How To Approach A Successful Data Analytics Project

We have all been told to be excited about the business opportunities that data analytics can unlock. However, if your data quality and efficacy aren’t up to scratch, you are not going to get very far.

In this post, Cloudreach Solutions Architect, Dave Loveridge, suggests some key principles that you should follow if you want to deliver a successful data analytics project.

1. Define Your Project Succinctly and Avoid Scope Creep

The first step when looking at a data-driven project is to determine what you hope to achieve with the initiative. This informs the scope of that project, which is created in consultation with the different business stakeholders.

If you are unable to define what you are going to be delivering, then you are setting yourself up for failure. If there is not a well-defined scope then this is likely to lead to misaligned expectations.

It is important to understand the business use case and how you can deliver value from the project. This should involve time spent between people within the business and IT communicating:

  1. Exactly what you want to achieve with this project
  2. How you’re expecting to see it delivered
  3. Listening and taking onboard feedback

*These discussions will be some of the most valuable time spent.*

Before you move to the next step of your project you should have a defined list of expectations and deliverables that you are able to refer back to as it progresses. These can still change throughout, as long as they are agreed and captured, but that baseline is key.

2. Define Operational vs Analytical Goals

The next important points to understand are:

  1. Who within the business it is going to help
  2. What this is going to enable
  3. Always refer back to your objectives and what you want to achieve overall

You need to nail down where the focus of your project is going to be. Are you going to be looking at the analytical side of your business aiming to drive efficiencies within your business? Or are you going to be looking at the operational side of your business and trying to get real-time reporting?

Reinforcing the first point, once you fully understand what your project is going to deliver, you are able to align your actions to your goals.

3. Data Quality

The third key principle is to maintain good data quality. When you don’t maintain controls over your data governance you will end up with a large amount of data that is not delivering value back to the business. Your end result will be a collection of disconnected data pools that all happen to be in the same place. Failing to maintain correct Data Quality will lead to the following issues:

  1. Data that is difficult to analyse, delivering less value to your users due to poor data governance practices.
  2. Challenges in proving the integrity of your data due to poor data lineage tooling.
  3. Difficulties in the event of offloading your data from its current system due to poor designs
  4. Poor quality data leading to additional effort being required to extract useful information from it.

Some key ways to avoid this kind of scenario happening are:

  1. Making sure your team are aware of best practices with using the platforms you’re working with. It is useful to have application and process owners as a gatekeeper here. These application and process owners should sit within the business units that will utilise the platform. These users will have far more context and understanding around the application layer so will understand the impact of making changes to the platform.
  2. Reviewing your data sources. Don’t keep old data sources if they are no longer useful. On a related note, ensure your teams are also adhering to this practice!
  3. Automate everything! Remove as much possibility of human error by taking humans out of the process.

The data that you ingest your platform is the key to success. It’s important to remember, as with any project, that garbage in = garbage out.

4.Manage Your Data Analytics Project Using An Agile Methodology

There is a good chance that you have heard about the concept of using Agile methodologies for managing projects. Within a project like this, where there is a requirement for such tight integration between the business and IT teams, such an approach is advisable.

Some of the key takeaways from the methodology are:

  1. Engaging stakeholders:By ensuring you have stakeholders engaged from the business and feeding progress back to them on a regular cadence, you can ensure that what you invest your efforts into is what the business is going to derive value from.
  2. Transparency between the business and IT:Having high levels of transparency between the business and IT will lead to more effective communication between the two parties and ultimately a better end product being delivered to the business.
  3. Launch using an MVP:Making the first version of your platform a MVP (Minimum Viable Product) allows your teams to get the platform into the hands of the people who are going to be using this quicker than would otherwise be possible. The key benefit of this is to get feedback quickly and change where necessary.
  4. Make use of short sprints:This point is in a similar vein to the previous point around getting feedback quickly. Ensure that at the end of the sprints the IT teams are demonstrating back to the business the progress that has been made. This ensures that both parties remain aligned to the end goals.
5. Understand the flow of data through your organisation

The final point to remember is that you need to understand how your data flows around your organisation. This covers the initial point of creation, ingestion into your system, ETL functions, presentation to the business, archival of long term data. You should be able to whiteboard the flow of the data within your business confidently.

Doing this allows you to truly understand the landscape you are operating within, how best to work with the data you’ve and, ultimately, how to deliver the most value to your business

Conclusion

In summary, by planning your Data Analytics project out thoroughly at the start, defining exactly what it is you’re looking to achieve and how you’re going to do it and ensure that the information you’re feeding into your new platform is of high-quality then you are setting yourself up for success.

  • agile
  • data-analytics