Data analytics is all about optimizing data, insights, and better decision-making, but there’s a lot that go wrong especially internally. It’s not the first thing people think of when it comes to analytics which leads to costly mistakes, wasted resources, and frustration. Little things like response times and updating the team can have drastic effects on the overall process, so what exactly goes wrong?
A report revealed that data workers waste about 44% of their time because of inefficiencies within the company, despite the strides in innovation and growing data expertise. One of the biggest complaints and problems is stated to be the data preparation process. When the process can be automated, why are organizations still choosing to spend more time on manual labor that frustrates employees?
There’s a lot of questions that need to be answered before handling data in order to optimize time, money, and effort.
Can you confidently answer these questions?
- Do you have all the talent you need (management, development, etc)? Do you know what skills you need?
- How accessible are resources (people, money, data)? Does everyone have everything they need to move forward?
- How are team members communicating? What are the roadblocks for the team/management/product/client?
- What is the response time between collaborators/teams? Are deadlines and
- Can anything be automated? Are there any human errors that put projects at risk?
- Is there a training process or lesson that will fill
Any business handling data needs to build a foundation for effective collaboration and efficient management of tools or you risk inaccurate, delayed results.