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Tag: Business Intelligence

10 Questions about Sigma Computing

Posted on by Fergus Ustianowski

Sigma is a relatively newcomer in the data analytics and data visualisation product space. In this article, Snap’s very own Sigma Certified and data visualisation wizard Fergus Ustianowski answers the top 10 of Frequentyly Asked Questions from our customers.

What is Sigma?

Embark on a transformative journey into the realm of data analytics with Sigma Computing—an innovative tool originating from the United States. This Software as a Service (SaaS) solution stands out with its intuitive, tabular-based interface, promising an unparalleled user experience in the dynamic landscape of data visualisation.

What can I connect Sigma to? 

Sigma excels in data preparation with robust features such as seamless joins, unions, and simplified data cleaning through column edits and calculations. The platform empowers users to aggregate data, create reusable metrics, and ensure enhanced efficiency and consistency. Sigma’s security protocols, aligned with warehouse security roles, ensure seamless transitions across software environments. Sigma does not have the advanced Extract, Transform and Load capabilities for data engineers, but offers extremely powerful data transformation capabilities for self-service users.  

How much does Sigma cost? 

Sigma operates on a user-centric payment model, featuring a platform fee and charges for pro users, offering a transparent pricing structure. Notably, viewer licenses come at no additional cost, making Sigma an ideal choice for organizations requiring extensive viewer licensing, such as those aiming to keep clients informed on project progress. The platform’s intelligent caching mechanism ensures that despite pushing calculations down to the warehouse level, there is no disproportionate increase in warehouse credits, providing cost predictability and efficiency. This unique approach enhances Sigma’s appeal as a budget-friendly solution without compromising on its powerful data visualisation capabilities.

What visualisations am I able to use? 

Sigma offers a diverse array of visualisation types, it offers the expected visualisations of an enterprise BI tool, not dissimilar to PowerBI, but it has interesting graphs like Sankey and extensive geo charts which are not yet standard in PowerBI. Unique features include linked input tables for interactive data insertion, web page embeds, and date range slicers, with an added ability to automatically calculate rolling time periods—a feature often challenging in other data visualisation tools. 

How easy is it for non-technology proficient people to pick it up? 

Setting a benchmark for user-friendly interfaces, Sigma caters to non-technical users with its familiar spreadsheet-like layout and calculations resembling Excel. The platform facilitates the creation of complex dashboards using low-code objects, contributing to its high adoption and retention rates in the competitive data visualisation market. 

What is the AI integration like?

Powered by OpenAI, Sigma’s AI integration introduces a layer of intelligence to data analytics, offering capabilities such as classification, sentiment analysis (opinion mining), and data synthesis (filling any gaps in the data). This integration elevates data interpretation and provides valuable insights for informed decision-making. I found especially the sentiment analysis easy to add rich content to any dashboards. 

How do I share the dashboards? 

Sigma simplifies the process of sharing reports, whether it’s direct sharing within the platform or embedding reports into websites using iframes, Sigma ensures a seamless and efficient sharing experience. Additionally, Sigma’s online portal embedding function allows integration into web pages, enhancing accessibility. This versatile approach accommodates diverse user preferences and ensures that data insights can be effortlessly communicated across various channels. Furthermore, with viewer licensing being entirely free, the platform facilitates widespread adoption and collaboration, making Sigma a cost-effective and user-friendly choice for organizations seeking to share critical dashboards with stakeholders, clients, and teams. 

Why Sigma over other data visualisation software? 

Sigma stands as the optimal choice for organizations seeking a powerful yet user-friendly data visualisation tool. Its intuitive interface, reminiscent of familiar spreadsheets, and a low-code approach make it accessible for users with varying technical expertise, fostering high adoption rates. Sigma’s affordability, approximately half the price of comparable tools like Tableau, provides significant cost advantages, particularly for large-scale enterprises. 

Moreover, Sigma facilitates seamless collaboration, allowing team members to work concurrently on impactful visualisation. By replacing complex DAX code with a user-friendly approach, Sigma streamlines dashboard creation, empowering users with diverse skill sets. In essence, Sigma delivers cost-effectiveness, collaboration, and accessibility, making it a comprehensive competitor to PowerBI. 

How do I find out more about how Sigma can with my data analytics and visualisation requirements? 

Snap Analytics provides unbiased advice about which data & analytics tools are the best match for a specific customer, based on their unique requirements. Analytics is our core business, and we have a long track record of success with a wide variety of applications. Snap is a Sigma partner, and we have certified Sigma consultants who can help you demonstrate the value of Sigma for your organisation. Get in touch with the Snap team to discuss how we can help making your data & analytics journey a success.  

Be a Data Hero and deliver Net Zero!

Posted on by David Rice

The biggest problem in the WORLD!

It is clear that we need radical changes to save our planet. Governments, the private sector and individuals aspire to achieve ‘Net Zero’ – but radically changing the way we operate is not going to be easy.

Achieving this goal is going to be a huge challenge for big, complex organisations.  There are so many areas to explore, from reducing travel and fossil fuel consumption, leveraging renewable energy, improving efficiency of existing equipment, or simple behavior change.  With so much complexity the task can be daunting. 

Can data save us?…

Starting with data can help you to understand where the quickest and biggest wins are.  This helps you to understand what to focus on first.  As Peter Drucker once famously said “You can’t manage what you don’t measure”.

To create a link between desired outcomes and measurable targets you can use a ‘Data Value Map’. Whilst I love technology and data…it’s only useful when it drives actions and creates positive change.  The Data Value Map helps to visualise how data can help you to achieve your goals.  If your goal is Net Zero…it could look something like this:

Data Value Maps can be achieved using a mind mapping or collaboration tool (I like Mindmeister and Miro) and are best done as a highly collaborative team workshop…don’t forget to bring the coffee and cakes!

Now you have a clear view what data is required to measure and act (your “use cases”) to deliver the Net Zero goal.  Next you can score these in terms of Value and Complexity.  Something like a prioritisation matrix can help:

By focusing in on the ‘high priority’ and ‘low complexity’ use cases you can deliver quick wins to the business.  This will help you to demonstrate you are a true Data Hero and can help your organisation to fly!

Once you have prioritised your use cases, you can start to map out the underpinning systems and processes that are needed to deliver connected, structured data to drive your Net Zero goals. 

Delivering at lightning speed…

There are numerous technologies out there that can help you connect all of this data, but we love Matillion for being able to easily and quickly connect to almost any source and transform and join data to make it useful.  As a data platform Snowflake is fantastic for virtually unlimited storage, blistering speed, data warehousing and data science capabilities.  These technologies will certainly enable you to hone your capabilities as a true Data Hero!! There are also many other fantastic cloud solutions that can help you to supercharge your Net Zero data capabilities.

Join the Data League!

Snap Analytics’ team of Data Heroes are helping one of the UK’s largest food manufacturers to leverage data to drive positive change…but if we’re going to solve humanity’s greatest threat…it’s going to take a whole Justice League of Data Heroes.  So join us on this mission to save the planet, and lets all make sure the decision makers in our organisations have the data they need to drive positive change.  Don’t delay…be a Data Hero today!

We believe that businesses have a responsibility to look after our earth…it’s the only one we have!  We will give any organisation a 15% discount on our standard rates for any work directly linked to making a positive change to the environment!

How to get buy-in for your data project

Posted on by David Rice

Despite the talk of a data driven revolution, the reality for many companies often lags someway behind the ideal of a business built on reliable detailed information analysed using AI. According to a Mckinsey Digital report on leadership and analytics, CEOs cite their biggest challenges to investing in data are “uncertainty over which actions should be taken” and “lack of financial resources.” Fundamentally, it appears that some business leaders still don’t believe that analytics have a high enough ROI.  

If you know that a data project could deliver huge value for your business but you’re struggling to get anyone else to appreciate the importance of all that expensive tech and numbers nonsense, here are a few ways to help change their mind. 

Speak their language 

There’s no point trying to convert anyone into data evangelists at this stage, instead work within the parameters of your organisation. Align your project to existing business priorities and show that you understand the CEO’s strategies. The best way to do this is to create a link between the results of your project and the financial benefits. Get those graphs at the ready! 

Remember that this is a business transaction and not a technical pitch. You’re wasting your time if you can’t convincingly demonstrate that you are addressing a particular business need, so resist using too many technical terms. Instead, explain using visuals which demonstrate that the outcomes from your data project align with the strategic objectives of the business and will bring tangible benefit to the company and the individuals who work there. 

You may need to convince people that you are addressing a demand that they didn’t even know they had. By the end you want not just buy in, but for them to believe that it was their idea all along! 

Recruit key players 

It’s not enough to expect the techies to wave a magic wand and sprinkle stardust over the business. Successful data projects are a partnership between the project team and key business users who work with the numbers on a daily basis. Don’t expect that a diktat from the 17th floor is going to be enough to drive them into making this thing a success, it’s your job to involve as many key players as possible, make them feel they’re being listened to and that they have some control over the direction of the project. 

Throughout the course of any data project, key business users should have frequent and regular opportunities to provide feedback. This agile approach will help to ensure that the solution is actually what the business wants – unless they are able to see the solution in action it’s impossible for them to really know what they want! An added bonus is that they will feel that they have shaped the project and will have a vested interest in the outcome. 

Recoup your investment 

From the C-Suite down to the office floor, what everybody wants is something that makes their work easier and the business more successful. If you can demonstrate that what you are doing is going to achieve tangible results they will sit up and listen. For example, a report which currently takes 5 hours will take 1 hour as a result of this project. And if that report is created by 100 people at a cost of £50 per hour, the bill drops from £25,000 to £5,000. 

To hammer that point home, McKinsey carried out analysis over a five year period which showed that companies who put data at the heart of their operation enjoyed marked improvements across all departments, with sales and marketing ROI increasing by 15%-20%. Investing in creating a data driven culture is vital for the growth of any business determined to stay ahead of the pack. 

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.

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