Stephen Henn is the Vice President of Conduent, Consulting and Analytics.
For years we’ve been hearing about the promise of big data. And for many companies – and more to come, the promise of big data is being realized to the benefit of organizations’ stakeholders. But big data was never the end point. It was just the opening volley in the revolution of using data to further business objectives, connect with customers in a more meaningful way, and identify and reduce risk. We are now ready for the next big step in the data revolution – integrated data.
What is “big data” anyway?
It would help if we took a moment to review what the term “big data” is and what it promised to do. In its simplest form, a big data system takes vast amounts of data and uses artificial intelligence and data analytics to look for patterns and trends that humans – with our biological limitations – cannot find. Generally, these insights come in one of two forms.
- The first are patterns that humans could see if we could view and understand the very large data set. Think of this as computers giving us the ability to see the landscape from 30,000 feet rather than ground level. Things look different from above.
- The second are patterns that are too subtle for humans to detect, but over the data set, they become apparent. Think of this as the difference between watching your child grow up every day as opposed to seeing your 10 year old niece for the first time in five years. Computing power provides the ability to identify incremental changes.
There is clear and compelling value in a big data system, but this was just a first step. There are a number of limitations of a “traditional big data” approach, but we should focus on two. The first is that traditional big data approaches were limited to one type of data. For years, big data was restricted to structured data, primarily numeric in nature. As an example, usage data could be analyzed to identify common buying patterns – the classic “customers who bought Fox in Sox also purchased The Giving Tree.” Second, the data was silo-ed. It was data from one, or possibly two, systems. In the book sales example, it was purchasing data from an eCommerce system – simple stuff in the realm of artificial intelligence.
That was then.
Integrated data is the next leap forward
Now is the time of integrated data – an approach that addresses both the “type of data” and “silo-ed” limitations — and operationalizes it all. Integrated data is the analysis of any type of data from multiple systems that give actionable intelligence to improve all of a company’s operations and processes. Data sources can be structured (finance, procurement, HR) or unstructured (forms, contracts, data feeds, websites), and include input from multiple internal and external systems. The objective of integrated data systems is to harness the power of artificial intelligence and data analytics to produce improvements in operational efficiency in a disruptive way.
Let’s consider a concrete example within the cell tower leasing space. In this integrated data system, the following core components are combined:
- Unstructured data: from cell tower lease contracts
- Structured/semi-structured (static) data from internal sources: information on equipment specifications and geo-location data of tower sites culled from operations and maintenance systems
- Structured/semi-structured data from external sources: a live feed of alerts from the National Weather Service, which indicates that a high wind warning will start within 24 hours and last for a period of 36 hours for the Delmarva Peninsula
The integrated system data takes the NWS feed, identifies the affected sites and recognizes that certain types of equipment on towers within the Delmarva Peninsula have a wind-sail characteristic that leaves the equipment vulnerable to high wind damage. The system further identifies 17 sites with performance issues that are deemed especially vulnerable. It also determines that 12 of those same sites require notice to the tower landlord prior to commencing any maintenance or repair work. Finally, across the target area, it identifies that 30% of the equipment is outdated and requires a full upgrade.
By incorporating business rules, the integrated data system responds by issuing a notice to maintenance crews to pre-position the correct equipment and supplies in sufficient quantities with a focus on the 17 sites with performance problems. Further, the pre-positioned equipment includes complete installation packages for a total system replacement for the sites representing the outdated equipment. To ensure maintenance crews can quickly make repairs, the system generates a notice to the tower owners that maintenance or repair may be required on equipment on their land or building. It also alerts maintenance crews that they have full access to the site. The notices and alerts are automatic.
Integrated data systems take the next step in the big data movement to improve corporate performance by operationalizing data silos. Implementation of an integrated data approach can result in up to a 90% reduction in the resources – time, money and effort – required within operational processes. The flexibility and power to analyze different types and sources of data allows the benefits of integrated data systems to be applied to any and all operations and processes within an organization.
Big data was a revolution in the way businesses approach gaining insights and advantage from their data. Integrated data systems are the next leap forward in the evolution of deriving large, tangible value from big data approaches.
Want to learn more? Let’s connect. Email us at firstname.lastname@example.org.
About the AuthorFollow on Linkedin More Content by Stephen Henn