Next Practices For The Public Sector Intelligent Enterprise
Evidence of digital transformation within governments is everywhere. Internet of Things technology is helping federal, regional, and local governments better manage and evaluate infrastructure, assets, traffic, and the environment. Artificial intelligence and machine learning are being put to work by agencies to deliver a more personalized, interactive citizen experience. Blockchain is being deployed to reduce fraudulent activity and increase transparency. Augmented reality is improving emergency management training, transportation planning, healthcare, and tourism.
Yet Gartner’s 2018 CIO Agenda Survey found that only 16% of government CIOs in 98 countries said they planned to increase investments in business intelligence and analytics, and only 6% will increase spending in data management. And a recent report by IDC predicts that by 2020, only 20% of national governments will revamp their metadata strategy to streamline how data is stored, indexed, and searched – even as the volume of unstructured data starts to slow compute times and the speed of data searches.
The disconnect between opportunity and action is glaring, especially since one of the big barriers to a full embrace of organization-wide data analytics has been solved. Until recently, the ability to bring together data from across and outside of an agency for real-time analytics on a grand scale was easier said than done. Now it’s available to everyone.
From our global experience working with the leading, most innovative public sector organizations, here are three SAP “next practices”– capabilities and outcomes to help you utilize data and analytics on a grand scale.
Integrate diverse data sources. Data is scattered. It’s in multiple applications, files, data warehouses, data lakes, and public and private clouds. Each silo walls off the data with proprietary rules and complexity. You need visibility into that data. Without it, you have a disjointed picture of the organization. With it, you can deploy an intelligent payment system, applying machine learning to tax and other payments to decrease revenue leakage. You can harness a broad collection of data sources to combat drug abuse, reduce infant mortality, improve trash collection, and optimize emergency response teams. Anticipate and mitigate breakdowns to water, roadway, streetlight, or other infrastructure with predictive maintenance – and much more.
Next practice #1: Integrate your data by combining data sets as needed – including Big Data, process data, product data, analytical data, etc. – into a single data universe for much greater visibility.
Make data more useful. Your data comes to you structured, semi-structured, and unstructured. It may be spatial, chart, numeric, geographic, time-series, relational, JavaScript Object Notation (JSON), and so on. Integrating all these different types of data is extremely complex. But without it, your organization risks growing inefficiency and squandering available resources.
Next practice #2: Integrate your data sources, using orchestration and governance solutions. Go from raw feed to intelligence with real-time analysis of vast data sets. How? With solutions to understand, integrate, cleanse, manage, associate, and archive data to optimize business processes and analytical insights.
Simplify your data landscape. Centralized. Easy-to-use. Automated. That’s what you want from your data analytics platform. And those features have been a challenge because of all the different databases, apps, and clouds in your IT and business environment. But now a centralized data management solution is available that manages all facets of a data universe. Represented visually, the architecture is easy to share and understand. Stakeholders assigned to an architecture team within your organization can collaborate through a user-friendly Web application in the planning, design, and governance of the architecture.
Next practice #3: Create and maintain a complete landscape architecture that is easy to share and understand. Open up this landscape to an array of employees and managers to jointly manage your data environment as an agile, strategic tool.
A growing number of data analytics use cases for public sector organizations
Data analytics is being recognized as a vital tool for public sector organizations. The need for speed has grown – along with diverse types and quantity of data. Becoming a truly intelligent enterprise organization requires a reliable, easy-to-use platform to capture, ingest, process, orchestrate, compute, and consume data at tremendous scale.
SAP customers in the public sector that are intelligent enterprises are using data analytics fed by an increasing array of data sets for use cases that include:
- Smart city traffic monitoring
- Predictive maintenance
- Intelligent payment systems
- Trade violation detection
- Business fraud analysis
- Real-time financial analytics
These are just some of the many quickly evolving, creative ways that larger and diverse data sets are being put to work by public sector intelligent enterprises today. Some use cases are relevant to every type of organization within the public sector. Others are more suited to different types of governments, geographies, markets, and other unique characteristics.
For more on how public sector organizations around the world are transforming into intelligent enterprises, read the new SAP white paper “The Data-Driven Public Sector Organization – Data Management for the Intelligent Enterprise.”
And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.