Data can be an invaluable tool when used correctly and with the end goal in mind. In fact, companies like Netflix have saved approximately $1 billion a year on customer retention thanks to understanding data and having clear organizational goals for the use of data. It’s no wonder so many organizations are turning to data when making critical decisions. The challenge lies in the trust and completeness of data; if data is siloed and not easily accessible, can it truly be leveraged for all the value and insights it contains?
While enterprises have access to more data than ever, it’s practically useless until it’s properly organized, centralized, and analyzed. Data comes from a wide variety of sources and systems, and typically comes in all different forms and structures. So before it can be analyzed accurately, it needs to be unified into a single system of access and connected for characterization.
That’s where unified data comes in.
Unified data connects and centralizes data from all sources in an organization (both internal and external) so that it can be structured, organized, and processed effectively and efficiently. This leads to amplified value in data as well as opportunities to uncover hidden value in data that already exists.
Why is unified data important?
It is estimated that the global Big Data Market size will grow from USD 138.9 billion in 2020 to USD 229.4 billion by 2025. As time goes by and technology evolves, data is becoming a huge asset for businesses in regards to customer retention, developing leads, and optimizing revenue. According to a 2020 report by McKinsey, many mature organizations also suffer from fragmented data repositories and storing and maintaining those troves can eat up between 15 to 20 percent of the average IT budget. Herein lies one of the biggest opportunities for businesses to unleash the hidden value in their data.
Properly connecting, analyzing and processing data in a central and consolidated system is imperative for businesses today to survive. Unifying data unleashes its inherent value through proper management and modeling of data from a variety of sources. This is further amplified when artificial intelligence (AI) & machine learning (ML) is effectively applied. It is incredibly important that data is gathered from a variety of sources and that those sources are incorporated into a central solution ensuring models are constructed with all of the data available. Not doing so risks missed opportunities at best, at worst, it risks creating real financial and operational challenges.
The challenges of creating unified data
While unified data comes with a multitude of benefits, it also has its share of challenges due to the nature of siloed data created.
This is due to a typical organization having a wide variety of software and hardware providers, of which many have protective 3rd party software business models that is not conducive to organizational growth. An organization typically has data stored in the cloud or on other local storage devices; some of which are unable to communicate with each other. Not only that, but each server can have its own language, syntax, and practices, making it extremely difficult to bring them all into one space.
This can become even more difficult when dealing with legacy systems with interoperability challenges. However, creating a unified data solution is a great way for companies looking to utilize data to amplify and empower business decisions. So while this may require an upfront investment to update or consolidate some systems, the return on investment could be substantially more. Years ago, IBM estimated that bad data cost the US economy $3.1 trillion dollars and Gartner research found that organizations believe poor data quality to be responsible for an average of $15 million dollars per year in losses. Both these metrics may be out of date today, but we can only imagine that this impact to the economy has not materially improved.
The unified data solution
Data comes in all shapes and sizes. Whether it was collected by an IoT device or tracking software, it needs to be centrally consolidated for maximum effectiveness. Data unification plays an important role in getting the most out of datasets and providing AI and ML with the full picture of information it needs to build proper predictive models based on the entire business.
A Unified Platform for Automation & Integration
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