snap-analytics-logo
Book a Call
snap-analytics-logo

Tag: Machine Learning

Your Enterprise Data Isn’t Aligned and It Shows 

Posted on by Luka Jasionyte

Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Raj Shah, Director of Strategic Accounts, to dive into his journey in data and analytics and explore the key challenges facing enterprise businesses today. Raj shares his insights on making sense of enterprise data, tackling data silos, the importance of strong governance, and how AI and high-quality data engineering are shaping the future. 

What inspired you to get into data and analytics, Raj? 

I was inspired by the opportunity to work across a variety of clients and industries, as data exists in every organisation. The business value that proper analytics can deliver feels almost magical, and although I didn’t enjoy programming, I found SQL surprisingly easy to write and understand.

What is the most exciting trend right now? 

AI, without a doubt. It has really focused everyone’s attention on getting their data right. It’s no longer something organisations can put off while continuing with manual or Excel-based analytics. AI depends on high-quality data, and ‘human-in-the-loop’ AI is the stepping stone for most organisations. Those that fail to embrace it risk being left behind.

What are some common data-related challenges that large enterprises typically face? 

Large enterprises often face several recurring data challenges that impact their ability to deliver reliable analytics. One major issue is the lack of a common data definition or language across the organisation. For example, a term like ‘margin’ can mean different things to different departments, leading to inconsistent reporting and decision-making. Another challenge is the reliance on flat files and manual data processing methods. These approaches are time-consuming, error-prone and make it difficult to scale analytics effectively. Excessive data transformation and manipulation often happens within front-end tools rather than being pushed down to the data warehouse. This creates inefficiencies, performance bottlenecks and governance risks.

What are the top client priorities for those looking to drive successful outcomes in data and analytics? 

For clients aiming to achieve successful outcomes in data and analytics, several priorities stand out. First, making data a true business asset is essential. This involves consolidating information from multiple systems into a clean, unified data warehouse that provides a single source of truth. Second, building data literacy across the organisation is critical. When teams understand and trust the data, they can make informed decisions and fully leverage analytics capabilities. Finally, reducing reliance on manual processes and Excel-based reporting is a key step towards scalability and efficiency. Moving to automated, integrated solutions not only saves time but also improves accuracy and enables advanced analytics. Together, these priorities create the foundation for delivering actionable insights and driving measurable business value.

Raj, why Snap Analytics? 

Clients choose Snap because we combine deep industry expertise with technical excellence. Our consultants have broad experience across the modern data stack, enabling us to design and deliver solutions that meet diverse business needs. We also bring specialised knowledge of complex systems such as SAP, supported by proven frameworks that reliably extract data and integrate it into modern cloud platforms. This ensures accuracy, scalability and speed. Snap focuses on delivering real business value. Every project is driven by outcomes that matter, whether that’s improving decision-making, reducing costs or accelerating innovation. Our approach uses lean teams, automation and reusable frameworks to achieve efficiency, standardisation and strong governance, giving clients confidence that their investment translates into measurable results.

Why AI is a lot like teenage sex – and how you can get better at it

Posted on by David Rice

In 2013 Dan Ariely, professor of behavioural economics at Duke University, got the analytics world all aquiver when he stated: “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” His risqué comments appeared in the midst of a data revolution, the experience of which was less than satisfactory for many companies.

Six years on and we’re doing it better. However, AI is in danger of being in the same position as Big Data was six years ago. Some data strategists would have you believe that AI and its bedfellow machine learning are better than sex. But as with anything in the data revolution, don’t expect to achieve total fulfilment overnight.

Cost vs kudos

A report from the McKinsey Global Institute predicts that early adopters of this technology could grow the value of their business by 120%. It adds that those who fail to jump on board the AI gravy train could lose a fifth of their cash flow. No surprise that companies are throwing money at the problem – but not necessarily always for the right reasons.

Kudos is not enough of a reward for a business to pump considerable sums of money into an AI project. A survey by the US analytics company Figure Eight showed that the majority of companies were spending at the very least $50,000, rising to over $5 million for those serious about making it a central part of their business.

AI has now been around for two decades. Advances in tech plus heavy investment from the likes of Google mean that the cost of leveraging AI tools will continue to fall, levelling the playing field and allowing smaller businesses to utilise AI. If you’ve made it your business to amass a wealth of clean, properly managed data you are already well positioned to launch an effective AI project.

Let’s not get ahead of ourselves

With any data project, there are things you need to think about before you get started. If you’re looking to get started with AI specifically, first consider whether the problem you would like to address is best served by the technology. Don’t expect AI to act as a sort of panacea; you need to be deploying it in the right way and for the right reasons. If you’re unsure, talk to an expert first (yes, we can help with that) to assess what sort of data analysis would be best for your particular problem.

Maybe there is an area of your business you are certain would benefit from an AI solution – if only you could convince the CFO to invest. If they’re keeping a tight hold of the purse strings ask yourself: does this align with the greater corporate strategy? If no, you’d probably be better focusing your efforts somewhere that does.

Finally, when you have identified the right AI project and hired yourself a crack analytics specialist (hello), don’t assume that the thing will just run itself. AI is smart but it still needs help. That means putting together the right team – and not just a couple of people borrowed from the IT department. Successful AI needs buy-in from people who understand the business need and who are working with the numbers on a daily basis.

Get it right and you’ll transform your AI experience from a meaningless one night stand to a satisfying relationship that grows into something really special.

From wayfinding to driverless cars – explaining the analytics maturity curve

Posted on by David Rice

Once upon a time when the world was young, people got around by remembering landmarks, looking at the stars and making the occasional lucky guess. For the most part they didn’t have far to travel so taking a wrong turn here or there did not mean getting lost forever. Until recently, the business world was a bit like this too, with people relying on assumptions about their customers and acting on hunches based on past experience.

But now we’re living in a globally connected society and operating in a sophisticated data driven landscape where chances are, if you rely too heavily on your nose and just hope for the best you’re going to get badly lost. Thankfully analytics can help, whether you’re tracking sales or avoiding traffic jams in an unknown neighbourhood.

The process exists on what we call a ‘maturity curve’, a four part journey which takes us from the most basic statistics to a process driven entirely by AI. Understanding the different stages will give you an idea of how the business of analytics works and will help you plot a course for your business. Gartner’s model helps to visualise the analytics journey:

Descriptive: Say what happened

One day people got sick of walking through the woods, taking a wrong path and stumbling across a sloth of angry bears. After returning to their cabin and counting their remaining limbs they decided to begin to chart those woods and eventually the rest of the world around them.

Diagnostic: Why did it happen?

Without accurate maps, unpleasant bear encounters seemed inevitable. But once people began to join up all their fragments, accurate maps began to appear. People got lost far less and the bears were left to get on with whatever it is that bears do.

So it was in business that people began to make accurate records of their sales which they used year on year to measure growth and diagnose where their problems were. In data analytics this is known as ‘descriptive analysis’ and it is the bedrock of understanding your business.

Predictive: What’s going to happen?

The paper maps were all well and good but what if you hit road works and need to stray beyond the confines of your usual route? SatNav provided the solution, removing the need even for basic wayfinding skills – it simply tells you where to go.

This is how the second ‘predictive’ stage on the maturity curve functions. It combines the historical (descriptive) data with current variables that may affect your business, things like weather or an influx of tourists; it then accurately predicts how your business will fare in the months and years ahead.

Prescriptive: What do I need to do?

Now you no longer need to worry about how to get somewhere and your fancy SatNav can even tell you what time you will arrive. The next stage involves removing the need to even engage in the mechanical process of driving as all that crucial information is accessed by a driverless car that makes all the key decisions for you. Traffic jam forming up ahead? Sit back and relax while it swerves past the accident takes you the scenic route through the woods (don’t forget to wave to the bears).

The final ‘prescriptive’ stage of the maturity process offers you the ability to hand over more and more business decisions to AI. So, for example if you sell ice cream, the data will look at the weather forecast and automatically send extra stock to shops in areas where there is a heatwave. And when you reach the top of the maturity curve the system can be set up to read a huge variety of cues and make automated decisions right across your business.

In analytics – as in life – there are no shortcuts to reaching the top of the curve. It is a long and sometimes difficult journey. But thanks to technology it is becoming increasingly rewarding, if done right.