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Any data project is filled with challenges; solving these problems or avoiding them altogether is the key to success. Narrowing this list down to just five was a challenge in itself!
Challenge 1: My data is getting too complicated
Our mantra is ‘keep things simple’. That applies to the amount of code we write as well as reducing unnecessary maintenance and management of technology. Unfortunately this isn’t everyone’s experience. For example, when you’re attempting to integrate a variety of existing systems it presents your average developer with a tempting challenge: ‘I know how to fix this,’ they immediately shout out, ‘but it’s going to take a while!’ Chances are, somebody, somewhere has already solved that exact same problem. Rather than waste time and money reinventing the wheel you can simplify these things by using out of the box connectors and automated data pipelines which allow you to connect to your source systems straightaway. They’re pre-built and remotely managed, so you can be working on your data in minutes not months!
Challenge 2: Nobody’s using my dashboard
The dashboard is the place to splash your important findings in a way that is accessible and easily understood. So how come nobody is looking at it? It’s a gripe we often hear from stakeholders who’ve asked for the dashboard and the developers who sweat over them. The truth is, you can make the best, most functional dashboard in the world but nobody will look if it’s carrying the wrong information. The solution lies not in greater technical or creative prowess, but during the planning stages of your entire data project. First define the overall strategy and goals for the business and then ask how your data can help reach those goals. Secondly, identify those individuals in the business who are accountable for the numbers and targets. Without clear ownership of numbers and without insights that actually drive action, then why should anyone care what’s on the dashboard?
Challenge 3: The tech team is arguing with the business team
Traditionally there’s been ‘them’, the business team and ‘us’, the IT guys, and communication between the two can be more dysfunctional than a bad tempered debate in the House of Commons. Cross purposes and hidden agendas can make a long term data project a fraught affair, but there is a straightforward solution: nominate one developer with the appropriate people and communications skills to be embedded in the business to act as a single point of contact. This ensures that information and requests get passed on to all the relevant parties in a language they understand and you’ll avoid tedious delays and costly repetition of work.
Challenge 4: My data project is going over budget
This comes back to our golden rule of keeping things simple. Many data firms will spend three months or more setting up infrastructure and installing software before they even begin to analyse data. You needn’t do any of that if you use a data streaming service like Fivetran or a cloud data warehouse like Snowflake. Rather than wait until day 91 of a project you can start integrating data straightaway And the quicker you begin loading and looking at the data, the sooner you’ll start to see results. Beyond this we like to use the agile approach in any project, delivering in regular stages which gives us the chance to make mistakes and reset if need be. As we like to say: fail fast, but fail cheap.
Challenge 5: My data project is taking too long
This is perhaps the number one gripe for many companies involved in complex data projects. The causes are many, and can be solved by successfully overcoming the other challenges on this list. Making sure you clearly understand the requirements up front, and have a clear view of the problem you are trying to solve is key. In addition, we recommend clear planning with measurable outcomes at every stage and employing a good project manager to keep everything (and everyone) on track and to deadline. Find out how our own agile approach to analytics could help you quickly get the data your business needs.