Most companies strive to be data-driven, and digital enablement initiatives are on the rise. As a result, big data is growing and growing. The average organization expects its data volumes to grow to 247.1 TB over the next 12 to 18 months—an increase of nearly 52 percent, according to an IDGE study. That is a ton of data! With more data, data initiatives are actually failing at a higher rate. Only 32% of companies report being able to realize tangible and measurable value from data, according to Accenture.
Companies need to create an environment where data consumers can feel confident in the business context of the data they are using. This starts with equipping your organization with tools for data adoption and data literacy.
Data adoption is having people in your company begin using data. Adoption requires more than just data teams; the marketing department and the product team should also be able to self-serve data and start using it. Many companies fail in the data adoption process because data users can not find the correct data.
If you’re like most companies, you might have 1000s of tables scattered around different servers with complex server names and confusing schema names. Finding the correct data often requires meetings with subject matter experts and IT teams. Most data users do not want to spend hours hunting for data; this disorganization kills data adoption and can halt any digital enablement initiative in their tracks. Data consumers and users need an easy way to find the correct data.
The second piece is data literacy. How can teams begin generating actionable insights from data? Data literacy is the ability to communicate, understand and analyze data in context with business. Everyone in a company, not just data scientists, needs to understand data and generate insights. Imagine the ROI if empowering more data analysts can drive better business outcomes; data-driven decisions are worth millions of dollars.
Enabling data literacy requires two knowledge areas, the technical and business sides. Basic technical skills are quickly graspable. Most data users do not need to build machine learning models or complicated interactive dashboards and quickly attain basic knowledge to use most data platforms. The confusing part is locating the right data and understanding the data in context. After all, 40% of a data scientist job is just finding and understanding data.
Data needs to be organized at your company in a way that is easy to understand. Imagine having an internal Google search that can point anybody to the correct data set and provide perfect business context to actionable insight. That is what Datalogz is.
