Learn how technology in the analytics space has changed over recent years – and how you can make sure you have the right tools for the job.

In our recent blog covering the five pillars of a successful analytics programme, we talked about the importance of choosing the right technologies and platforms to support your analytics projects. 

This is something we see a lot of organisations struggle with, and for good reason. There are two primary challenges: First, the options available on the market are numerous, leaving IT departments with a wealth of on-premises, cloud and open-source  technologies to choose from. 

Secondly, the rate at which these technologies develop is astounding. And that means investments must go some way to ensuring capabilities are future-proofed – otherwise the whole process of choosing and implementing new technologies will start all over again; sort of like a tech version of painting the golden gate bridge.

In this blog, we’ll take a look at how the analytics tech world has changed in recent years. And talk about the things you should keep in mind when planning your investments.  

The ever-evolving world of analytics tech

Technology is ever-present throughout the analytics lifecycle with a range of tools needed to store data, process it, conduct analytics, and present the results to decision makers. In recent years, every stage of this process has changed dramatically. 

Data warehousing, for instance, was traditionally conducted on-premises, with tools provided by the likes of Oracle, Informatica, Teradata and Netezza. Today, the volume, variety and velocity of data has resulted in using big data technologies to a larger extent. 

Similarly, as companies seek greater scalability, more speed, and simplified data management, a lot of the analytics workload for many organisations has been moved to the cloud. 

The cloud provides many advantages as a computing platform in general, and several when it comes to the specific application of analytics. 

In the last decade or so, the rise of open-source software has driven another big change in the analytics tech landscape, especially with regards to data processing, algorithm development, machine learning and visualisation. For these use cases, tools like R, Python, Streamlit and Plotly, now offer alternatives to traditional vendor solutions. 

These tools not only offer a more cost-effective alternative to proprietary analytics programmes, which lowers the barrier to entry for analytics work, but also provide greater flexibility that can help you avoid becoming locked in with a specific vendor.

In fact, the growth of these platforms actually provides a great deal of room for customisation, allowing you to mix and match capabilities from different vendors to create a solution that meets your exacting requirements. 

The speed and depth of these changes over the last decade or so is a reminder of how important it is to look ahead and monitor changes in the market when choosing your technology investments. But it’s also an indication of how hard it can be.

If you’re aiming to gain real-time insights and be adaptive in your environment, you need the right capabilities to support that. And that requires the right insights into the tech market.  

A brief comparison of how tools and technologies that can be leveraged for analytical projects have evolved

What does the wrong tech look like?

Picking the wrong technology platforms for your analytics programs can stifle your ability to gain value in any number of ways. It can slow time to insight, place a strain on your budgets and IT resources, and limit the ROI of your analytics investments. 

Abhishek Rauniyar, Associate Vice President of The Smart Cube, gives an example: “One of our clients in the retail sector, had a conventional data warehouse, where storage and compute sat on the same layer,” he says. “In this instance, when data is ingested it then becomes impossible to conduct analysis at the same time – and that means accessing real-time insights becomes impossible, too.”

In this instance, we helped our client move its data warehouse to a cloud infrastructure, where it was able to decouple storage and compute and conduct both data ingestion and analytics at the same time. 

It’s all about finding the right tools for the job. But the real question is, how do you know what the right tools are? 

A checklist for success

Staying on top of the advances in these tools and technologies can be a difficult task, and it’s something our experts at The Smart Cube dedicate a great deal of time to. That’s why companies in all verticals and across all regions turn to us for advice and assistance. 

To help with your initial considerations, we’ve put together the following four-point checklist.

1. Is your chosen tool future-proof?

This may seem like an obvious question to ask, but we often see clients opt for a cheaper alternative to the latest and greatest tech in the hopes it will save them money, only to find themselves spending more money upgrading or replacing that tech later down the line. 

For this reason, it’s important to make sure that your chosen solution isn’t at risk of obsoletion. And also, to ask yourself if it sets your organisation up for something beyond just short-term success. 

2. Does it make sense from a resource, skillset, and availability perspective

Any good analytics investment is aligned to a specific use case. So, before you do anything else, consider what questions you’re asking and what is required to answer them. It’s important to avoid just investing in the latest capabilities when you may not need them.

It’s also vital to consider your resources in terms of the skillsets you have in house. Do you have the required expertise to work with these systems? If not, can you outsource effectively to make sure you do?

3. Is it the optimal investment from a cost perspective? 

The next consideration is to make sure you’re getting the most for your money, meeting requirements without over-investing. A good tip here is to look at some of the SaaS (Software-as-a-Service) options on cloud platforms, which ensure that you pay only for what you use. As a bonus, these solutions are almost infinitely scalable, which means you can add extra resources as and when you need them. 

4. Does your solution provide maximum speed to insight?

Finally, if real-time insights and agility are important to your organisation, make sure your chosen analytics platform provides them and is capable of enabling fast decision-making and providing genuine competitive advantage.

In fast-moving environments, your insights need to provide information into what’s happening right now, as anything else may lead your decision makers astray. 

Data analytics at The Smart Cube

At The Smart Cube, we work with organisations across all verticals and geographies to make sure their analytics investments provide maximum returns.

We work with the world’s biggest vendors as well as building custom solutions to meet our clients’ specific needs. And because we’re vendor agnostic and not tied into any partnerships, every solution we create is based on what’s best for the task at hand. 

If you need help or guidance with your analytics investments, get in touch.

Co-authored by: Abhishek Rauniyar and Raman Sharma
Co-authored by: Abhishek Rauniyar and Raman Sharma