How does one measure the success of an organization in the age of insight and business intelligence? Enterprises today run on a strong foundation built on the clarity and efficiency of the data that they possess. Highly beneficial data analytics that gleans business insights faster is the lifeblood of the data economy and every business' evolution is plainly dependent on analytics.
If a business aims to evolve its operational models to maximize its revenue generated from data it must find ways to accelerate its data analytics workflow. Organizations depend on continuously upgrading the quality of their insights to survive in the highly competitive marketplace of today.
In this article, we will learn why data remains a crucial common denominator for data-driven businesses to accelerate decision velocity and drive continuous innovation. We will go on to discuss how enterprises can make data analytics faster to deploy billions of bytes of data while balancing hybrid cloud deployments.
Innovating New Approaches To Data Analytics
Until recently, physical systems were employed to crunch so-called Big Data that involved moderate volumes compared to today's standards. Edge sources would send the data which would be stored on on-premises data centers that were then centralized to facilitate smooth data transit. When the data volumes expanded beyond anyone's imaginations, enterprises had to give up the old strategies as the old applications were no longer sufficient.
Unstructured data sets have been on the rise at an unprecedented pace, involving entire terabytes of IoT content, images, and edge-sourced videos. This is a problem that gets exaggerated when environments that are expanding data volumes are forced in siloes with a multi-generational IT infrastructure. With the combined impact of siloed data stores and non-interoperable environments, the analytics process gets bogged down.
Minimizing Time To Insights
The slowdown caused by siloed data and multi-generational IT landscapes tends to make enterprises end up with missed opportunities. New opportunities can only be capitalized upon by meeting them with quick IT environment upgrades and migration of entire data sets with ease.
The end goal is to reduce time to insights by introducing new approaches that facilitate processing closer to their sources. For reasons of efficiency and economics, processing data at higher speeds remains the priority for any IT enterprise. The costs of data retrieval and transfer across data networks need to be minimized with localized processing of data in enterprise clusters.
Organizations must also minimize the gaps in the interaction between analytics teams and the teams involved in artificial intelligence. This helps deliver new experiences for customers where data remains the essential guiding force into the future for enterprises.
Data Analytics Needs To Evolve Alongside Edge Technologies
A true innovation in data analytics is about finding the right solutions to the problems faced during pairing it with edge technologies such as machine learning, CloudOps, deep learning, etc. This requires there to be clean data at all stages of the analytics process from the collection stage all the way to the insights and inferences.
Leaders of data analytics system design have also leveraged workflows to surface information from expansive knowledge bases. This is especially useful when working on information surrounding a prospective customer, wherein a workflow can find links to executive briefing centers with historical data.
Certain technologies which may not be seen as cutting-edge yet are utilized massively include AI-driven speech-to-text and optical character recognition. Contextual search technologies such as these that implement AI capabilities can also be used to accelerate data retrieval.
Analytics Should Be Implemented Across The Board
The market for analytics is seeing an expansion with demand coming in from several avenues and sectors. Leaders of IT enterprises are enabling business intelligence and SQL operators to work alongside each other to come up with the right solutions as and when needed. Another important transition taking place these days is the acceleration of AI initiatives while the Hadoop workloads are being supported by legacy systems.
Data analytics teams are encouraging upskilling in technologies such as Spark while enabling machine learning projects to implement a combination of these capabilities. Since data is a strategic asset for every organization as it is critical to unlocking business insight, it should be clean and uncluttered. Once that is ensured, the business tends to derive immense potential for growth through the collaboration between multiple teams.
If the enterprise teams are all made aware of what the data truly contains it can drive new business cases across the board that can expand to power several new customer experiences. Data is also in a way the fuel that drives the entire technology sector into the era of enterprise AI.
Medical imaging in healthcare, fraud detection in finance, video surveillance in retail includes some areas of innovation that will get better traction with an accelerated approach to data analytics.
Service Level Agreements For Data Silos
Each team of the data analytics-powered enterprise needs access to the same data, be it siloed or not. While maintaining data silos separately may be a good idea for companies that deal with very strict client-provider Service Level Agreements (SLA), actually eliminating these siloes could power the productivity of analytics teams. All data customers must be served with data maintained over flexible, high-density infrastructure at scale.
Open-source analytics solutions are also being embraced by the technology sector to drive far greater performance than ever imagined. These solutions drive the decentralization of analytics projects so that compute and storage tasks can be delineated. This way, diverse analytical workflows can be managed within the same data infrastructure. As a variety of distinct open-source analytics solutions are involved, data scientists and engineers need to take a unified approach towards different data types.
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Data Accelerating Must Be Done While Ensuring Best Practices
Organizations need a value-added approach for cost-effectively scaling, and processing data while reducing data movement and avoiding vendor lock-in periods. This should be ensured while leveraging containerized workloads to power seamless data and app mobility across multiple environments. To take on an approach that ensures a data-first modernization, you can learn how Daffodil can help with its Data Management Services.