Survival of the Most Adaptable: 5 Ways to “Survive and Thrive” with Data

Survival of the Most Adaptable: 5 Ways to “Survive and Thrive” with Data

In order to survive in the competitive world, data scientists make data into usable format

Each day in business, Darwin's expression "survival of the most adaptable" rings true. Faced with constantly evolving markets, models, and technologies, agile organizations always seem to be the ones gaining a competitive advantage.When considering agility, it is important to note that the role of data analysts has never been more important, whether it's profiling new opportunities, optimizing processes, boosting upsell, or the myriad of other business problems they solve.However, most data scientists are forced to spend most of their time looking for the right data and massaging it into a usable format.

Data-driven insight, which can be achieved by dynamically combining data from multiple sources, is critical to uncover new opportunities and to help analysts derive greater insights. However, the variety and complexity of new data locations, formats, and protocols mean that traditional methods of data integration are no longer able to keep pace with business needs. In response, organizations are looking to modern data integration technologies such as data virtualization (DV) to create a logical data layer capable of integrating data siloed across the disparate systems, manage the unified data for centralized security and governance, and deliver it to business users in real time. They also leverage DV to help them take advantage of the cloud and to utilize it to modernize their applications for a data-driven architecture.

Unfortunately, migrating to the cloud is not a trivial process, and it can be fraught with challenges. For example, during the cloud migration process, companies oftenmust restrict access to certain data sources and associated data sets, which can be disruptive to daily operations, and difficult to plan for, in today's24/7, international online setting. In addition, after a migration is complete, companies cannot easily revert to their previous system if they encounter any problems. As a result, a company might wish to retain certain on-premises systems for compliance or for a variety of other reasons, and it can be challenging to provide access to both on-premises and cloud systems at the same time.

Technologies, such as data virtualization, help to overcome each of these challenges, enabling companies to seamlessly move to the cloud with zero downtime, or move to the cloud at their own pace, without impacting daily operations. In fact, with DV, data analystsdon't even notice that a migration is taking place. Data virtualization has established itself as a key enabler to creating a logical data fabric, andorganizations are leveraging the technologyto migrate and stitch together data sources and applications spanning multiple clouds.

Why are so many businesses turning to the use of the cloud with data virtualization? According to analystswe've talked to, the top five reasons include:

1. Data access:

"As a business user, it is difficult to understand the connectivity, formats, and protocols of all of our data sources to handle source changes and security has been an important issue."Data virtualization eliminates the need for analysts to understand and manage the complexities of accessing data. Instead, they simply connect to the DV layer like they do to a data warehouse. This layer provides easy-to-use, yet secureaccess to all data sources, regardless of the location, formator protocol used. Data delivery is handled by the virtual layer and not the analyst. As a result, DV makes it possible to have a logical data warehouse architecture.

2. Dependence on a single supplier:

"We are concerned that semantic models are built into our BI tools. This makes us dependent on a single vendor, which is a barrier to adopting new BI and analytics tools."With data virtualization, consumers can use different analysis and visualization tools that run on top of the shared virtual layer. It is not necessary to carry out costly rewrites of the data models in each new tool because the semantic model is defined in the virtual layer. It also makes a huge difference to business agility, as any change only needs to be performedonce.

3. Performance:

"Our BI tool does not provide delegation of requests to sources, so large amounts of data is moved across the network to the BI server, where it is processed, resulting in poor performance."A key element in the performance of data virtualization solutions is their ability to optimize query by delegating it to sources. This can make a big difference in the amount of data that needs to be moved across the network. It is one of the key elements of advanced data virtualization solutions.

4. Sharing and collaboration:

"Having oursemantic model in ourBI tool also means that we don'thave the ability to share the data model with users of other BI tools without rewriting everything. This creates extra work as well as inconsistencies in the models with the different BI tools."The use of data virtualization makes it possible to create common semantic models in the virtual layer rather than on the analysis platform. This is essential because it means that analysts get a single view of the data, easily shared among users of different analysis tools.

5. Effectiveness:

"We wanted to minimize the data search, data copying and data movement by our business analysts and data scientists, because data movement is expensive, requires a lot more maintenance and generally Is very taxing from resource perspective."Data analysts and specialists spend 80% of their time gathering and preparing data rather than performing analysis.Reducing the time spent gathering and preparing data is where analysts who use data virtualization have the most significant ROI. Customer feedback indicates that thetime spent in data aggregation and preparation can be reduced from 70-80 percent to 10-20 percent. Thus, the remaining 60-70 percentof time can be devoted to valuable analytical work, which can increase the productivity of a business analyst by 3x-4x.

Darwin Knows Best

As Darwin's principles of natural selection implies, in order to"survive and thrive" in today's competitive market, business analysts need to answer business questions and share business insights with executives and other business users in real-time. That means they need to have most up-to-date data at their fingertips, so they can focus all their energy purely in data analytics. To make it happen, they need the help of a logical data fabric created with data virtualization as the foundation.

About the Author

Saptarshi Sengupta is Director of Product Marketing at Denodo, a leading provider of data virtualization software. For more information visit  or reach out to us on Twitter.

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