The data landscape of businesses worldwide has been irrevocably changed by digital transformation. Based on an IDG research study, approximately 89 percent of organisations have plans to adopt a digital-first business strategy. This increasing trend of corporate digitalisation is a catalyst for data management and being concurrently generated.
To deal with the increasing quantities of data, the digital age calls for a heightened digital governance. This entails businesses to better comprehend and account for the heterogeneity of data through addressing and solving issues regarding the data’s availability, accessibility, usability, integrity and security.
With digital transformation services becoming more accessible worldwide – it is evident that the world of business will never be the same again. One tool that will make business sense in the data-driven environment is cloud technology, which will become essential for businesses in the new digital normal because it can provide the computing power, scalability and transparency necessary for optimal data management.
Better data management through machine learning
Data management issues in businesses are usually attributed to the handling of unstructured data. Also known as qualitative data, unstructured data refers to data that is not organised and storable in pre-defined database formats like traditional spreadsheets or SQL relational databases.
Prominent examples of such data include satellite visuals, surveillance imagery and even social media postings. While unstructured data is commonly used for monitoring, tracking and reporting on the movements of logistics or consumer chatter, there are several data management concerns stemming from its unorganised nature.
Firstly, there are reliability and security issues attributed to the exponential growth of unstructured data – where Gartner deduced that an upwards of 80 percent of an organization’s digital information is unstructured. This sheer volume presents higher levels of difficulty for data verification with standard data analytics systems.
The mismanagement of unstructured data manifested itself in the high profile 2016 email hack of the U.S. Democratic Party. This hack was a primary example of a theft of unstructured data rooted in cyber espionage. Less than a year after the hack, CNBC released an article illustrating how cloud computing could be an asset in tackling cybercrimes. The decentralised security features and 24/7 automated threat-monitoring empowers the cloud to form a secure cyber perimeter around vulnerable data.
Furthermore, the cloud can be integrated with other features such as machine learning to convert unstructured data into structured data. Recognised more as quantitative data, structured data refers to data that can be readily accessed and stored within a fixed database model.
Fitting in within fixed rows and columns, structured data is beneficial for businesses as it allows for an easier understanding and extraction of data for users. Furthermore, the organised nature of the data increases the “findability” of a business’ webpage because search engines are able to better understand the catalogued information within the data sets.
Immense processing power for faster data analysis
The raw computing power of the cloud is exemplified through virtualisation technology – where previously-isolated data is now part of a single, interconnected virtual environment and is automatically managed through virtual machines (VM). Using VMs, users are able to simultaneously run multiple applications of operating systems on a single hardware machine – saving load times and enabling faster data processing.
The high computing power could also withstand machine learning technology that would automate the conversion process of unstructured data into a structured form, making the cloud enhance the data management process to optimise both speed and accuracy.
As an example, one of our clients (a leading sports analytics company) had to manually compute data inputs and rank players/teams. Managing this unstructured data was time-consuming for their operations and made it difficult to consolidate for further analysis. This led them to seek an automatic process for the computation and ranking of players/teams.
To address these issues, we partnered with Microsoft Azure to implement a cloud solution for our client which had a machine learning feature, Azure Machine Learning. This feature would automatically rank players and/or teams – thus, computing the metric data which would then be stored within the Azure Data Lake storage system. The predictive and automated nature of our solution resulted in our client making more timely decisions while saving on manpower.
Flexible data storage with more scalability
Cloud storage allows for data to be saved in off-site locations that can be accessed either through a private network connection or the public internet network. Businesses can thus easily scale their storage requirements on demand without having to buy or upgrade more hardware and software. This infinite scaling makes the data stored more resilient to physical disruptions while being easier to back up.
Additionally, cloud storage costs are only derivatives of the storage service used. Maintenance of the cloud servers and other associated infrastructures become the responsibility of the cloud provider, helping to defray costs for businesses.
Therefore, as compared to computer hard drives that store only a finite amount of data and built-in storage area networks (SANs) that generate expensive maintenance costs, cloud storage provides an overall cost-effective and elastic storage outlet for data generated by businesses.
Higher data transparency and enhanced data security
In a cloud environment, data and applications across multiple environments are secured through the system illustrating extensive visibility to users regarding all file activity.
Cloud also has built-in security measures that will prompt both the user and provider to security threats or malware, maximising the security of stored data. In reference to the hacks of the US government emails mentioned above, the data security breaches brought to light the effectiveness and danger of phishing emails. This hacking method was popularised and crossed over to the corporate realm leading to organisations like FACC losing €54 million as a result of a single phishing email sent to just one entry-level employee. Hence, comprehensive security reduces the potential for data leaks and vulnerabilities, building customer trust and avoiding legal liabilities in the process.
Cloud as a comprehensive tool for data management
The cloud addresses both front-end and back-end issues of data – providing businesses with an extensive solution for data handling. Data processing and analysis can be efficiently done through the sheer computing power of the cloud while security and storage capabilities are maximised by the cloud through its infinite scaling and built-in monitoring features.
As the digital age is upon us in full swing, phenomena such as remote working and cloud migrations are slowly becoming a norm. It would be wise for businesses to begin thoroughly exploring the transformative potential of the cloud to ensure that they will be well-equipped to cope in a data-driven future.
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