Intelligent Enterprise Data Analytics
Finding value in data collections is more important than ever in our data-driven world. US Army Deputy CIO Gregory Garcia even went so far as to refer to data as “the new oil.”
National security agencies in general are focused on how to turn data into actionable intelligence, at cyber speed. (After all, the National Security Strategy and National Defense Strategies prioritize Data Analytics, and AI.)
“We’re data-rich, but analysis short.”
As DHS CIO John Zangardi mentioned at the GDIT Emerge event in Washington DC, “We’re data-rich, but analysis short.”
You can gather all the data you want from across different domains, but if the analysis and context aren’t happening, that data doesn’t provide any value.
Building an environment capable of continuous, rapid data analysis at scale is challenging, but not impossible.
Harnessing the power of an intelligent enterprise comes down to unlocking the trove of collected data and making analytics and predictive analytics capabilities more accessible with machine learning (ML) automation and more self-service capabilities.
Set Your Data Foundation: Operate from a Single Source of Truth
Data collected and stored across multiple domains is a challenge for innovation and analytics, particularly for the intelligence and national security communities.
Data floods in from different sources and in different formats, making it particularly difficult to manage.
However, building a pipeline that continuously digests, organizes, and cleanses data is easier said than done. Data is everywhere, and it continues to grow.
Gartner predicts that this year, Internet of Things (IoT) devices alone are expected to grow to over 21 billion.
These connected devices are revolutionizing everything from modern warfare, to supply chains, to infrastructure maintenance. Agencies need to be ready to secure and use the data that is streaming in.
Distributed landscapes create significant hurdles for agencies with data silos and security challenges.
This is especially true when data is stored in multiple locations such as on-premise, in the cloud, in data lakes and warehouses, or on edge devices.
Here’s where a data fabric can help.
Using a solution like the SAP Data Hub supports an intelligent enterprise by gathering, preparing, managing, and sharing massive quantities of data with automated workflow pipelines across your entire landscape.
Data fabric solutions can also:
- Provide visibility and control across distributed data landscapes
- Utilize a centralized data control cockpit to access and manage your data above siloes, data structures, applications, and locations
- Manage access to specific data across the whole data landscape
- Orchestrate and govern any type and volume of data across your entire distributed landscape to ensure your analysis is based on trust-worthy data
Enable Self-Service Analytics
There simply aren’t enough data scientists to go around. Agencies must democratize data science and create new processes to make it easier for mission experts to access intelligence across varied environments.
New approaches can automate and enable self-service capabilities, making it easier for non-data scientists to engage with data and use predictive analytics. Automation and Machine Learning can also help alleviate resource challenges in managing and supporting analytics.
Challenges in analyzing large, diverse, distributed datasets is an exercise in overcoming complexity.
Solving for complexity requires a comprehensive approach that includes enforcing new processes that may require a culture shift.
Finding the right partner to help unify your data fabric and make intelligent analytics more accessible is a start.
SAP Analytical solutions use advanced automation and predictive capabilities to help mission workers draw meaningful insights from massive volumes of data.
NS2 solutions include integrated cloud solutions, intelligent applications, and self-service tools – all designed for your unique mission. For more information, please contact us.