cookie consent

By browsing our website, you consent to our use of cookies and other tracking technologies. For more information, read our Privacy Policy.

Intro

Around the world, organizations are waking up to the importance of data and how data-driven decisions can help them stay ahead of the curve. From marketing personalization, ad targeting, to customer experience, data offers boundless possibilities that span all industry verticals. The right data makes it possible to verify, understand, and quantify anything from new business initiatives to dynamic pricing algorithms. And as noted by MIT Sloan, start-ups that possess information unique to their industry and apply analytics to interpret and deploy data in strategic ways are more likely to succeed.

According to analyst firm IDC, more than 59 zettabytes (ZB) of data is created, captured, copied, and consumed globally in 2020. When you do the math, this is enough data to fill up a staggering 8.7 million units of the largest commercially available 18TB hard disk drives (HDD) with data every day.

And herein lies the issue. Properly harnessing data and deriving the desired insights is no walk in the park. For underprepared organizations, surging data volumes and the associated management overheads will only serve to aggravate this challenge.

Getting the right data is hard

In a utopian world where data-driven businesses are a reality, one might imagine sleek charts and precise reports that materialize at the click of a mouse button. What happens in real life is far more mundane, and often calls for laborious data preparation and getting various colleagues to work together. This challenge is further exacerbated for start-ups expanding at breakneck speeds, as they grapple with constant schema changes as new services and apps are developed.

Regardless of enterprises or start-ups, stakeholders within organizations can invariably be classified into business users, the IT department, and the data team. Business users are anyone who requires data insights and can range from the CEO, board members, or the sales executive. On their part, the data team is typically tasked to do the actual data analyses and reports.

A significant amount of work goes on behind the scenes: Depending on the nature of the received request, the data team might have to first identify the relevant data, retrieve it, clean it up, combine it with other data, and analyze the results. If the data volume is substantial or further number crunching is required, then the appropriate computing resources will have to be secured or scheduled.

Data processing can introduce further delays to an already laborious and time-consuming process. And because the data team might not be trained in IT, team members are often reliant on the IT team to manage their data environment and resolve technical hiccups. Unsurprisingly, half the challenges faced by data professionals revolve around keeping data systems running or resolving faults when intricate data flows “break”.

Of course, data professionals have over time developed various strategies and tools to speed things up data processing. But even when the relevant data is safely ensconced within a data lake, an analyst must still input the right parameters to extract and format it from the raw data. Moreover, getting things right is dependent on a good understanding of the business, which might necessitate them consulting with business users – introducing additional delays.

A cycle of distrust and risks

The above situation means that business users, the IT department, and the data team often do not see eye to eye when it comes to harnessing data. And when data teams fail to explain their jobs and associated difficulties, they eventually end up with a narrowing slice of the budget and waning influence in the organization.

Some common complaints include:

  • Reports taking too long: Laborious data processes can increase friction between stakeholders. Even if delays stem from IT issues, data requests that consistently take too long can lead to both personal frustrations and missed opportunities for the organization.
  • Errors in reports: Despite the best efforts of data professionals, mistakes are sometimes made. Quality assurance is frequently manual and can miss out on serious errors or problems with the data models. Erroneous outputs can foster a deep and lingering sense of mistrust. It does not help when errors are sometimes only discovered after weeks or months, weakening trust in dashboards or reports.
  • Cycles of distrust: It might take just one or two executives to stop trusting the data to kick off a chain reaction across the organization. As key stakeholders stop making data-driven decisions and revert to their “gut feel”, managers and junior employees notice and soon start ignoring what the data is telling them, too. The result is a cycle of distrust that worsens over time.
  • Risk of data leaks: One consideration that is rarely mentioned but of increasing pertinence is the heightened risk of data leaks. The ad-hoc data management strategies employed by many SMBs and start-ups can make it practically impossible to ensure that personally identifiable information (PII) and other sensitive data are properly secured. This is hardly reassuring when you consider that most data leaks originate from internal gaps in control.

Taking a cloud approach

Data analytics and data management are hardly new fields, though traditional solutions can be costly and rigid. However, the cloud has opened the door to address traditional pain points around utilizing and managing data, while simultaneously enabling greater cost-effectiveness and flexibility.

Imagine a cloud-based service designed to integrate and funnel data from disparate data sources into a centralized repository, which can be a data warehouse or data lake. Data can be transformed as it is transferred to the data warehouse, with data types translated automatically to maintain compatibility with the destination. Ideally, this could be created using a graphical interface or accessed programmatically using API calls,

Such a solution could offer a range of advantages, including:

  • Significantly reduced manual data preparation
  • A cloud-based solution eliminates IT issues
  • Resolve the problem of data silos without having to relocate data
  • Flexibility of a pay-as-you-go cloud service

Assuming this cloud-based solution is built from the ground up, it further opens the door to integrate capabilities not typically found in older products, namely around testing and governance. For the former, automated tests can help ensure that erroneous inputs or malformed data are weeded out as early as possible to ensure accurate insights.

With data only accessible from the centralized system which serves as a single source of truth across the organization, fine-grained controls, and integrated data logging can be leveraged to ensure trust and support data audits. For instance, a single dashboard can be used to assign or revoke access to various pools of data, including PII.

Data integrity is also protected by rules that prohibit the modification of source data without explicit permission, while tags and groups can be created for easy assignment of permissions. Finally, because the data is accessible globally, new workers on the team can leverage built-in data discovery tools to quickly identify the top data sources or repositories within the organization.

Winning the war on data

To be clear, existing enterprise information solutions on the market can perform many of the data processing capabilities highlighted so far. The hefty costs of deploying them aside, many of them are also designed and built with rigid, legacy technologies that do not integrate well – if at all – with the cloud. This means these older tools are unlikely to be optimal for a fast-moving, cloud-native startup, and make a cloud-based service much more appealing.

Yet winning the war on data requires more than just data transformation but must include the aggregation of data from multiple sources. An example of this in the public sector would undoubtedly be the various smart city initiatives happening around the world. Enabled and integrated by digital technologies, they require data from IoT sources and other areas to be utilized together.

Take the buzz surrounding the Punggol Digital District , a town in Singapore. Designed with deeper integration and synergy of multiple uses in mind, blends commercial with inclusive living spaces, with hawker centers, community centers, childcare facilities, parks, and public spaces making up the latter. It stands to reason that government agencies can leverage a plethora of data from this futuristic town to further enhance and improve the standard of living for residents there.

This can happen with multiple incoming data pipes feeding real-time or batched data into a data lake. This stream of data can be automatically processed with predefined rules, offering an up-to-date view of the town’s health that ranges from monitoring lift breakdowns to detecting larger-than-usual crowds attending celebratory events. Advanced analytics or machine learning (ML) systems can process this data to look for anomalous data or even determine if more public buses need to be scheduled.

Harness the power of data today with CloudCover

The potential of data is virtually limitless. To help start-ups and businesses leverage the full power of their data, we are currently building a cloud-native data pipe service. To offer maximum flexibility, we are adopting a modular approach that makes it easy to plug in customized code to manipulate data. This offers the flexibility of proprietary code or even to integrate with external software.

If you are interested to find out more about data pipes, visit here .