Data Insight

Regardless of your business model, a forward-thinking data management strategy will determine the future of your enterprise.

Data Analytics

Data Analytics means different things to different people. Simplified, it means: inspecting, cleansing, transforming and modelling your data in a way that makes it easy to uncover the patterns and vital information to support decision-making.

A higher-level description can be: the use of business intelligence, machine learning and predictive algorithms to push data through tools which forecast options and opportunities to achieve business growth, productivity improvement, cost savings, risk mitigation, or for regulatory or legal compliance.

One thing we know from experience –
the more you see, the more you will want to know.

And that’s why it’s important to configure your analytics platform specifically for your operation from the beginning.

The first step in creating a successful data analysis platform is to outline what you need data analytics for, and to clearly decide what you are trying to achieve. That’s why we always begin with a detailed discovery by forensically looking at your existing landscape, and speaking with all internal stakeholders to understand current issues and what they hope to accomplish.

Most businesses use a variety of software and applications which record and store data. There are myriad tools, APIs, plugins and other apps which can be used to bring it all together, transform, and then dynamically deliver the answers to your questions.

Key to success is engineering of raw ‘computer-friendly’ data into ‘business friendly’ data. This includes naming conventions to convert acronyms, formulas and computer code into plain language – and more importantly applying business logic to ensure you’re seeing the entire truth.

There are no limits to what your data can track and report on.

Infrastructure services, utilities and construction companies use analytics to improve processes; manage assets; control preventative maintenance and life expectancy / redundancy of mission critical components; map regulatory obligations; and manage their projects and work force more efficiently.
If your company relies on selling products or services, you can track product popularity; monitor operational efficiency; track revenue growth or decline; monitor sales cycles; understand consumer trends; measure customer satisfaction; trigger stock clearances or sales events and much more.

A connected data analytics ecosystem will reveal trends and metrics that may otherwise be lost in a mass of information. However, if you don’t have the foundation right, you’ll never be able to build anything worthwhile on top of it.
Our multi-disciplined team of data specialists will help you get accurate reliable reporting to deliver real and meaningful insight that you can use – from a system that you are confident you can trust.

Whether you’re just beginning, or have an existing system to improve upon, book a call and let’s see if we are the right fit.

Data Visualisation

Data Visualisation is the graphical representation of information and data so everyone in your team can easily access, read and understand the trends, outliers and patterns in data.

A whole lot more than bar charts and pie charts, modern data visualisation is an expert skill which uses a graphic design interpretation that brings the story your data is telling you to life.
Our eyes are drawn to images, colours and patterns and we all know that our culture is becoming more and more visual.

Using images, animations and video, our visualisation team can represent your data in ways you never imagined – and certainly in ways which will engage your stakeholders in the process of understanding the value of data analysis and how it affects everyone in the business, from the factory floor to the C-Suite.

And because these visualisations are dynamically linked to the story the data is telling, they are orchestrated in a way to help your key people gain a rapid understanding of what needs to happen next.

From reports to statistics, alerts to action items - inject a little excitement and variety into your data.

Data Engineering

Data engineers design the umbrella data infrastructure which collects, stores, and analyses data to provide accurate and timely insights for just about every industry. They work with you to discover and apply specific business rules or make your data more user-friendly.

More than this, their role is to design, build, test, blend, manage, and optimise the data, often from a multitude of disparate sources.

When engineered properly, data becomes more than itself.

Engineering often begins with a blank canvas and is scoped to deliver on any idea that your business stakeholders have.

Consider information about your customers. You may have a different system or software for:

If these datasets are independent from one another, getting a wholistic picture of that customer is very difficult: what orders resulted in the highest customer support cost; is this customer an A, B or C client (do they deserve a special offer or are they costly to service). Combined, this data quickly delivers a comprehensive view of your customer – enabling you to understand how better to make them loyal.

When you’re not all singing from the same song sheet

One of the primary reasons to involve a data engineer in your project is to get your tools to connect to each other. Completely different computer language is used depending on where, and which programs are storing your information.

It’s common for different business units to have legacy software and information storage systems that the team is familiar and comfortable with. If you want it all brought into a common language to speak to your analytics dashboard, you need a Data Engineer.

The engineering process encompasses pulling raw data out of source systems, then collating, validating, and modelling so it can be called upon to retrieve and deliver any combination of business logic – and express the results in a user-friendly way.

Well-engineered data becomes more than its component parts. Where there is a data field for A & B but not C, the data engineer will configure the information in A and B to direct it to the right report for quick and easy analysis, and to show what needs to take place next. Here’s an example for a services company:

A = service interruption
B = maintenance call to repair
A+B = C (directed to operations)
A = service interruption
B = maintenance call to repair broken component
A+B = C (direct to asset management)

Today, even small organisations are collecting a huge amount of data, and to put it to good use, they need the right framework to ensure it is in a usable and bug-free state by the time it reaches data analysts and scientists.

Why not speak with a specialist today and get the ball rolling.