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Scalable data management and analytics solutions

April 22, 2022

Data has never had more value than it has today. It has the potential for transforming organisations, accelerating innovation, improving the customer experience, and driving operational efficiency. However, many organisations are struggling with the sheer volume of data available to them, with exponentially more data being created every day through business systems, smart devices, digital customer interactions, R&D activities, product testing, and a host of other key business processes. Regardless of how much potential data has, it has no business value if organisations cannot effectively manage and process it to gain insight.

The changing data landscape

Small and medium-sized businesses, as well as large corporations, are being impacted by digital transformation. A key priority is the development and purchase of new data management and analytics technologies to support digital transformation programs and a wider variety of decisions and innovation opportunities.

Organisations collect and analyse data from a variety of sources to make data-driven decisions that will provide them with a competitive advantage, such as producing unique product offerings and predicting and responding to a wide range of customer demands.

The four V's (Volume, variety, velocity, and variability) of data are rising. Keeping pace with the volume of mobile, IoT, AI/ML, and cloud computing data is a new challenge for organisations. Investments in data management and analytics technologies are rising.

New legislative requirements are being imposed as new threats to data security emerge. GDPR (General Data Protection Regulation), the Privacy Act, and other regulations require organisations to develop effective data governance and protection systems. A data breach can compromise revenue and reputation, making data security crucial.

Business Challenges

A massive amount of data is being generated due to factors such as the increasing number of devices at the edge and IoT. Managing and storing that data presents a significant business challenge. The increasing V's of data (variety, velocity, and volume) compel organisations to seek more effective management tools. This presents significant challenges to businesses:

  • Operational challenges - Data can originate from various sources within an organisation, including databases, web applications, and customer relationship management (CRM) systems. Each of these sources has its own set of interaction rules, which adds time and resources to data extraction, transformation, and normalisation. Businesses often lack the adequate data migration tools to manage data across multiple locations. Data should be replicated continuously, and it is important that dependencies are managed.
  • Data Silos - Duplicate data entries create obstacles for data sharing across the organisation. These disparate collections of overlapping yet contradictory data exist in silos. Siloed data increases procurement and integration costs and prolongs the time needed for business intelligence reporting.
  • Automation - Data comes from both inside and outside an organisation. As a result, multidimensional datasets are created, with each data stream defining its own set of standards. Managing diverse data that adheres to various standards has become a significant challenge. Automation is a more reliable method of managing data than manual processes due to human error. While providing accurate, standardised data can be a challenging task, data automation can completely transform how businesses operate when done correctly.
  • Data security - Many data sources, IoT, mobile, robots, vehicles, and corporate backend create new vulnerabilities, complicating monitoring, and security. There is a growing concern regarding data breaches, regulatory compliance, and other potential threats.
  • Speed of Insight - Data and information are both necessary for discovering insights, which can subsequently be used to influence decisions and bring about change. Large amounts of corporate data are only helpful if a company knows what data it has, where it is stored, and how to use it. Scalability and performance are critical for data management solutions to give essential insights faster.
  • Lack of Governance - Data governance is the set of rules that dictate accessibility and tools. A lack of governance leads to misalignment and unfavourable business results, and data ownership that is not streamlined.

Data management framework

An efficient data management system assists firms in discovering, preserving, and delivering all critical information at the right time, in the right location, and with the required quality. Innovation through data-driven technologies is only possible with enhanced, organised and easily accessible data.

The modern data management framework considers several key factors, vertical solutions, data access and visualisation, processing and analytics, storage, governance, orchestration, security, and infrastructure.

  • Vertical solutions - Businesses can leverage data to gain a competitive edge and establish a distinct market position. Necessary business models should be developed and strategically applied.
  • Data access and visualisation - By giving visual context for the data in the form of maps or graphs, visualisation helps us grasp what the data means. Visualisation can be used to understand patterns, trends, and outliers in large data sets.
  • Data processing & analytics - Data collection and identification alone are ineffective. Processing is required to validate, organise, transform, integrate, and extract data in an appropriate output format for further use. In contrast, analytics assists businesses in identifying, analysing, and communicating data patterns.
  • Data storage - Data is kept in various systems, including data warehouses and data lakes. Data engineers need to transform data swiftly and efficiently from its original format into the required format for analyses. It is recommended that data backups be made and retained.
  • Governance - Data governance refers to the rules, standards, and metrics that ensure an organisation's data is used effectively and efficiently to achieve its goals. Data governance is critical for any company that wants to satisfy various compliance and risk management objectives.
  • Orchestration - Orchestration optimises business intelligence by making data from multiple storage locations available to analytics tools.
  • Security and Trust - Data security is becoming increasingly important as organisations acquire, manage, and store larger volumes of data. Computing systems are growing more complex, with public clouds, data centres, and a wide range of edge devices. Security strategies should be implemented from the edge to the core, from hardware (Silicon Root of Trust or sROT, which integrates security directly into the hardware level of HPE servers and offers unprecedented levels of protection against firmware attacks) through encryption and access control.
  • Infrastructure - Data infrastructure includes and is not limited to hardware, software, networking, services, policies. Infrastructure enables data consumption, storage, and distribution.

HPE is a key provider of data management technology. HPE brings together the Data Services Cloud ConsoleHPE Alletra, and HPE InfoSight - delivered as a service via the HPE GreenLake edge-to-cloud platform. HPE's intelligent data platform provides a data experience that removes silos across people, processes, and technology to unleash data, agility, and innovation for organisations.

Data management and analytics solution characteristics

The ideal data management solution should be flexible and agile, with the ability to respond to changing demands automatically. The management system should be scalable as the companies grow or their data sources expand. Future development opportunities should be anticipated, and the return on investment maximised.

Data management systems should be high-performing and available while protecting data and preventing loss. The solutions should not add complexity, and be intuitive to manage, resulting in increased operational efficiency.

As data management infrastructure evolves, solutions should support digital transformation and platform consolidation while saving time and resources. Data migration should be straightforward when choosing new or upgrading old technology.

Modern data management solutions need to support new workloads with managed infrastructure by offering pay-as-you-go or consumption-based pricing.

Conclusion

The quantity and quality of data collected are critical to the successful implementation of analytics applications. Data streams are dispersed and hybrid in nature. The primary objective of many organisations is to optimise data utilisation and infrastructure management across IoT, edge, cloud, and mobile apps.

HPE solutions for database and data analytics dramatically accelerate business insights while providing industry leading performance and unmatched workload optimisation. With features such as enterprise-grade security, rapid scalability with a cloud-like experience, intelligent infrastructure management, we can provide expert support for implementing or upgrading your data management solutions.

HPE delivers technology where and when you need it - from edge to cloud. HPE technologies are purpose-built to revolutionise how you interact, organise, and extract value from your data and drive business outcomes. As your IT partner, we can help you take advantage of HPE solutions. Contact us today.