The Importance of Analytics
Nowadays, data has become a core asset of any business and the demand for data analysis capabilities is rising more than ever, leading to an unprecedented fast pace of advancements in Analytics. A suitable analytics infrastructure can support strategic decision-making by providing vital information about market trends, customer behaviour and satisfaction, performance, financial health and indentify opportunities and threats to the business. The Business Analytics market is rapidly transforming due to the rising demand to extract insights from large sets of data and it is expected to display an exponential growth pattern during the next years driven by the introduction of new developments that are reshaping how businesses harness the power of their data. However, the implementation of innovative data management technology can be complex for both technical and non-technical stakeholders, resulting many times in the setup of incomplete solutions and/or reliance on legacy systems.
Demand for Data Management capabilities
Data management systems are essential tools to organizations as they enable collection, storage, management and trusted access to data in a safe and secure way. Thus, they facilitate informed decision-making, operational efficiency, and cost reduction while ensuring the security, accessibility, and accuracy of valuable corporate data. A robust data management system offers benefits such as increased productivity, accuracy, cost-efficiency, scalability, security, continuity and compliance with privacy laws. Well managed data aids business intelligence, enhances organizational resilience and supports business continuity in the face of various risks and challenges. For many years, enterprise data was stored in on-premises data warehouses which are deployed and maintained by the organization within local servers. Typically, the required hardware and software for such systems is expensive and infrastructures updates are often required to handle the progressively larger volumes of data being stored and processed on a daily basis. In response to this, the emergence of cloud data management technologies has paved the way to increased efficiency, performance and scalability since the hardware and software management is handled by the vendor. Cloud technology automatically adapts to the growing needs for storage and processing capabilities at a cost-effective way and it is pushed to users through an online interface with simplified data management for each organization.
Legacy Systems and Data Silos
Legacy data warehouse systems (e.g. on-premises) were considered the gold-standard in the data storage space and their value is still irrefutable when complete autonomous control and security is required. Nevertheless, the inherent complexity and cost associated to deployment, maintenance and scalability of these systems have inevitably contributed to the progressive creation of siloed data within organizations, hindering inter-departmental collaboration and leading to organizational inefficiencies. Data silos are a collection of data held in isolated repositories that are incompatible with one another or inaccessible by another group in the same organization, resulting in a scattered and fragmented holistic view of data. Their existence is extremely common and they rise naturally over time due to the necessity different departments have to store their own information for various purposes. Nowadays, access to enterprise-wide information is crucial to provide accurate insights about businesses and markets. This capability can only be achieved through data storage centralization, where all organizational data sources are integrated into a single source of truth, providing a unified view of the data while reducing the risk of inconsistent or conflicting records. Given the scalable, flexible and cost-effective nature of cloud data management technology, it becomes the obvious choice to accomplish such deed.
Cloud Data Management Systems
The adoption of innovative cloud data management solutions can be a daunting and overwhelming experience regardless of the numerous benefits they may bring in the long-term. Similarly, migration from legacy systems may come with setbacks and downtimes that can affect business operations. The selection of a suitable provider and data management architecture to handle the volume of data for storage, processing and/or analysis is a task that should be carried diligently to avoid future disruption to the business. The rapid cloud technology advance has vastly contributed to the emergence of various building blocks that together compose the backbone of modern data warehouse system and each component may or may not be included in the environment, depending on the desired functionally and performance output that is expected from the solution. Azure is a cloud computing platform run by Microsoft that enables the design and implementation of cloud infrastructures according to the business needs. This process usually requires a technical specialist that accesses the requirements stemming from storage, processing and analytical capabilities in order to design an appropriate workflow for each case. This step can be tedious for stakeholders trying to initiate their journey into the cloud space as they are often presented with technical questions that are either difficult to answer accurately or pertain to business strategy that remains uncertain to predict.
Extract-Transform-Load (ETL) and Data Modelling
The following step which consists of implementing the solution requires an increased involvement of technical teams to develop detailed manual instructions and algorithms tailored for the extraction and transformation of data from the original source system for subsequent loading to the appropriate storage destination component within the cloud infrastructure. This process would ideally be repeated for all relevant data sources contained in the company repertoire. This phase involves a laborious multi-department approach, which is also accompanied by lengthy and strict security and compliance measures put in place to prevent data exposure risks.
One of the biggest challenges the analytics market faces today is the ability to integrate data from diverse business sources and blend it with each other in order to obtain a holistic view of organizational data. The portfolio of systems setup under a certain business will most surely produce and organize data in inconsistent formats and structures across the various tools as a result of the natural building strategies and intellectual property built in each solution. Thus, the incorporation of different data sources within a data warehouse will lead to an overall disconnected scenario if they are not adequately transformed to adopt synergetic relationships. Logically, the larger the number of data sources the greater the complexity on integrating and blending the entire portfolio together. To add to this, the increased volumes of data will most likely amplify data quality issues and add a new degree of difficulty.
Artificial Intelligence-driven Cloud Data Management Systems
The ETL step describes the drawbacks observed currently for both on-premises and cloud management systems. The ETL process is still an extremely manual and time-consuming procedure, which requires urgent redefinition by the community for further simplification. Likewise, data modelling and schema design are areas that display the same type of issue and demand for innovative initiatives that promote the development of sophisticated approaches. The recent investment and progress in artificial intelligence has driven the markets to a state of continuous technological advance fuelled by the consumer needs. Nonetheless, it is noticed the existence of gaps in certain markets that prevent the adoption of such solutions that could be attributable to the immaturity of the movement. Viable strategies to fill this gap are essential to stimulate the adoption of cloud management systems and unlock the true value of data and technology.
Who is Furivatics
Furivatics has identified the gaps and bottlenecks in the market and is committed to solve them through the delivery of high-quality and cost-effective analytical services and by leveraging the Microsoft technological ecosystem. We are dedicated to pioneering solutions that promote the seamless integration and unification of data. Our goal is to empower businesses with a holistic view of their activities, unlocking the full power of their data for valuable insights. By facilitating informed decision-making, we accelerate their journey towards success, fostering progress and innovation. With a keen understanding of the evolving landscape, Furivatics stands as a catalyst for transformative growth, ensuring our clients harness the true potential of their data.
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