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The five pillars of a successful data analytics programme

  • Sudeepta Chaudhuri
     
    December 8, 2021

Analytics can add significant value to a business, but only if approached in the right way. Here’s our take on the five pillars of a successful analytics programme. 

Before you embark on any analytics initiative, before you invest in any technology or consider onboarding a partner, it’s important to have a clear vision of what you want to achieve.

Once that vision is defined, you have to plan, prioritise, and implement the building blocks that will allow you to achieve it. This requires making sure you have the right data, the right expertise, the right technology, and proper governing mechanisms. And, of course, ways to measure success at each stage of the project. 

For those just starting out, and even for those who are looking to advance their existing initiatives, knowing how to approach all of this and where to focus efforts can be a huge challenge. So, we wanted to make things easier. 

Based on our experience of supporting clients at all stages of analytics maturity, we’ve put together this list of five underlying pillars for a successful analytics journey.

1. Data

At the heart of any analytics project, no matter how complex or how simple, is good data. 

Fifteen or twenty years ago, most organisations weren’t able to take advantage of the data at their disposal, simply because the technology wasn’t readily available or easily affordable.

Today, this has changed. Whether you work in retail, consumer goods, manufacturing, life sciences or any other industry, endless amounts of data are generated by the systems within your organisation. And we’re increasingly seeing this data put to use, thanks to lower storage costs, greater processing power, and more accessible technology.

More importantly, over the last decade, we’ve started to see companies combine their internal data with external data sources. This can relate to things like social sentiment, demographic behaviours, or even the weather. And it’s when these streams are integrated that we see the real value in analytics unveiled. 

As technologies like IoT gain prominence, the amount of available data is going to increase dramatically – and that means opportunities will grow too. Advancements in image processing, voice recognition, and natural language processing techniques have opened up new ways to make use of unstructured data. So today, your data processes must allow you to capture both internal and external data, and process both structured and unstructured forms, too.      

Knowing how to capture, process, store, and maintain these various forms of data from different sources is essential to getting the most value from them. 

2. People

From laying out the design for your data warehouse architecture to finding the deep insights that add new value to your organisation, every aspect of a good analytics project has people at its heart.

Even things like automated processes and machine learning algorithms need to be created, monitored, and updated by a team of experts – and establishing that team early on will be integral to the success of your projects.  

A successful analytics programme will require the experience and skills of data engineers, data stewards, data scientists, visualisation experts, and business analysts. And it’s only when all of this expertise comes together that organisations can see the full value of their investments. 

These people are integral to helping you dissect your business problem or use case, ingest the data required to examine it, ensure that data is well managed and maintained, and build the analytical models needed to derive true value.

However, a lot of organisations simply don’t have the manpower or resources to create this team in-house, which is why outsourcing to a partner to ensure you have the capabilities you need can sometimes be an essential part of building a successful programme. 

 3. Technology

The decisions you make around the technology you’ll use to support your analytics projects are absolutely critical, not least because they can involve big investments and may take a great deal of time and effort to reverse if you get them wrong.

Making technology choices in line with your overall vision requires a thorough understanding of the various components of your analytics landscape, but also an understanding of the upstream and downstream ecosystem of your organisation     

Today’s organisations work with huge volumes of data, so significant processing power with minimal storage cost is a must. But perhaps the most important consideration is to make sure the solutions you choose are scalable, able to integrate with your other systems, and part of a wider, future-facing strategy.  

In our work, we often see organisations build a technology stack that simply isn’t fit to meet their long-term ambitions. The temptation is to create a solution for the immediate task at hand. But that kind of myopic thinking will likely cause problems further down the line. 

Similarly, it’s not uncommon to see different functions make their own technology investments based on their individual needs. The result is a patchwork of different solutions and a huge headache for the technology teams responsible for integrating them. As a consequence, it becomes next to impossible to gain a single version of the truth. 

This is why establishing a long-term vision is so important from the outset of your analytics endeavours. This vision should be broken into phases, and a flexible technology roadmap should be created to support each phase of the journey. 

4. Processes and governance 

Similar problems with scaling capabilities can come from poorly established analytics processes and governance mechanisms. 

Organisations that have individuals as the gatekeepers of essential knowledge often face difficulties as people leave or change jobs, taking vital information with them.

For any analytics project to be successful, it should be governed by well-documented processes with defined custodians.

These processes should ensure that your models are deployable, that the data you need can be ingested as regularly as desired, and that it is well managed and maintained.

5. Collaboration and adoption

Finally, perhaps the most often overlooked element of a successful analytics programme is the collaboration between business leaders and analytics teams.  

Fundamentally, these two groups of people often have very different ways of thinking. Business leaders have a primary focus: solving business problems. They want to know what insights analytics teams can provide, and how to use those insights to make better day-to-day decisions. 

Analysts and data scientists on the other hand, tend to be focused on the finer details and improving the accuracy of their models. Without proper and regular communication between these two parties, it can be easy to lose focus on the ultimate goal. Even in the most successful projects, it can take several iterations to get a model to a point where both parties are happy with it.

The only way to overcome this problem is for business owners to take control of analytics projects, and ensure constant communication and collaboration with the analytics teams that are supporting them. This will also ensure the adoption of the analytics output by key business stakeholders.          

Analytics at The Smart Cube 

Here at The Smart Cube, we offer bespoke, end-to-end analytics capabilities, from data engineering through to reporting and visualisation, and advanced analytics. 

To read about some of the ways we’re helping our clients, or to learn how we can help you achieve your own business goals, visit our Commercial, Sales and Marketing section.