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The Future of Data and Business Analytics

Want more data analytics tools? Done. Want to see the data-driven impact across all your teams? Good luck... In the face of this, an emerging data trend is transforming how business analytics are built & shared within organizations -- by taking inspiration from LEGO bricks.
David Mora
David Mora
January 12, 2021
Data Analysis
Updated on:

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Until now, advances in data analytics have focused on the silo of data analytics itself: new AI methods, better integrations, easier-to-build visualizations, real-time updates.

Put bluntly: we’ve largely built products for those already using data in their job.

But the future of data analytics won’t be better charts or easier to use AI: it’ll be platforms that let anyone stitch them together into data workflows that apply directly to their decision-making. Rather than merely giving analytics teams more power, the future of analytics will integrate data driven decisions into every part of the organization.

How exactly?

Big data trend: The rise of the Data App

The future will bring the rise of the data notebook: Custom data workflows built from discrete analytics blocks. These blocks will make it easy to build workflows that otherwise would have required a whole team of data scientists, developers and software engineers.

list of analytics blocks: data cleaning, database queries, third party integrations and automated workflows

The data analytics steps or blocks will include:

  • Complex data transformation and cleaning
  • Database queries
  • Third party integrations
  • Ways to automate repeatable workflows

Unlike code-based Python or R notebooks, these “data notebooks” will be built visually and be easy to share. Once shared, these data notebooks will allow anyone with access to run them.

Far from replacing data analysts, data notebooks will amplify analysts' impact. The ability to productize their analyses will enable savvy analytics personnel to have impacts on more business units, unlocking organizations’ ability to make truly data-driven decisions across the board.

This article pushes beyond old-school thinking toward emerging innovations built on three ideas:

  1. The way we use data today is broken: it’s siloed, brittle, and slow. We want a future where anyone can build data workflows to fit their own needs.
  2. The field of data analytics produces data features and techniques. But it’s lost sight of the holy grail: to deploy those techniques ubiquitously to enable intelligent decisions.
  3. Rather than bottleneck requests through the analytics team, or piece together specialized tools, analytics of the future will let you compose analytics techniques together to fit your workflow, and do so collaboratively.

This is a paradigm shift in analytics. Where do we even start?

Let’s start where we want to end up: What will this future of data analytics look and feel like?

The Future:

Reusable Data Notebooks

the future of data analysis shown with lego blocks that contain: automated data processes, reusable workflows and collaborative data notebooks

Meet Alejandra. She's a non-technical manager at an online school providing critical support to thousands of students. She loves her job. Her teammates love her, too.

But back in 2021, she spent much of her day reacting to problems -- problems that could have been prevented using her company’s data. Alejandra knew this, but nothing changed.


In 2021, trying to use data for her daily work felt like a gauntlet: chasing down over-worked data analysts, endlessly emailing spreadsheets attachments, waiting weeks for a simple tweak.

But, by 2025, her relationship to data transformed: working with data now felt like building with LEGO bricks. She built data workflows that fit her work brilliantly, and shared them with teammates and clients. And she did all this without waiting on data scientists or developers.

What changed? Data notebooks. Let’s pay Alejandra a visit on a typical Monday morning, and see the data notebooks that have changed how she works...

Data notebook #1: Automated Email Reports

Monday mornings used to be hectic: Alejandra would rush to download, format, and email her colleagues data about key student issues from the weekend.

Now, that full process happens automatically: an email appears in her colleagues’ inboxes every Monday morning, formatted exactly how they need it.

Alejandra designed this automated workflow herself -- it took less than an hour. She created a new data notebook: added a data import block, added a few blocks to reformat the data, and a few others to perform some computation that she used to perform in Excel and Python previously, and finally a block to send the data via email. She scheduled it to run weekly.

Impressive? That’s just the start. Alejandra has built almost a dozen “data notebooks”, and uses many data notebooks built by others. This Monday specifically, she’s about to complete a data notebook that predicts student success, and share it with her team. Let’s take a peek:

Data notebook #2: Predicting student success

Alejandra pulls up an in-progress data notebook in her web browser. Immediately, she sees a clear, interactive document: a workflow that uses AI to pinpoint students who need help. It pulls from multiple data sources. It runs sophisticated data transforms. And Alejandra built it herself.

Something like this would have seemed impossibly complex back in 2021. What’s changed? Two things:

First, Alejandra can easily construct data notebooks block by block, like LEGO. The blocks are easy to reason about: she sees exactly how the data is changing, and can use the data output however she needs.

Second, she's not relying on her own skill alone. Many pieces of the flow were re-used from what other teams or specialists built.

For example, for this data notebook, Alejandra needs both demographic and grading data -- which requires complex SQL queries across multiple databases. Thankfully, since many people need this data, Alejandra’s data science team already shared a “data import block” to do exactly this. She drops it into her data notebook and zips forward.

Unbeknownst to Alejandra, the data science team carefully hid sensitive data from being exposed via their “data import block”, ensuring Alejandra has the data she needs, without the privacy risk.

Publishing the data notebook

Alejandra smiles: her notebook is ready. She hits publish.

When a teammate opens the app to use it, all the technical details are gone: they see just a clear interface ready to run predictive analytics with. Why? Alejandra hid all calculations in the published version of her notebook, ensuring that end users see only what they need. And for more technically minded teammates, Alejandra can give them access to view or edit the full underlying app directly.

Alejandra gets back to work. Is she thinking about stats or databases or machine learning? No, she’s too busy making the world a better place, aided by data tools built for her exact needs.


Repetitive Data Tasks and Limited Data Access

reality of data analysis shown with disconnected lego blocks: repetitive data tasks and limited access

Back to today.

Alejandra is still stuck in an organization ubiquitously dependent on data, but unable to effectively interact with it.

The data that could drive powerful decisions is bottle-necked through a privileged few with the esoteric coding and software skills. Even when data is within reach, repetitive data tasks take up hours of time, or become unfeasible altogether.

Alejandra’s organization is far from unusual.

But how did data analytics end up like this? And what can we learn to change where analytics end up in the future?

How we got here:

The 3 waves of data analytics

the three waves of data analytics: collection, analysis and predictive analytics

On a high level, data analytics has gone through 3 overlapping waves, each with its own cohort of technological advances: Collection, Analysis, Prediction and AI.

Each wave shares a serious flaw however: we learned new ways to use data analytics, but we did little to help integrate data across the whole organization.

In each wave, we overburdened our data analysts, asking them to do the same things over and over again in numerous, disconnected tools. The result? Their impact is limited to a few business units. This was understandable: early tech was only usable by data specialists. But we’ve gotten stuck there.

To understand why, let’s take a closer look at each major analytics wave:

Wave 1: Data Collection

Computers proliferate, and so does data. We focus on capturing and accessing that data. “The cloud” emerges as a core infrastructure. Prominent companies: Oracle, AWS, Snowflake, Microsoft Azure. At this stage, data sits squarely in the realm of specialists, who access it through complex SQL queries & code. Customizing data flows for each individual is unthinkable.

Wave 2: Data Analysis

We’ve collected data, but how do we understand it? Data Visualization emerges as a field in-and-of-itself, and goes mainstream, from journalism to public health (“flatten the curve”). New companies rise up, proliferating words like “dashboard”, “real time analytics”, and “data-driven insights.” These tools aim to answer “Given the data, what happened?” and include: Looker, Tableau, Power BI, the R and Python data science ecosystem.

But despite hype around “data-driven decision making,” few companies effectively enable their employees to use and share data without relying on analysts. It’s true: more people see dashboards; but few see data in forms that directly fit their daily work.

Wave 3: Predictive analytics & AI

The volume and velocity of data explodes. It’s no longer feasible to process data using human review alone, or to look only at what’s already happened. AI gives us the first systematic way to ask “Given past data, what most likely will happen?” and “What should I pay attention to in all this data?” Key players emerge: Intersect Labs, DataRobot, Google’s TensorFlow ecosystem.

Hype reaches a fever pitch. But the Emperor’s New AI increasingly receives troubling feedback: a 2020 survey found only 11% of companies see significant ROI from AI. Once again, adopting analytics techniques proves far easier than integrating them in truly impactful ways.

Looking at where we’ve been, the goal of the next wave of data analytics becomes clear: To effectively integrate analytics organization-wide, beyond just what analysts can do alone.

But how do we do it? Let’s return to the vision cast by the story of Alejandra...

How we get to the future:

Organization-wide and Collaborative Data

The future of data analytics enables anyone to stitch together data analytics techniques in ways that apply directly to their decision-making.

In this future, team members will build & share custom “data notebooks.” These modular “data notebooks” will make it easy for team members to create, share, and pull from custom data analytics methods and pipelines. Unlike the self-contained tools before it, these “data notebooks” will support automation, scheduling, and integration at their core, making them flexible and practical.

The result: data embedded into everyday actions.

Let’s be clear: this is not about making data analytics “easier” or “code free.”

It’s about recognizing that:

If data is an organization's lifeblood, then we need a cardiovascular system, not an IV.

We need data systems that compound & grow collaboratively, focusing less on injecting analytics methods and more on composing those methods into tailored workflows.

Are big data analytics tools doing this already?

It’s true: nearly all major analytics tools attempt to unify analytics methods & make it easier to collaborate. Tableau publishes visualizations and helps clean data; Domo unifies data sources and provides dashboards.

But people don’t need dashboards or clean data. They need data that helps them at their specific role.

When you think of even the first step in that -- getting the data -- you see the failures of existing tooling on full display. Too many tools force you to use a particular database or a data warehouse. This ignores reality: no matter the sophistication of an enterprise, some data is always going to be in spreadsheets, CRMs etc. Forcing use of particular data sources forces out  data that could have powered better decisions.

Stuck in the past waves of data analytics, existing tools focus on the techniques themselves and impose impractical limits. Worse, they limit collaboration to those who can code or have specialized training in a complex tool.

As the dust settles, the data analytics tools of the future will take the opposite approach: they’ll begin with the vision of true organization-wide collaboration and integration of data. Rather than making a technique like AI or visualization "the service", they’ll integrate the many techniques of data analytics directly in service of relevant actions & decisions.

Are you ready for the future?

We’d be remiss to undervalue developments in data analytics, or the work that happens within specialized data teams. Both compose the vehicle that’s brought us to where we are. But the future will be driven by human collaboration and customization, amplifying analytics far beyond what a siloed team could do.

The future belongs to companies who provide their teams a fundamentally better medium for data analytics. A medium that puts collaboration, automation and usability at the center.

Are you ready to be a part of that future?

Join the Future Movement with Us

At Intersect Labs, we're passionate about this future vision.

So passionate we went out and built it.

And, yes, it feels every bit as amazing as Alejandra made it seem.

But don't take our word for it: forward-thinking teams & analysts are already using our platform to automate repetitive data tasks, trigger actions and ship data apps in no time.

Looking to transform how you work with data? Let us give you a taste of the future.

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