In 2016, Charity Navigator’s list of the “10 of the Best Charities Everyone’s Heard Of” put Direct Relief in joint top spot with a perfect score, based on financial performance, transparency, and accountability. Data, analysis and reporting play a central role in ensuring that Direct Relief reached and maintains that score and, more importantly, achieves its vision of improving the health and lives of people affected by emergencies, catastrophes or poverty.
Although only a small organization of some 70 full-time, professional staff, Direct Relief depends implicitly on a wide variety of data from multiple sources to manage its work. These include information about people in need, usage of the products supplied, to whom which products are shipped, donations from medical and pharmaceutical companies, and so on. These are derived from internal operational sources like SAP, as well as a range of Excel spreadsheets and external data sources. For many years, the organization has driven its analysis and reporting needs using complex tools which required in-depth knowledge and were thus confined to a few individuals in the organization.
Direct Relief rolled out data discovery tool, QlikView, this year as an improved solution to their analysis and reporting needs. Business users from all areas (CEO, warehouse supervisions, Program Managers, etc.) now use Qlik on a daily basis and are delighted with the analysis and reporting function provided. As these users have become proficient in Qlik use, they demanded additional data from new sources. Therein lay the new challenge.
IT have long provided the basic data sets for analysis and reporting. They were used to searching for and finding the data they needed. However, it was a completely manual process that left them relying on memory of where they had found what data and/or where they had saved it. With the additional enthusiasm and demands of the business for new data, IT were in danger of becoming a bottleneck to the business use of data.
In essence, Direct Relief had gone from Qlik newbie to expert in months, but quickly reached the limits of the organization’s ability to source data quickly and cleanly enough to feed their new environment. Modern tooling was required to achieve new levels of agility in data sourcing and TimeXtender’s Data Warehouse Automation suite fit the bill. Data modeling—understanding the data sources and streamlining naming conventions—was a vital first step to the creation of a Discovery Hub where all common data was collected and made available within a single platform, directly accessible to all Qlik users.
The outcome was an immediate improvement in the speed and agility of data delivery from IT and faster, more reliable analysis and reporting. New information needs for donors or recipient organizations could be satisfied more quickly and with better quality assurance. Furthermore, the availability of a well-modeled and designed Discovery Hub allowed IT to take a step back and undertake ongoing examination and improvement of all available information using the metadata gathered and stored in TimeXtender. With the foundational data about funding, donors, products, medical supplies in one place, transparency of information is facilitated and accountability demonstrated.
Business users in the warehouse, shipping, and all operations now have direct and immediate access to all data required and can easily identify, for example, what was shipped in any given period, what supplies have arrived, outstanding requests, and so on. With data now well-organized in a Discovery Hub, easily sourced and maintained with TimeXtender, users throughout the organization can ask for the information they need and be assured that it either exists already in the Hub or can be rapidly added.
Looking forward, Direct Relief are planning to train some departments in how to develop their own reports in Qlik Sense. The data model will still be maintained by IT in the Discovery Hub. This will help address one of the main issues previously seen: the CEO could ask the same question to 3 different people and get 3 different answers. Of course, this typical problem can’t be eliminated entirely, because users can filter any way they like in the Qlik environment. However, having common formulae and criteria applied in the Discovery Hub up front to routine questions provides a more consistent set of results.
So, as outlined, with a Discovery Hub, an organization can easily improve decision making and operational productivity. But where do you begin to find out more about this technology for your company or organization? You can start by reading the report titled “A Reference Architecture for Self-Service Analytics: Balancing Agility and Governance.” You will also find helpful information in this white paper about Data Discovery Automation.