One of the greatest challenges that organizations are grappling with starts right at the beginning of the data pipeline. Our new research with IDC revealed that a staggering 96 percent of global business leaders reported that it is challenging for their company to identify potentially valuable data sources – with 56 percent stating that it is either very or extremely challenging. If this rings true with you, breaking down these challenges will require a new kind of thinking and approach.
Automated Data Discovery
One increasingly essential piece of technology that is emerging to assist in getting access to data is the metadata discovery tool. This new class of AI-enabled tools look to apply tags and definitions to data based on structure, distribution, context, or even column header information. This tag can then be used to drive policy around security, distribution, and even obfuscation rules for analytics use.
The side effect of this tagging is that it makes the data easier to search for in a catalog. The technology in this space is still young and, like AI technology, requires human effort to train, especially for unique data sets, putting a premium on governance policy and expertise. But those who go through this process will find the time well spent.
However, even with a mature data discovery process in place, many of you will still face challenges in understanding which data sources are of high value. In the world of analytics, getting critical data into the catalog is key to success, but this should also be put through the lens of a concrete set of business goals so that an organization does not get bogged down in low-value targets. Getting a list of high-value data sets will require alignment with the business and a connection between data sets and their potential analytics value.
Creating A Single Pane of Glass For Data, Regardless of Where It Physically Sits
In the old world of data warehousing, data was carefully fed into a single repository and modeled into a common structure, a process that often took months or even years to build. Modern analytics requires much more dynamic access to data – some raw, some structured, and some trusted, authoritative sources. Data cataloging enables you to establish a single repository of all of the data a business has available for analytics so that everyone across the organization, from data engineers to business users, are able to identify and access the data to inform decision-making.
This helps to overcome some of the biggest barriers you will encounter in identifying data sources which are of value, these include:
- Finding valuable data sources – When it comes to establishing a clear data pipeline, the first hurdle that many organizations stumble over is identifying data sources that will provide the most useful insights. In fact, nearly two-thirds of business leaders (61 percent) cited that finding sources that are valuable is one of their greatest challenges. Establishing a data catalog can help overcome this. For example, Qlik Catalog has a keyword search so business users can easily identify what data sets are available for visualizations or analytics dashboards. Much like an Amazon online shopping experience, you can search for data and put any relevant results into a basket for further actions later. These can be tagged, so that if you search “customers,” for example, you could be shown the different data sets that may support your analysis – from invoice to customer service data. A data catalog provides businesses with a straight-forward process to access the insights most relevant and valuable to the task at hand.
- Profiling data to maintain quality standards – Data in the wild is often not well understood, or even usable. This can be because it is in a structure that business analysts don’t understand (such as JSON or XML). In these cases, business leaders don’t realize that the data needs to be profiled to assure its quality. Otherwise, you can fall foul to the most common reason data analytics projects fail to meet their objectives: the quality of data just not being good enough (40 percent). Data profiling – collecting data from an existing source and distilling it to informative summaries – helps you understand and quarantine data that might not have initially met quality standards, either because of extra characters, bad formats, or violations of a business rule. By profiling and cataloging all data, including data in the wild, you can ensure you have access to the complete and accurate picture, enabling you to make more informed decisions every time.
- Establishing access – Ensuring the right people have access to the right data at the right time can be a significant challenge – cited as such by just under half of global business leaders (47 percent). With security and governance factors at play, establishing correct access to data is just as important for compliance as it is for insights. Consolidating data into one platform can be an effective way to manage access. Enabling everyone within an organization to see what data is available to them and managing access privileges from a single source can allow organizations to ensure they’re getting individuals the insights they need, while ensuring processes remain secure and compliant.
Make Trustworthy Data Identifiable To The Business
The ability to identify valuable data sets has been revealed to be a significant and pervasive issue amongst global businesses – and one which presents a massive hurdle to becoming a data-informed business. However, it’s important to understand the full process required to identify valuable data sets doesn’t end at discovery – you need to make it trustworthy and identifiable to the entire business. By profiling, cataloging and effectively managing access to data, you can ensure the right data is accessible to the right person at the right time. With the correct measures in place to do this, it is possible to create an effective data pipeline that provides everyone with access to well-structured data sets and accurate insights for decision-making.