We all know the importance of data. Over the years, it has become the lifeblood of every organization, regardless of its size or industry. And, as digital technology continues to take precedence, every action, interaction and reaction is producing more. In fact, Forbes recently estimated that more than 2.5 quintillion bytes of data are produced every day. A number that will only grow over time.
When used effectively, this data becomes the most valuable asset of any organization. Businesses can use it to increase productivity and improve decision making, setting them apart from the competition and delivering real added value to their customers.
However, while many business leaders are too aware of the value of data, few know how to truly maximize their potential. Data is almost worthless if it is not properly analyzed and understood. In our post-pandemic world, it has never been more important for businesses to take control and move from “data awareness” to “knowledge analysis”. However, it has never been so difficult.
Data is the key to survival
The complexities of data integration are not new. Often, regardless of industry, important information is dispersed among multiple data sources within an organization. Governments, public sector organizations, and private businesses all face the challenge of distributing and storing data across a network of on-premises, multi-cloud, and third-party environments. Other complications often arise from siled data, legacy applications (some of which were never designed for data sharing), and a range of communication formats and protocols.
While this was a downside before, in the midst of a global pandemic, it quickly became the difference between a business that survives or fails. Indeed, crises such as Covid-19 become even more difficult to manage when critical information is not easily accessible to those who need it. From point-of-care physicians to businesses trying to navigate a changing landscape, the lack of critical information presents a never-ending list of serious issues.
In order to weather the initial blows of the pandemic, businesses needed to be more resilient and creative than ever. And, as we emerge into this new hybrid landscape, the journey is far from over. Businesses around the world need to figure out how to digitally connect with their employees and customers. At the same time, they must be able to predict what is going on in the market and process this information almost instantly to make quick decisions.
Ultimately, data is knowledge and knowledge is power. But this knowledge must be transmitted quickly and effectively in order to inform decision-making during this time of uncertainty. However, in many cases, traditional and outdated data management tools stand in the way.
Traditional processes are no longer suitable
In the past, probably the most popular method to retrieve data from multiple sources was Extract, Transform, and Load (ETL). With this, data files are extracted from an existing source, transformed into a common format, and loaded into a new data store, such as a database server, data mart, or data warehouse. Once completed, the information can be made available to prescribed users, according to predefined access and security protocols. Essentially, the data is moved and copied to an organized store for a single point of access for business users.
Sounds positive, right? The problem, however, is that ETL has been a standard method of integrating mass data since the 1970s. It is therefore not surprising that certain limitations are becoming more and more apparent. The reality is that ETL processes and legacy data storage techniques make detailed data analysis almost impossible because you are still working from historical information. ETL also lacks any form of centralized access, preventing companies from using all the data they want. This creates significant bottlenecks for engineers due to the time and effort required to produce datasets, run queries, and perform other business user requests. Over the years, data volumes have grown (and continue to do so), making the ETL process more expensive, cumbersome, and error-prone than ever before.
To make matters worse, the ETL method of data duplication results in the creation of new data repositories that can quickly multiply into complex, siled datasets with their own governance and security mechanisms. With the General Data Protection Regulation (GDPR) requiring robust personal data policies, strict record keeping, and time limits for data retention, this could present a very real governance problem with potentially devastating consequences.
Using data virtualization to become knowledge-driven
Data virtualization avoids the typical ETL move and copy. In fact, the principle is quite the opposite. With these technologies, organizations can leave data at the source and do not need to move and copy it to another location. Instead, they only abstract it if and when it’s needed, for immediate consumption.
In our domestic life, many of us now enjoy movies and music through Netflix, Amazon Prime, Spotify, and other services where entertainment is streamed from a location unknown to us. We don’t have to worry about where the media is coming from, but we have immediate access to a huge selection of entertainment and there is no need to store a copy of the data locally (in the form of CDs, DVDs and discs, etc.). So why not use the same approach in business?
This is the function of data virtualization! We see it being widely used as a key delivery style across many top-notch organizations – including most major banks, retailers, and manufacturers – as a key part of their data integration architecture. By automatically integrating disparate data sources, optimizing queries, and creating a centralized governance architecture, data virtualization enables organizations to securely access the data they need, easier and faster, increasing to both their income and their results.
Data virtualization is a key technology that powers the data structure of an organization. The benefits of data virtualization include being independent of location, format, or latency. It provides information in real time and in the appropriate format required by each individual user. This means that all company data, no matter where it is stored – whether on-premises, in a cloud environment, a data warehouse or a data lake – can be brought together to create a view. completes in real time much faster than when using traditional processes. In fact, Forrester recently discovered that this type of technology can reduce data delivery time by 65% compared to ETL, which could save $ 1.7 million.
Data virtualization helps reduce the load on IT and data engineers, while allowing data scientists to quickly and intuitively get what they need to build models and develop knowledge. It can help businesses improve overall performance and efficiency strategically, reducing costs and project cycle times, and helping improve decision-making capabilities with real-time information. It also has unquantified benefits, such as organizational flexibility and agility, customer and employee satisfaction, and peace of mind when it comes to audits and security matters.
In today’s landscape, data is everywhere. But its value doesn’t depend on what you create or own, it depends on how you use it. The shift from “data awareness” to “data analytics” will not be easy for many. This will involve an overhaul in terms of infrastructure and also state of mind. However, modern technologies such as data virtualization could provide an answer for companies looking to capitalize on their most valuable asset.
Charles Southwood, Regional Vice President, Denodo