5 reasons why mentoring accelerates your data analytics projects

By: Ancoris says
Tag(s): Data
Published: Mar 24, 2021
5 reasons why mentoring accelerates your data analytics projects

Cloud computing is changing how we tackle data analytics. Organisations of all sizes and across all sectors are now able to become more data-driven thanks to affordable cloud-based solutions that can be implemented quickly. But there’s still a learning curve to mastering these technologies and applying them effectively to deliver business benefits.

That’s where the mentoring that Ancoris provides during data analytics projects will make a big difference to how quickly you can achieve your objectives. 

One customer that has seen the impact is Play Sports Network, the world’s largest cycling media company and community. It brought in Ancoris to help build a recommendation engine for its GCN social media app.“Our data scientists are extremely capable but they’re a very small team,” explains David Taylor, technical lead at Play Sports. “The training and mentoring Ancoris has provided around tools and best practices has been just as useful as the technical solutions we’ve developed. It’s saved us from making a few mistakes and taking longer to figure out how to use what’s available in GCP.” 

In fact, Taylor estimates, mentoring from Ancoris over the course of the three month project has allowed the team to save a year of work and also develop the infrastructure needed to support its plans for the next two years.

So how, exactly, does our mentoring-based approach make a difference? We can help you:

1. Understand what’s possible

We don’t just explain what technologies and tools are available but listen intently when you talk about where the pain points are in your business. We’ll then come back to the table with imagination and concrete ideas — and maybe even a fast prototype that everyone can touch and explore — to show you how cloud technologies can change the way your business performs.

Take the example of a workshop we ran for one client interested in using Machine Learning models to analyse free-text documents. We compared using “traditional” coding to build a model with using Google Cloud AutoML, which automatically derives models from training data. The company's in-house team of data scientists commented that the workshop “provided great insight into our data with all the supporting details — from the machine learning fundamentals to a productionised workflow — allowing us to immediately put the improvements into action.”

2. Identify a suitable proof of concept project

We understand how important it is, at the start of any long-term data initiative, to get early buy-in from influential decision makers and prove the viability of the technology — while also ensuring you set off in the right direction for long-term success.

We can help you determine which projects can be implemented quickly — but still have a high-value business impact — and lay the groundwork to address privacy and governance requirements.

This is what we did for our customer Causeway Technologies, which provides cloud solutions to the construction industry. Working with them, we were able to create an initial solution — in just a a few weeks — that helps everyone in the company understand how clients are engaging with the company’s products. “From a business perspective, this was data the organisation was crying out for,” says Patrick Locke, product owner for data and analytics at Causeway.

3. choose the right tools, technologies and techniques

Google’s core data analytics platform, BigQuery, sits at the heart of a wider ecosystem of storage, advanced analytics and presentation tools that let you extend the capabilities of the core platform in a variety of ways.

We can help you weigh up your options, giving you insights into the strengths and weaknesses of each tool and how they map to your specific needs — in both the short and long term. For instance, as part of our work with Play Sports Network, we introduced their data scientists to a third-party tool called dbt for data ingestion. “We’re so impressed by dbt and how easy it makes it to set up data pipelines and schedule them to run as often as needed that we’ll be using it in other projects,” says Taylor.


Modern data analytics platform - 7 rules for success


4. understand how to best organise your data

Modern data analytics platforms make it easy to ingest massive amounts of data, but you need to store it in a way that makes it fast and efficient to exploit.

Imagine having all your saucepans stuffed into a kitchen cupboard and having to move them all every morning to find the pan to make your breakfast porridge. Wouldn't it make life easier if you put the pan you use most often at the front? But it takes skill and knowledge to transform cleansed data into the right data assets to support a particular organisation’s needs.

For example, it’s usual to organise data assets in the presentation layer according to the business process they support (such as sales, logistics, marketing and operations), with a general pool of common assets such as customers, employees and dates. For Causeway, however, we advised them that it was more appropriate to group assets around each of the company’s cloud products, rather than its business processes. 

5. Get answers quickly when your team are stuck

Above all, our mentoring helps you stay focused on deliverables rather than getting bogged down in the learning curve. We can hold regular calls with your team to work through how to get the most out of the tools you've chosen and how to deal with any obstacles you’re currently facing. “Google has a lot of great documentation, but there’s no substitute for that human touch and being steered in the right direction by people who really understand the technology, what’s possible and how best to implement it,” says Causeway’s Patrick Locke. 

How can we help you too?

Our Data, Analytics and AI practice brings together a highly committed team of experienced data scientists, mathematicians and engineers. We pride ourselves in collaborating with and empowering client teams to deliver leading-edge data analytics and machine learning solutions on the Google Cloud Platform.

We operate at the edge of modern data warehousing, machine learning and AI, regularly participating in Google Cloud alpha programs to trial new products and features and to future-proof our client solutions.

We have support from an in-house, award winning application development practice to deliver embedded analytics incorporating beautifully designed UIs. We are leaders in geospatial data and one of the first companies globally to achieve the Google Cloud Location-based Services specialisation. If you'd like to find out more about how we can help you build your own modern data and analytics platform, why not take a look at some of our customer success stories or talk to our data analytics team.


Contact the Ancoris data and analytics team


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