Self-Service Data Analytics Makes it Easy to Prep, Blend, and Analyze Big Data
As federal, state and local agencies amass more and more structured and unstructured data, the use of data analytics can help save time and resources in making informed decisions based on that data. However, challenges remain as agencies struggle to find the right skill set and manage the complexity of implementations for multi-structured data platforms.
Much of the complexity with big data initiatives is because of diversity in data formats. Data comes in many forms – machine-generated (from IoT), process-mediated data (such as data captured via forms and other transactions), and human-sourced information (documents, social media, email, videos, images, etc.).
The problem is that data analysts are underserved by traditional approaches when it comes to managing and accessing the right data for analytics. Accessing the right data quickly (as this infographic based on survey data from JPMorgan Chase from DLT partner Alteryx shows) is the biggest challenge that analysts face. 72% of respondents are not satisfied with how long it takes to get the results they need, while 90% cite challenges with data blending as the cause. Why?
Because the information analysts need is rarely in one place. In fact, only 6% of organizations surveyed have their data in one place – and waiting for IT staff or data scientists to prepare data for insights simply isn’t practical.
That’s where self-service data blending comes in.
Self-service data blending connects business analysts and decision makers to data, regardless of size, format, or physical location (spreadsheets, databases, cloud-based apps, and more). This accelerates time-to-insight, improves trust in data and analytics – without relying on other for data gathering or spatial or predictive analysis. With solutions like Alteryx, business functions can blend multiple data sources quickly easily using an intuitive workflow that doesn’t require IT or coding.