To borrow from Hemingway: change happens gradually, then suddenly.
While the world has transformed dramatically since 2019, in many ways the changes we’ve seen in the business world are merely the culmination of a transition already well underway.
The last 10 years have seen some paradigm-shifting evolutions in technology. The gig economy, digital content streaming, and commercial space travel have gone mainstream. Space-based manufacturing, the metaverse, and self-driving cars are no longer science fiction.
Now, technologies like cloud, AI, and digitization are at the forefront of doing business. Together with newer advancements—like Web3, quantum computing, and 5G—they’re poised to build an entirely new world. These digital developments will create opportunities for businesses that take advantage of them—and risks for those that don’t.
And, of course, when you look under the hood, underpinning these trends is a whole lot of data. Data that is living entirely in the cloud for the first time.
We’ve entered the defining decade of data. And the companies that dominate this new decade will be the architects of our new world. Shouldn’t your business be one of them?
In this guide, we’ll explore the horizons of this new decade of data.
We’ll deliver insights into how companies can successfully navigate the transition from data-aware to truly data-driven. We’ll outline the rules of engagement for the new era, and hear from some of the world’s premiere data leaders on the changing realities facing today’s organizations. Finally, we’ll share our advice on how your company could become an architect of the coming era.
The evidence is all around us. Data volumes are exploding at a pace of roughly 2.5 quintillion bytes per day.
Data is driving multi-billion dollar deals and acquisitions in the business sphere. Just look at Microsoft’s $26 billion acquisition of LinkedIn or Oracle’s $28B acquisition of Cerner.
In some cases, a company’s data is two or three times more valuable than the company itself.
Businesses like Snowflake or Databricks, which are foundational to the modern data stack, are breaking market records. In 2020 Snowflake commanded the biggest software IPO of all time —and it wasn’t because of their catchy billboards.
But it’s not just about the money or the sheer scale of the data volumes. This is the decade of data because of the dramatic impact data is having on the way we work and the power dynamics in the market.
Data is driving action. It’s upending power structures that have existed for years. And as data becomes more democratized, we can see pervasive, undeniable, and immediate shifts in power happening across markets.
Today, every sector is being redefined by data-driven power shifts. It doesn’t matter whether your organization buys into them or not. The shifts are happening and if you haven’t already felt their impact it’s only a matter of time.
Keeping the viewer experience
Chief Data Office,
To set your company up for success, you’ll have to face these shifts head on and adopt a new playbook for staying ahead. The actions you take today will dictate the future of your business for the next 10 years or more.
Luckily, the very trend that is driving these shifts is your key to staying on the right side of them. And that is your data.
Your strategy for using data is now the strongest competitive differentiator you have. It’s the key to building great products, delivering excellent customer experiences on the frontlines , and keeping your employees engaged .
But you can’t just collect data for the sake of itself. Action is the destiny of data.
Using data to expand the art of the
CIO and CDO,
The decisions you make now for your data will help you navigate the next quarter or meet our annual targets, sure. But they’ll go far beyond that. They will set up your team, your department and your entire organization to dominate this new era. And create a lasting impact on your customers, your industry, and perhaps even the world at large.
You have a unique opportunity to create your legacy for decades to come.
Sure, change is difficult. It often feels easier, even smarter, to continue on a known path. But the defining decade of data demands more from us.
Redefining the technology stack creates an opportunity that’s too big to pass up—the ability to shift more power to the consumer. This change allows a company to create a legacy benefiting not only current teams and customers but also future teams and customers.
So, will you go down the same path, following the last decade’s playbook? The opportunity for doing data differently is massive, but there are plenty of mistakes that can stand in your way.
Let’s dig into those first—and then what to do instead.
Building a legacy during a global
VP, HealthSight Platform
Too many businesses are keen to embrace the new decade of data–but struggling to let go of the past. Any of this sound familiar?
Just 10% of executives believe their employees can effectively use data for decision-making.
Two-thirds of executives aren’t confident about their ability to access or use data with their existing tools and resources.
After two decades, analytics adoption rates have stalled, at around 30% .
So, what gives? Why are businesses still struggling to use their data to drive action–despite the major advances in analytics technology available to them?
Here are the biggest mistakes we see companies making again and again–the issues that are holding them back from dominating the defining era of data:
Far too often, companies are reluctant to part with traditional, one-size-fits-all tools that can't handle the realities of the new data era. These tools may do one thing well—but they don't connect to the modern data stack.
Worse, they aren't built with today's data challenges in mind. Legacy BI tools were built for desktops and single-server applications. They usually don't play well with the massive scale of today's cloud-based data warehouses.
And it's not just about the scale of the data; it's also about the demands placed on that data by a modern data-driven business. Tools built around dashboarding, cubes, and extracts start to fail as the number and complexity of use cases begin to surge.
A Dimensional Research report found that in companies relying on legacy BI dashboards.
It no longer makes sense that business users have to go cap in hand to data analysts whenever they need to use data for decision-making. This is a poor use of time for both parties. A study by Harvard Business Review reported that 87% of organizations believed they would be more successful if frontline workers were empowered with data–yet only 20% of them had made moves to put data into the hands of those workers.
Meanwhile, more than two-thirds of data analysts say they lack adequate time to implement profit-driving ideas since they’re wasting up to 50% of their workdays maintaining dashboards and providing customized reports.
The purpose of data is to create insights that drive action. But for that, you need those insights presented in ways your frontline workers can actually use.
Unfortunately, insights often die on the vine because tools can't talk to each other. For instance, in the traditional process, an employee must manually find an insight, go to a meeting, share the finding, and suggest the team use it to make relevant changes. Each step along the way is another opportunity for human error or for new questions to arise, starting the cycle all over again.
Yet too many companies continue to rely on data extracts and static dashboards. Sure, they may have the necessary insights–but they’re often out of date, too hard to access at the moment of need, or too fragmented to be reliable.
A Dimensional Research report found that in companies relying on legacy BI dashboards:
86% of the data used to create insights is out of date; and
41% of insights are using data that is two months old or older
This is creating major obstacles for companies on a quest to dominate the decade of data. Two-thirds of the organizations surveyed in the HBR report believe that improving their organization's success with BI and analytics comes down to using live data and trusting in the data.
Enter the cloud-connected ecosystem. With this technology, companies can access timely data—plus insights can fuel actions directly since systems interact.
Too many organizations are not only still relying on static dashboards but equally static and inflexible data pipelines.
Every time a new business query or use case emerges, the business user has to ask the analyst, who must ask the data engineer to build that specific data into their flow, who must then ask the IT team to make sure the new data is validated, governed and secure before it can be modeled. The process is repetitive, frustrating, and expensive.
Today’s data leaders will need a data foundation that is structured and governable, but also flexible, with programmable data models. That way, it can be iterated as the decade of data throws up new opportunities and challenges.
For a complete, holistic view of your organization’s reality, you’ll also need to take advantage of the wealth of available third-party data . This isn’t exactly new.
But plenty of leaders make the mistake of thinking what was possible five years ago is still the case. The amount—and kinds—of data available today are far and above what was available even half a decade ago. The feasibility of capturing and leveraging this data isn’t some future-state. It’s here, now.
Of course, with cloud data storage, data sharing no longer requires data movement, making the process easier to visualize and scale. Even with a cloud data warehouse in place, if you’re not set up for the modern data stack, you’ll still be facing a laborious, manual process of preparing and analyzing third-party data.
Data insights shouldn’t be relegated to analyst reports. Business users, customers, and frontline workers need data at their fingertips to compete in today’s data-driven market. But many businesses keep data siloed in BI platforms, instead of embedding Live Analytics into their employees’ day-to-day.
Your employees and customers shouldn’t need to go looking for relevant data–it should be right there in front of them, in the apps where they spend their time (think Slack, Salesforce or Google Docs).
Users should be able to ask any business question they want, in the natural language they’re used to–without needing help from a data expert. The businesses that complain that their users aren’t making the most of their analytics tools have probably not spent enough time thinking about the frustrating, overly complex experience most users are having with those tools.
Don’t let these mistakes hold your business back. Try ThoughtSpot free for 30 days to dominate the decade of data.
So, how do you make the leap from 2014-esque insights to what you’ll need in this next decade? How do you move away from the common mistakes we’ve seen so many companies make–and start to dominate the new data landscape?
In our work with customers who have already moved boldly into the decade of data, we’ve seen what it takes. While every business is different, we’ve seen successful companies follow six common steps—and we’ve turned those into the six rules for this decade of data. Follow these rules, and your company will be poised to join the ranks of data leaders.
When building a modern data stack, it’s easy to fall into the trap of using “good enough” technology, provided by a convenient one-stop-shop vendor.
Navigating the modern data stack flow
But good enough really isn’t good enough. Not anymore.
86% of frontline workers report a need for better insights technology. Businesses often rely on multiple data stacks—for instance, marketing and customer data may live in a CRM, but finance and sales data is in a POS system. As a result, their data is siloed, and their analysts can only ever see part of the story their data is trying to tell them, never the whole.
Of course, many companies have moved their data to the cloud to try and fix the issues with their existing data stack. But cloud data storage is only the beginning. You also need to use best-in-class technology across every layer of your stack to realize the full flexibility and power of the cloud.
“End-to-end” is the new status quo; the minimum for getting by in a world driven by data. But, in this decade of data, the difference between “good enough” and “excellent” is so significant that you can’t get by for long—even if it’s easier.
Soon enough, your team and your customers will start to notice.
If you’ve decided you want to capitalize on the power of the modern data stack, you may find yourself spoilt for choice: from end2end stacks to single service offerings, in areas ranging from data integration, transformation, to data platforms, to governance, to analytics...
All that choice sounds great—but how do you separate the best from the rest? If you’re not methodical about how you build your stack, you could be stuck with systems and services that do not work well with each other, are simply not best of breed, or are too difficult to use.
So how should we make our choices?
While many companies claim to be making modern data technology, truly world-class data tools have a few characteristics setting them apart from the rest. When considering adding a new technology to your stack, ask:
Is it easy to try and deploy? Will it help us move quickly to take our place in the defining decade of data?
Is it cloud native? Can it take advantage of live connectivity from cloud data platforms at scale?
Can it handle modern data volumes-while also busting data silos?
Is it open and straightforward to integrate into our tech stack? Will it be easy to adapt in the future as our data strategy evolves?
Does it have a user experience layer built in–and was it designed with the business user in mind?
Will it help us put the value of our stack into the hands of every employee?
Sure, finding the right tools for your data stack might take you a while, but just consider the benefits. With modern cloud analytics capabilities, you’ll suddenly find that your company can:
It’s long been said the secret to a better answer is to ask a better question.
But in an era of legacy BI tools, that’s easier said than done. You could ask the best question in the world. It won’t make any difference if your analyst team takes weeks to pull the answer for you based on stale data. The answer you’ll get isn’t timely or personalized. At that point, it might not even be actionable any more. And what happens when the first question inevitably sparks a second one?
Dashboard vs. modern world speed
Unfortunately, if you’re still relying on static dashboards, it doesn’t really matter how much you modernize your data stack. There’s no way you can get your business ready to compete in the defining decade of data.
If your data is sitting in the hands of a few overworked analysts, your business users can’t get the insights they need to drive their decisions. Instead, they’re stuck in what we like to call “the infinite loop of dashboard insanity.” The business user has a question, but is waiting for the analyst to find them a metric to answer it. The analyst is waiting for the data engineer to build them a new model to capture that metric. The data engineer is waiting for the IT department to validate the data that they can use to build the model to provide the metric. And then the whole cycle repeats again!
This isn’t only inefficient—it’s expensive.
When you’ve added up all the people hours involved, we estimate that the cost of personalizing insights and creating new dashboards is approximately $18,000 a piece.
Meanwhile, the world around us has become self-service. We book our own flights, we find our own restaurants, and we watch videos on Youtube that match our interests.
On the consumer web, this is made possible by search and AI as core technologies. Now, businesses need to enable the same level of self-service for their employees and customers.
Instead of the old model of running “dashboard factories” to answer business questions, today’s data leaders empower everyone to create their own insights with search and AI.
Because that’s the only way. There’s just too much data and too many questions from the business to handle if you don’t let your employees and customers go ahead and find the answers themselves.
Getting hungry for data
Dr. Ansar Kassim
Head of Financial Analytics
Insights are powerful, but on their own, they have limited value. Businesses are built on actions, not information. Instead of relying on people to act on data by looking at dashboards, let’s bring insights into operational workflows to drive smarter actions at scale.
What do we mean by that? We’re talking about embedding analytics capabilities into the tools your teams are already using. With Live Analytics built for the modern cloud ecosystem, insights created in one place can seamlessly flow through the entire workflow without friction.
If you have insights about your most valuable customers, or customers that are most likely to churn, don’t just show them on dashboards. You can bring them automatically into your marketing campaigns and customer support workflows to drive higher value for your business.
Or, with a tool like ThoughtSpot Sync, you can sync customer data into your business apps using prebuilt destinations like Google Sheets or Slack—with no need for API scripts. Say you’re evaluating your annual sales, and some of your growth numbers spark excitement and the need for a group huddle. You just click “Send to Slack” from any live chart, Select “my channel” as the destination, and personalize your post, before sharing with the team.
Suddenly, business insights aren’t just something “out there”. They’re right where you need them, helping you make better decisions, and take action. That’s what being data-driven really looks like in the decade of data.
The new decade of data will be defined by the modern data stack and self-service analytics for all – propelling organizations toward the next level of data fluency.
But you can’t dominate the decade of data by wasting valuable resources on maintaining legacy data pipelines, processes, and operations.
Over the last two decades, companies have learned that there’s no such thing as a perfect data warehouse. Over the last 10 years, we’ve also learned that taking all our raw data and dumping it mindlessly into a data lake doesn’t work either.
When you define your data architecture and models in the cloud, you can make well-informed assumptions about your business needs—but, sadly, many of them will turn out to be wrong. That’s because the world around us continues to evolve with increasing speed.
What you need is a foundation with structure and governance that is flexible enough to be iterated and improved. With modern data engineering and programmable data models, you can build a data foundation that follows best practices from software engineering: extensibility, programmability, reusability, version control, and collaboration. This approach will allow you to meet the needs of today but also help you change rapidly as your business needs evolve.
After all, every company and team has their own use for data. And data volumes are expansive, so old processes and pipelines are breaking. Automation is essential to survival; it is the only approach that will scale as data volumes expand.
In this new context, we’re seeing a new role—the analytics engineer—and a new data workspace.
Analytics engineers take the practices and learnings of software development—things like version control, testing and continuous integration—and apply them to analytics. They’re also leveling up on skills to keep up with the growing demands of their role—doubling down on SQL and adding new languages like Python and new tools like dbt to deliver more value to their teams.
As we fundamentally change how we work with data, we need a new data workspace that will let analytics and data engineers meet their end users' growing expectations.
The modern data foundation needs to be able to:
CONNECT directly to your favorite cloud data platform, like Snowflake, Databricks, Starburst, GBQ, and more.
MODEL your data with SQL and dbt.
OPERATIONALIZE your insights through Reverse ETL so you can push data to business-critical third-party apps like HubSpot, Slack and Google Sheets.
BUILD data models that empower business users to ask thousands of new questions that you didn’t explicitly model.
LAUNCH new use cases and data apps across your organization, quickly and easily.
Bridging the gap between analytics
engineering and business outcomes
Chief Product Officer
A flexible data foundation is exactly that: just a foundation, a starting point for surfacing the insights you need.
While your own data is your intellectual property, to get the most value from it you must combine it with third-party data to build a 360-degree view of your business. You need insights into what’s happening with:
That way, you avoid blind spots and make better decisions. It’s the difference between driving your car by just using a paper-based map vs. using Google Maps, with its constant updates about traffic and warnings about hot spots. Third-party data is like GPS signals you are getting from other cars.
In this decade, third-party data is becoming both rich and easy to consume. With the impending death of cookies, global supply chain instability, and rising customer expectations, analyzing your data in a silo just doesn’t cut it.
While the idea of using third-party data isn't new, the ease and feasibility is—thanks to the cloud and data marketplaces from Snowflake, Databricks, and others.
Third-party data from trusted providers gives you the granular, actionable insights you need to augment what you already know. And because this is all happening in the cloud, you simply get fresh data—without having to deal with brittle FTP or archaic CSV uploads.
In the decade of data, it’s time to rethink all those business applications you’re building. They should no longer just act as systems of record. They should be data-driven apps that enable intelligent operations.
These days, our whole world is made up of data. And that data represents an opportunity to create personalized, actionable insights that drive our businesses forward.
Think of a Fitbit. It doesn’t just count your steps and read your heart-rate. It gives you an overview of your health and recommends actions you should take to keep moving, to get better sleep and to improve your overall well-being.
That’s what your employees and your customers want to see in your business applications. Whether you are building an HR app for recruiters looking for faster insights or an internal supply chain app for managing your suppliers, you can provide intelligence in all your applications using data:
You can help HR manage employee satisfaction and attrition by giving them insights about employee engagement.
You can help your procurement team do a better job of interfacing with your suppliers by giving them a 360-degree view of items and suppliers in the procurement apps they use.
Insights should not be limited to BI dashboards; they should be seamlessly integrated everywhere. In-app data exploration, or embedded analytics, is the new frontier for creating engaging experiences that keep users coming back for more.
30% According to Gartner, the average analytics adoption rate is only 30%.
53% 53% of product builders believe they need to focus on analytics UX.
But building your own interactive analytics experiences isn’t easy. According to Gartner, the average analytics adoption rate is only 30%. Curious users often need to ask a series of questions before they derive real insights. Most in-product analytics experiences don’t support this. They’re based on predefined dashboards that only let the users explore data within certain limitations. That’s why 53% of product builders believe they need to focus on analytics UX.
And that's the exact opposite of what makes amazing product and customer experiences. For users to get the most from your business app, they need analytics to be self-service, flexible, and personalized.
Today, the number of new apps available to consumers is growing exponentially. Yet among that rising competition, engagement and retention rates are low. These products aren’t sticky. Users log in and leave without ever thinking twice because they aren’t getting the experience they want.
As a product builder, you know embedded analytics is the new frontier for creating engaging experiences that keep users coming back for more.
ThoughtSpot Everywhere allows you to build an innovative embedded analytics experience and incorporate any service available in the Modern Analytics Cloud, including search and AI-driven analytics, directly into your apps, products, and services.
In doing so, your teams can launch new products to market faster, saving on time to build and maintain, while giving customers exactly what they’re looking for.
Every embedded analytics experience powered by ThoughtSpot Everywhere is customizable and representative of your brand and your customers’ unique journeys.
So, it’s the defining decade of data. The power is shifting. The new rules are clear. The technology is ready.
The only question is: Are you?
While tech is part of delivering this kind of change, it’s only one side of the coin. More often than not, it’s the other side – people, process, and culture – where transformation stalls.
68% of data teams say they lack adequate time to implement profit-driving ideas.
92% of data workers report that their time is being siphoned away performing operational tasks outside of their roles.
91% of executives say culture is the biggest barrier to becoming a data-driven company
Culture and technology are two sides of the same coin. The hard truth is, any organization running on dead-end dashboards—or worse, static PowerPoints that analysts spend weeks creating—was probably late to the cloud. They probably have brittle ETL processes that rely on a monolithic data warehouse. And this outdated technology reflects a culture of complacency, distrust, and fear of failure.
Modern data leaders champion new ways of working. They put new paradigms like data mesh to work, think beyond their own data, and leverage external data using modern data sharing instead of legacy FTP. They know time is money, and speed to insight is synonymous with success.
These data leaders also recognize that every data project only matters when it solves a business challenge. They structure their teams to work collaboratively with lines of business to embed analytics expertise everywhere. Domain expertise combined with low-code analytics tools is what makes this game-changing. Analysts are no longer order takers — they’re business partners.
All organizations on the leading edge of data and analytics have a culture that celebrates excellence, trust, and necessary risk-taking. There are no diving lines between IT and Business, or Data and Business. There’s just one team with access to the same data, using insights to deliver results aligned with the company mission.
At the helm of many of these organizations is a leadership team who recognizes the digital economy is the only economy — and data is the currency. Often at the helm is the Chief Data and Analytics Officer.
These are not your first-generation data leaders, playing defense and mainly guarding the keys to the data kingdom. These leaders are on the offense — no matter how rough the waters. They are personalizing customer experiences while ensuring privacy and innovating products and services. Everything comes down to propelling the business forward.
The new role of the data
Chief Data Officer
Adopting this new mindset and making all the right transitions is a tall order. You have to navigate rapid shifts in technology, change (and possibly even break) long-standing processes, and influence or even rebuild your business culture.
To take the new decade of data head on, you need technology partners that will walk hand in hand with you, who are as committed to your success as you are.
At ThoughtSpot, we’d love to be that partner. We understand the realities, challenges, and pitfalls facing today’s organizations as we enter the defining decade of data. We pride ourselves on offering customers the ability to influence our product roadmap and a community to connect with peers and exchange best practices. We know that you need to move quickly and that there’s no time to waste on implementation.
Here is what it looks like in action:
92% of data workers report that their time is being siphoned away performing operational tasks outside of their roles.
Instead of using up your data team’s time maintaining pre-defined, static data-to-dashboard pipelines, switching to ThoughtSpot lets business users self-serve their own insights with Live Analytics — no more data remodeling, schema rebuilding, pre-aggregation ,or data movement required. Your data team will have far more time available for creating real business value.
ThoughtSpot’s consumer-grade UX supports even the most non-technical business users to self-serve the insights they need, when they need it. Plus, with Spot IQ, you get one-click auto-analysis of huge data volumes to surface the insights they might not even have thought to look for.
Bridging the gap between analytics
engineering and business outcomes
Global Director of
Senior Director of Data
90% of data analysts report that numerous data sources were unreliable over the last 12 months due to fragmented tools and processes.
That’s not really surprising—fragmented data sources produce fragmented insights. On the other hand, ThoughtSpot lets you live-query all of your cloud data wherever it’s hosted, with no aggregations or data movement required. ThoughtSpot’s cloud-native architecture is built for speed and scale, and it sits directly on top of your cloud data warehouse, natively inheriting all data models and scheme.
With ThoughSpot Everywhere, you can power up your apps by embedding search and AI-driven Live Analytics, customizable charts and tables, and instant insights right into your apps, product, or service.
In short, ThoughtSpot will put your business at the leading edge of the data revolution.
Let us help you dominate the decade of data. Find out why customers have chosen ThoughtSpot to become the architects of the new data-driven economy.