literacy.) This is yet another reason why search is such a powerful enabler: it lowers the technical skills required to get to data. Gartner defines data literacy as the ability to read, write, and speak data in a business context. But instead of thinking about this as data — something potentially intimidating and new — you should think of it as the language of the business.
Meanwhile, data fluency is the ability to think in data terms. Just as not everyone reads at the same level of proficiency, not everyone needs to be at the same level of data fluency. A data engineer may need to understand where data originates, if there are data gaps, potential biases and so on, while a manager may only need to interpret and interact with data in the form of live analytics. This is normal and expected. However, there is a baseline level of fluency everyone needs in order to be able to think critically about data and to recognize when there are both gaps and biases.
For business users, aim to spend no more
than 20% of the
time on using the tool and 80% of the time on the data.
A good rule of thumb for power users is to spend roughly 50% of the time in onboarding sessions teaching people about how to use ThoughtSpot and 50% teaching them about your data. It’s far better to spend 30 minutes on key functionality and 30 minutes on the key data fields than to spend 60 minutes going into more detailed functionality alone. For business users, aim to spend no more than 20% of the time on using the tool and 80% of the time on the data.
It’s also important to provide continued support after the onboarding sessions. Some common approaches include:
Setting up email and chat groups for people to ask questions.
Holding weekly office hours to provide support and gather feedback.
Creating an internal documentation site with FAQs, a glossary of common data fields, on-demand videos, and so on, so people can continue learning on their own.
The key to driving long-term engagement and retention for your users is to help them create the habit of using ThoughtSpot as a standard part of their daily or weekly workflows. This won’t happen overnight. It can take around 4 weeks to create a usage habit for someone who uses ThoughtSpot daily, and around 8 weeks for someone who uses it weekly.
The way to create a habit is through repetition, and an effective way to create that repetition is through the use of various “nudges” that help encourage usage until a habit is formed. There are many different types of nudges (several are listed below) but the primary rule for an effective nudge is that the user must perceive it as providing value to them. Otherwise, users will see the nudge as annoying, and it will have the opposite of the intended effect.
Creating engagement loops
Engagement loops are things that have an inherent repetitive nature to them. For example:
Using ThoughtSpot’s scheduling capabilities to email users a snapshot of a high-value Liveboard every Monday morning.
Using ThoughtSpot’s alerting capabilities to create alerts that get triggered whenever a key metric goes above or below specified thresholds.
Making data a key part of business conversations
Spend the first 5-10 minutes of weekly meetings reviewing a liveboard related to the topic of the meeting and drilling into the data.
Share links to Liveboards in your communications to reinforce that data is critical to business discussions.
Keeping analytics top-of-mind
Send out short weekly emails highlighting what’s new in the data or Liveboards.
Regularly share examples of how analytics has had a positive impact on the business such as:
Where analytics helped identify opportunities to improve the business, and what the measurable impact has been.
Where analytics helped identify valuable lessons learned in unsuccessful initiatives.
Providing positive reinforcement
Publicly recognize those who have used analytics to drive impact. That is, in addition to sharing examples of where analytics has had a positive impact, it’s equally important to publicly recognize the people who used the data to drive that impact.
In 1-1’s, managers should commend their team members when they make effective use of data.
Add a section in employee reviews that focuses on how effectively the employee uses data in their role. This ties the use of data to career progression.
Create weekly contests, such as who can get the right answer to a business question, with different rewards such as coffee mugs, water bottles, t-shirts, etc. (hear how Carlisle Homes uses gamification in this Data Chief Live)
Use the built-in adoption leaderboard of who has been using ThoughtSpot the most (such as most searches, most liveboard views, most days using ThoughtSpot in the past month, etc.) again with different rewards each month for the leaders.
Review user engagement each week to see which users are regularly engaged, and which are mostly inactive. Make sure to get feedback from those who are mostly inactive to help you identify what is needed to increase their engagement.
People change management is critical in driving success with ThoughtSpot. Current analytics processes in which business people are either spoon fed dashboards or have made decisions from gut-feel have been built up over years and decades. Without a plan focused on people as well as process change, your adoption goals may fall flat.
This article highlights the five phases of the Kubler-Ross change curve, the de facto framework for how humans engage with any major life change. Bringing ThoughtSpot to your company isn’t just about introducing a new technology, but asking people to do something fundamentally different when it comes to how they use data.
Measuring business benefits is a great way to document your transformation progress and driving funding and interest in subsequent use cases.
The goal of this value-based approach is to help business users generate insights that drive action and decision-making which leads to measurable value. This starts with understanding the pain (or opportunity) you are addressing with ThoughtSpot, and what the ideal future state looks like.
Owner: Who is responsible for owning this use case?
Persona: Who are the users engaging with ThoughtSpot?
Insight: What insight(s) will this persona gain from ThoughtSpot that they cannot today?
Action: What action(s) will be taken as a result?
Measurement: How can we measure the impact of this/these actions?
Your ultimate measure of success will be improving a particular business outcome, but there are also leading indicators that show progress. For example, in a people analytics use case, your desired business outcome may be to retain top employees and reduce attrition of high performers. Unwanted attrition will have a monetary cost.
Prior to launching ThoughtSpot, take a baseline of the current metrics such as:
Attrition rates, employee NPS.
Days to respond to requests for this information or to create a new dashboard.
Request backlog, along with the mix of requests from low value descriptive analytics to higher value requests such as new data sets and predictive analytics.
Number of people with direct access to this information.
Enablement sessions for this use case.
User satisfaction with the ability to answer key business questions.
Attendance at weekly office hours.
As you deploy ThoughtSpot, capture the anecdotal stories of business value - whether it is an insight discovered or time saved. Socialize these value statements as a way of continuing to drive change and excitement.
Train team and LoB Experts to watch and listen for: “I love ThoughtSpot”
Ask users why and how it improves their day to day
Investigate how we impact the business area they support
Tell the story in one slide
Advocate for the advocates, share stories and impact with internal user community to drive excitement and recognition
Identify specific improvements and quantify the dollar value of positive business outcomes achieved.
Understand the “before state” & “after state”, interview key users and stakeholders.
Keep an ROI scorecard, quantify the value of ThoughtSpot use-cases.
Review ROI use-case score card & pipeline status of use-cases quarterly with stakeholders.
Create an executive roll-up summary as a measure to enforce joint accountability toward success.
ABOUT THE AUTHORS
Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is an analytics and BI thought leader and expert with a flair for bridging business needs with technology. As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to becoming data-driven and influences ThoughtSpot’s product strategy. She recently was awarded Motivator of the Year by Women Leaders in Data and AI.
Maria manages the Customer Success team in Europe, working with our key customers like T-Mobile, NatWest and Siemens to drive enterprise-wide analytics transformational journeys. She is passionate about leveraging data to change the way we work and helping teams understand the value analytics brings to their organization. Prior to ThoughtSpot Maria worked at Deloitte in both the US, UK and Germany helping customers on analytics transformation journeys, evaluating which BI tool for which use case and was a BI developer at times.
Andrea drives the successful launch of ThoughtSpot by managing all aspects of project delivery during service implementations. She is laser-focused on ensuring our customers feel enabled and confident in building for and adopting ThoughtSpot into their organization. Before joining us, she scaled the ThoughtSpot deployment at mega-retailer Canadian Tire and continues to leverage her experience to drive customer satisfaction.
Josh supports ThoughtSpot’s enterprise customers in the US and Canada, helping drive transformational user adoption through organizational shifts to self-service analytics. Formerly a customer of ThoughtSpot, Josh enabled 1,000+ users across dozens of business units, making ThoughtSpot a key part of their finance operations transformation story and transforming dashboard developers into analysts of the future.
Ken leads the Product Led Growth initiatives across ThoughtSpot, focusing on optimizing our product to ensure users get the most value out of ThoughtSpot as possible. Previously, Ken led the global User Growth and Analysis team at Google, where he focused on driving user acquisition, engagement and monetization across all Google products and services.