By Manish Gupta, CMO, Liaison Technologies
IDC now forecasts that we will generate 44 zettabytes of data by 2020. That number is hard for most people to even comprehend. To put that in perspective, a single zettabyte equates to 36 million years of HD video.
With the amount of information capture on the rise, cloud is more important than ever as it will be where these massive amounts of data will reside and flow through. This data needs to be organized – through data integration and data management – in order to produce valid results for advancement and efficiency gains. This need affects all industries and touches every business. It is truly universal.
Today big data is pulling businesses in conflicting directions, with data technologies and tools fragmenting just when they need to converge. The enthusiastic adoption of innovative cloud-based applications coupled with the need to sustain legacy infrastructure further strains the data model. Finally, the need for streamlined processing and management of data is further exasperated by the insatiable drive for business insight. Companies need a data-centric approach that flexibly delivers broad perspectives drawn from a synthesis of traditional structured data and a rapidly expanding universe of free-form data.
So how did we get to this place? Enterprise data technologies evolved in a much simpler era, with separate disciplines emerging to address the basic challenges of data integration, data management, and data analytics. The actionable business intelligence comes from the analytics, but the analytics are only as good as the data normalized and delivered by the integration and management solutions.
A very old computer aphorism is still quite apt: Garbage in, garbage out. As enterprises struggle to cope with the growing data deluge, they need to optimize the data feeding their analytics engine, and this means taking a fresh look at how data is integrated and managed.
Currently, the data integration and data management solution providers are generally responding to the data explosion with a proliferation of tools. However, these distinct technology specializations don’t give businesses a holistic data solution that can make sense of data spread across enterprise data centers and cloud-based platforms. This type of offering may even complicate the issue further.To change this, we need a confluence of data integration and data management.Fittingly, cloud is enabling this change and cloud-enabled platform service providers can offer a solution to this data conundrum.
In this new data world, a unified data integration and data management platform can streamline the complex tasks of matching, cleansing, and preparing all data for business intelligence (BI) applications. Versions and hierarchies are maintained, ensuring that data remains in sync.
A unified platform also makes it much easier to identify data sources and monitor interactions between applications that use the data. The context thus gained makes patterns and relationships more evident during data analysis, speeding up the delivery of actionable insights that inform business decisions.
Consider the possibilities when the healthcare industry starts using a data integration and data management service provider as its data integration broker and information hub.
A patient wearing a sensor device is monitored continuously by a mobile app, which transmits biometric data to the data integration and data management service cloud. There, the remotely collected data is integrated with the patient’s electronic health record (EHR) and lab results. Analytics can then be realized with confidence. The result is a much bigger picture that improves diagnostics and enables more informed treatment decisions by clinicians and patients.
Increasingly, business success will be determined by the best use of data. The winners will be the ones who leverage the cloud to create the synergistic data whole out of the sum of the wildly increasing and disparate data parts. Companies that are successful here will be better able to identify customer needs, track market trends, more quickly diagnose a patient’s illness, or even invent the next big disrupter.