There isn't a department in any organization these days where data doesn’t have a positive role to play – HR teams use it to improve employee satisfaction, retailers analyze customer data to provide personalized experiences – the list goes on. Despite this, there is still a huge fear of data within organizations and a belief that data handling is only for the most IT savvy – But is data really that hard to crunch?
Step it up
Simply put, the answer is no. Collecting and standardizing large, unprocessed data sets is the first step in data analysis and often, the most critical and time consuming, yet the least technical. Research has shown that on average, 80% of a data scientist's time is spent on this first stage, when ideally it would be spent on the more exciting part – analyzing and making sense of all the data – but before they can do this, the data has to be standardized first – It’s a data analyst catch-22.
By limiting data access to a small, exclusive group of people, you’re limiting the business gain from this treasure trove of information. Therefore it’s crucial all areas of your organization understand the importance of data and how it can be analysed for business gain – not just the IT teams.
There are a few steps an organization should take to do so:
- Access to the data: Of course, you need to prepare the data to ensure it can be plugged into business intelligence tools, but access is key. Collecting and preparing the data, ready for analysis is only the first step in this process.
- Encourage collaboration: Large organizations will have a team of data scientists and data analysts who’s role it is to prepare and analyse the data. Ensuring this team has the platform and means to communicate with wider teams in the business is key.
- Finding the bottlenecks: There’s usually no lack of data to collect, but the time in which organizations can have it ready to analyse can be held up by various processes or systems along the way. Manual data preparation tools like Microsoft Excel can hinder collaboration and efficiency, but remain popular among analysts and IT professionals alike: 37 percent of data analysts and 30 percent of IT professionals use it more than other tools to prepare data. This reliance on manually driven data preparation tools will continue to delay data initiatives and deter new insight gathering.