Data and analytics are driving the next industrial revolution

In the second of our series with Stuart Deignan, Managing Director, UK at Globant, we find out why he feels that data and analytics are driving the next industrial revolution and what this could mean for businesses of all sizes.

Why does Globant believe data and analytics are driving the next industrial revolution
As we move further and further into digitized economies, more processes become digitally native (like digital banks), digitally triggered (like additive manufacturing) or, at the very least, digitally sensed (as in digital twins). So, it’s not like there’s a different space or dimension called data, and some industries can participate in it or not, but rather the new way in which any business will operate in the digitized world.

Now, whether as a sub product of that transformation, or as it’s driver, data and analytics provide a very granular way of seeing and acting in the world, in terms of the level of detail about something, or the frequency of new information about it. That is the substrate over which a lot of new technologies, techniques or advancements can be applied. If we are talking about leveraging AI, there is effectively the precondition of data to train and execute its algorithms, but it’s not only on that end. Proper analytics is about understanding what’s happening, being able to ask the right questions, and learn very quickly with a short feedback loop. The biggest advantages can grow from making those data-driven decisions instead of using hunches or preconceptions, many times rooted in experience and expertise.

But there is also another dimension, which is that data is not so much “like oil”, as it has been said, but rather something that can be turned into a capital asset, with unique properties that can give an edge to industries, in operational efficiencies, in new ways of generating value, or even in how to monetize the data itself. Data and analytics are driving the next industrial revolution because smart decisions and new insights are the required ingredients to make a reality of any grand vision a company may have.

What will happen to businesses that continue to ignore data
At some point it will be akin to ignore electricity, or to ignore the internet, or something similar. It will be so ubiquitous that we may not realize it’s always there. At present, companies may find that not investing in data may leave a bit of slack in terms of their capital, but they would be losing sight of efficiencies and opportunities that compound over time.

Companies that ignore this dimension will find it increasingly hard to compete, and increasingly costly to catch up. Beyond the technological components that evolve all the time, there is an organizational culture and knowledge around how to work with data that requires time and maturity. These companies will get to a moment where they will need to grow up very fast, and the growth pains might be more than they can handle.

Fortunately, it’s not too late, and it is easier than ever to get started or to keep evolving these aspects within companies, as open source technologies and companies have offerings, as we do, around helping companies with their data strategies and executions, or providing that analytical support so businesses can focus on their core value.

How are companies (particularly SMEs) supposed to find the time and resource to identify meaningful outcomes from data and analytics
It’s never easy, because they will always have everyone over-extended with many different hats and needs. There are a couple of things to take into account. One is that there are many platforms and tools that can help integrate the data and analytics workflow within their current way of working, such that it becomes a stream of work or feature within their own operations.

Another point is that not all analytics need to be done in-house, and there are on-demand approaches to it. But there is a need for a good amount of clarity on how and why they decide to tackle it. Just hiring a data “something” (sometimes data scientists, sometimes data engineers, sometimes business intelligence) profile without proper support, ownership or objectives doesn’t work. The same goes with hiring a very senior advisor without executive capabilities. It is a “not one size fits all” arena, and there is a sensible approach for every business!

Are there ways that the processing of insights can be made simpler for the benefit of busines
A good data strategy and data product design can help a great deal. You can speed up the time it takes, you can reduce the work or complexity of processing them, but there is no self-driving analytics that can solve your business needs if you are not sure what the relevant questions are.

A key component will always be to focus on what’s important, on the need-to-know rather than hey-let’s-see. Optimizing a process that shouldn’t exist is twice the spending. Beyond that, while many businesses may have very unique insights and KPI needs, the starting point should be on common metrics and trends of each industry, where many open sourced or commercial solutions can give a head start with minimal customization.

I believe maybe the most critical factor is that the information processing, gathering and delivery should be automated as much as possible, hence a well-designed data platform is a great analytic ROI in the mid and long run.

What will the next industrial revolution look like
This is a tricky one. We must not forget the old Danish adage, it is difficult to make predictions, especially about the future. We see some trends though, where decentralization of processing and ownership can make autonomous organizations possible, for instance the developments in web3, DAOs, DeFi and the myriad of technologies and concepts that stem from blockchain.

Also the massive adoption of additive manufacturing and other ways to achieve manufacturing at scale, which can enable innovation in the physical real closely tied to the digital one. And there’s a big outlook on metaverses, as the next frontier of digitalization. It’s all very incipient, and it’s all about data behind the curtains, but the seeds are there, and we don’t yet know how deep the roots will go.

What are the current trends with data and analytics
Privacy, governance and bias are prominently touted, and rightly so. The technologies and capabilities that stem from data grew much faster than our structural capacity to cope with them, and we are playing catch-up now. Beyond GDPR and similar regulations, there is a wave of algorithmic accountability, whereas attempting to regulate uses of AI to define accountability in a business for the results of any process that uses AI in it.

On the enable side, there are always discussions about the big patterns of what used to be called big data, like data meshes, data lake-houses, and other approaches to effectively handling the classical V’s, volume, velocity, and variety. When you weave in governance, ownership, stewardship and other considerations to make sure that the data itself is relevant, legitimate, fresh and even monetizable, then you have this evolving breed of technological specializations.

On the other end, there are modern data visualization solutions, many of them open sourced though not all, that simplify the exploration and presentation of the data in interactive, and some even say semantic, ways. Relevance, simplicity of interaction, simplicity of presentation, and triggering actions are the ways to win the game.

How powerful has data become over recent years
We’ve been exposed to several high profile cases alleging electoral influence, health misinformation, conspiracy theories and the like, and those are the tip of the iceberg that show the power that dealing with data involves.

But when we look at the flip side, the innovative, constructive uses, we’ve seen improvements in efficiencies, capabilities to reach previously misrepresented populations, better understanding of problems like the environmental change and many, many more. Data is a strategic skill for any company at the moment. It will be a necessary form of capital in the future.

How important is data and analytics to Globant
Important enough that we seek to reinvent our own industry through data-driven innovation to our own way of working! From tools to code, test, understand talent, drive internal processes, and more, we are always developing and experimenting on new algorithms and data products to keep and increase our edge over our competitors. And when we look to our clients, we see the need to evolve and improve on data sufficiently enough that we have a tremendous footprint on talented specialists around the world, and we are working on increasing that capacity in the UK and the region particularly.

What does the business predict for the future in relation to data and analytics
There is a growing awareness of the concept of a “data product”, and an explosion of offerings in terms of commercial offerings as a service in terms of data platforms, MLOps, business intelligence and custom visualizations, versioning and governance tools and more.

We know that at some point, the market will oversaturate, and some common standards will foster interoperability, driving mergers and a simplification of the landscape. We do not know which products will survive, as there is a very exciting exploration of the different ways to approach data needs, and we may yet uncover something that we can’t fathom how we did data before it.

Another trend is related to autonomy. Bringing analytics closer to the operation to drive better, faster decisions in a decentralized way, and increasing data literacy in practical terms, that is, people actually using data products, rather than taking a course and still relying on hunches.

Can you provide any examples of how data and analytics have supported your own or your clients’ businesses
We use data to find, recruit and nurture our talent, which is the lifeblood of Globant. But we also use it to increase our efficiencies and reinvent the way the industry works!

Speaking about clients is always tricky given that data is linked to very strategic uses, but we can say that we have used data to help e-learning companies improve their engagement and educational efficacy, improve operations and supply chain on global retailers, help computer assisted design companies get a grip, and optimize, on live information about construction sites, helped pharma companies on streamlining diagnosis and improve physicians workflows, and much more.