The Latest Trends in Data Infrastructure

As the data influx enterprises receive continues to expand, in line with the development of new data-driven technologies, businesses will need to develop data infrastructures that are unified, robust, scalable, and secure

Enterprises and business leaders that want to ensure the success of their digital transformation process will thus need to recognize the importance of implementing a well-designed data infrastructure. If they fail to accommodate the shifting tides of the digital landscape, digital information technology, and the exponential expansion of Big Data, they might quickly find that they have lost ground to companies that have adopted scalable and adaptable data infrastructure strategies. 

This article will explore some of the data-driven concepts, tools, and practices that arise as an effect of data infrastructure implementations at the enterprise level. Hopefully, by discussing some of the latest trends in data infrastructure, business leaders, especially those with lower data-science and tech literacy, will derive a concrete understanding that contributes to the optimization of their respective business models.  

Understanding Hyperconnectivity in an Age of Exponential Growth

The Law of Accelerating Returns indicates that the rate of technological innovation is exponential, a trend that is exemplified and enhanced by the continual expansion of Big Data. Throughout the last two decades, digital information technology has become a benchmark of modern society. However, for this technology to meet current and future functional standards, it requires massive amounts of data on which to train its algorithms, data which it typically obtains by harvesting and/or commodifying human behavior in digital ecosystems. 

Digital information technology has propelled humanity into an age of hyperconnectivity. In a recent report by Statista, it was estimated that approximately 63% of the world’s population are internet users, and out of this 63%, over 4.6 billion are social media users. This number is steadily increasing, especially as the price of information technology products is halved while processing power doubles every two years (Moore’s Law). Moreover, when considering the advent of IoT technology and edge computing in conjunction with the development of smart devices, it is apparent that the wealth of data throughout the digital landscape is becoming practically incomprehensible. 

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As an effect, companies that want to maintain a competitive market advantage and continue to build/offer, at high velocity, technological products that make our lives more convenient, will need to build scalable data infrastructures that are capable of handling an ever-increasing data surplus. Businesses that can derive utility holistically from their data, organize it coherently within multi-cloud environments, implement robust security procedures, and comply with data governance and regulation will retain the ability to actively pursue the digital transformation process. 

Implementing data infrastructures that are capable of handling the complexity and scale of current data surplus will surely produce positive consequences throughout every business domain, from workflow and supply chain management to consumer engagement, market sentiment, and analysis, HR and PR best practices, as well as the imperative to maintain legal and technical transparency. By recognizing that we are now part of a hypersocialized and hyperconnected digital world, businesses can preemptively develop data infrastructures that decrease inefficiency and maximize profit, without extensively compromising consumer privacy and experience. 

MetaData Utility

Metadata is, simply put, data that describes data. For instance, within the digital marketing domain, metadata can pinpoint consumer habits with incredible accuracy. Digital advertisers can gather information on the type of device you use, the location and time of day during which you made a specific search query, the duration of your engagement with a given piece of content, the rate at which you type as well as the syntax you use, and much more. Using this metadata, advertisers can build a detailed digital fingerprint for each user that indicates their interests, personality types, beliefs, morals, and political inclinations, in addition to any other online behavior. 

Metadata can also play a prominent role in search engine optimization (SEO), allowing companies to build websites that optimize for consumer engagement and search query appearance. For example, by creating meta-descriptions for products, mission statements, and website content, companies can boost their search relevance, effectively increasing their digital presence. Companies with well-constructed meta descriptions will receive more attention throughout the digital landscape. 

On the other hand, metadata also plays a prominent role in file organization and identification; each file stored in a database, cloud server, or personal computer/device holds information that describes when it was last accessed or modified, its name and location, the date and time of its original creation, who or what is the owner, as well as the size and file type, among many other unique identifiers. These pieces of information serve as proxy indicators that define the categorization of the file in question, and could conceivably allow companies to enhance their data organization process while reducing the prevalence of issues like data silos. 

By incorporating metadata into their data infrastructures, companies can significantly reduce the dimensionality of the data they acquire/produce while also streamlining the organizational process. For instance, vast and complex datasets, especially ones that deal with consumer behavior, can be organized in terms of their metadata tags, which indicate the relevance of specific data points or sets. This practice can incorporate enterprise data as well, for instance, employee turnover rates and HR queries/concerns; by simply labeling columns in a dataset or identifying relevant features that correlate with certain enterprise practices, companies can enhance their ability to make sense of and process complex datasets. 

Importantly, metadata, while it possesses a high degree of utility, does not reveal information that is intrinsic to the data itself. As an effect, companies seeking to use metadata as an organizational tool should bear in mind that the insight it provides will rarely venture beyond surface-level data characteristics

From On-site Data Management to Collaborative Multi-cloud Environments

Enterprises seeking to expedite their process of digital transformation will necessarily need to consider the transition from on-site data management to collaborative multi-cloud environments. 

For instance, some businesses have already begun implementing data fabric solutions, which seek to streamline multi-domain information integration by building hybridized or unified cloud environments that connect data pipelines with relevant emerging technologies and cloud services. Such an approach considerably reduces the prevalence of data silos, potential security breaches, and decision-making bottlenecks

Moreover, data fabric solutions allow enterprises to visualize their data holistically across several business domains, effectively cultivating deeper insight into the customer life cycle. A unified cloud platform that integrates relevant business domains with consumer purchasing habits and market dynamics along with the appropriate cloud services could allow enterprises to discover novel data patterns and trends that can be used to optimize the business model. 

Metadata, within this context, can also play a prominent functional role, whereby it substantiates the process of data virtualization. Enterprises can employ metadata to build virtual data layers that permit real-time processing, analysis, and leveraging of source data. Taken in conjunction with newer technological approaches like edge computing, this practice could considerably increase the efficiency and accuracy of data processing while reducing data dimensionality

Enterprise cloud services also offer a variety of automation functions that streamline data integration across numerous business platforms and reduce the risk of cybersecurity breaches. By building collaborative cloud-based infrastructures, enterprises can increase the transparency of their data harvesting and process practices, while also ensuring that they comply with relevant regulatory frameworks such as the GDPR

Dark Data Management 

Unfortunately, while data fabric solutions present a promising approach to the integration of unified cloud platforms at the enterprise level, they still do not conclusively solve the problem of Dark Data; data that does not yet have any established value, monetary or otherwise. Enterprises continue to harbor this data in the hopes that it will eventually gain value, but doing so typically hinders the process of digital transformation and adds unwanted layers of digital complexity. 

When a company pursues digital transformation, its leaders may realize that a switch in their cloud infrastructure, and subsequently cloud provider, may be necessary to accommodate business needs. In doing so, they will not only have to confront the problem of organizational data silos but also data gravity; the notion that massive data sets are becoming increasingly difficult to move and manage

Data-driven companies now have a plethora of native digital assets, some of which are used far more frequently than others, meaning that when digital assets are transported from one cloud platform to another, there is a high likelihood that some will be lost during the process, effectively becoming Dark Data. While there are available asset discovery and cloud inventory automation tools enterprises can employ to reduce the likelihood of this occurrence, such tools still struggle in their abilities to penetrate disparate cloud environments. This typically results in the loss of non-native digital assets

Moreover, the presence of Dark Data instigates a variety of concerns surrounding data privacy, governance, and transparency. If we find that a given company has undergone a digital transformation and effectively shifted its data infrastructure from one cloud provider to another, but failed to consider the fact that numerous digital assets may still be contained throughout previous cloud servers, it will encounter significant procedural and legal repercussions. These consequences would not only reverberate throughout a company’s business model but also likely impact its public reputation and corporate value

Enterprises that recognize the prevalence of Dark Data early on during their digital transformation process will be better equipped to deal with the implementation of robust and scalable data infrastructures. The problem of Dark Data is still relatively new, and as an effect, most remedies involve conventional methods, such as the use of organizational automation tools, or conversely, the implementation of educational data literacy programs throughout every business domain. Companies that figure out how to minimize the prevalence of Dark Data will directly expand the degree of utility they can derive from their data, resulting in increased profit margins, while also ensuring compliance with data regulation. 

Finops 

Finops is a practice that integrates cloud financial management with company culture to facilitate the acquisition of maximal business value through cross-functional team collaboration on data-driven financial spending decisions. By building a data infrastructure that optimizes for finops practices, companies can centralize their cloud financial management and ensure that all business teams and individual employees are held accountable for their cloud usage. 

In doing so, companies enable a collaborative decision-making culture, whereby decisions regarding changes in cloud investment or architecture are driven by compromises between relevant enterprise teams regarding the cost, speed, and quality of provided cloud services. At its core, finops is not just about collaboration, but also about entrusting and fostering the growth of employees within a company, such that they feel empowered to pursue new developments in the features and apps they design, the business and investment strategies they cultivate, as well as how they communicate with each other. 

Ultimately, finops, like devops, contribute to a work culture in which the decisions being made are not exclusively relegated to the primary stakeholders in the company; in other words, finops can be viewed as an attempt to democratize corporate culture from a financial standpoint and increase the velocity with which an enterprise can deliver relevant applications and services. 

Contributor

Sasha is currently pursuing an MSc in Bioethics at King’s College, London. Prior to engaging in his current studies, Sasha was a Division 1 Ski Racer at Bates College, where he graduated with a Bachelor’s in Cognitive Psychology and Classical Philosophy. He is deeply interested in applied ethics, specifically with respect to AI-driven exponential technologies and how they might one day affect humanity

About Sasha Cadariu

Sasha is currently pursuing an MSc in Bioethics at King’s College, London. Prior to engaging in his current studies, Sasha was a Division 1 Ski Racer at Bates College, where he graduated with a Bachelor’s in Cognitive Psychology and Classical Philosophy. He is deeply interested in applied ethics, specifically with respect to AI-driven exponential technologies and how they might one day affect humanity

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