Data wrangling – getting the best of us?
The knowledge gap is one challenge – trained data scientists have rare skillsets and are few and far between. Nevertheless, whether you’re an organisation with a designated data analysis function or one with relatively limited data science capabilities, if you’re generating data insights, you’re likely spending disproportionate amounts of time data wrangling before insights are unearthed. Data wrangling is hands-on, ‘janitorial’ work of cleaning and loading data from one raw form to another in order to prepare it for analysis, where actual business value lies. It’s an essential task: garbage in, garbage out. But this manual process is the most time-consuming part of successful data analysis. In fact, estimates of how long data cleaning takes varies from 45% of an analyst’s time to a whopping 80%. That’s at least half the day, week, month, or year, just prepping dirty data.
Based on data from Anaconda 2020 State of Data Science Survey.
Free your data analysts
For the business seeking to become truly data- and insight-driven (a mere aspiration for most), clawing that time back is a significant problem to bust. Unlock that time, and there’s a huge upgrade in value from data science up for grabs; analysts will work on rewarding tasks that generate tangible business advantages that keep the organisations on the front foot, from developing models to extract unique insights, to interpreting its impact of customers and products, making predictions based on historical data, and presenting reports based on analysis to management. In short, if you’re spending less time wrangling data, you’re spending more time realising value from it. And that’s a key distinction on the path to becoming an organisation that chases business intelligence, to one that’s driven by it.Database vs. Data warehouse
One trait unites today’s market leaders: the ability to leverage increasing amounts of data to grow their market presence through improved customer experiences. Many organisations are now in the throes of change, finding themselves weighed down both by data itself and the pressure to use it. An organisation could be using Excel for data reporting, relying on tens of different databases from multiple sources. A monthly reporting cycle might resemble a long painful process, with three weeks a month piecing the data together, and a week producing the end report in the correct format. But what’s missing for most organisations right now, and what’s at the root of the data wrangling challenge, is consolidation. There is a proliferating tangle of data sources, spreadsheets, and processes – and that’s where data warehousing solutions come in. Data warehouses enable businesses to access valuable data from multiple sources in one place, creating a single consistent source of information across the organisation.
Like the high-tech e-commerce fulfilment centres run by the likes of Amazon or Ocado, data warehouses enable efficient storage, automated categorisation, and rapid sourcing of specific items, ensuring the right product is delivered, at the snap of a finger. Source: Ocado
- Reduced turnaround time for more accurate reports and analysis.
- Enabling integration of multiple data sources to alleviate the production system.
- Saving users time in retrieving the correct information, and enabling greater access to data for the entire organisation.
- Storage of historical data to analyse past trends and make future predictions.
- Integration and enhancement of existing Business Intelligence and CRM systems.
- Improving the performance of transactional databases by separating the analytics process.
The data is extracted, transformed and loaded into the data warehouse (also known as ETL).