The volume of data being generated grows exponentially every year. In 2010, 2 zettabytes of data were created globally. By 2019, that number reached 41 zettabytes. The global datasphere is expected to grow to 163 zettabytes by 2025, and roughly 80% of that data will be unstructured. To access that data and extract actionable insights from it, businesses need to unlock big data transformation.
When data volumes first exploded in the 2010s, a plethora of big data transformation tools and technologies emerged to help make sense of it all. However, no standards existed, and it seemed that every day a new framework or approach was being touted as the solution to everything — think Apache Hadoop, Cloudera Impala, Cassandra, etc.
Luckily the landscape has matured since then, and various descendants of these early tools and technologies have found their way into everyday use during the 2020s — not only for solving unstructured big data but also in the simpler structured data workflows that many companies still use. This development has opened a new realm of wide-ranging possibilities for enterprises. However, many are unsure how to best capitalize on this new era of big data transformation tools.
Here we share some key insights to help guide your data transformation process and, in so doing, take full advantage of a matured and highly functional toolset at your disposal.
In the past, companies used centralized teams to ingest, curate, and manage data. Today, they are experiencing expanding datasets from a growing number of disparate applications, databases, data warehouses, analytics systems, and so much more. To add to this, the central teams are often stretched and overworked while they play to the needs of an ever-increasing and diverse population of data consumers.
Additionally, the technical literacy of the average business stakeholder has increased. Think Microsoft Excel in the 1990s and compare that to now, where almost all business users can probably develop a well-thought-through spreadsheet. Tools such as Power BI are becoming more user-friendly, leading to a democratization of data that is accessible to everyone.
This onslaught of data, combined with an increasing need to leverage data strategically across the enterprise, has created problematic bottlenecks for these central teams. These bottlenecks contribute to poor data utilization — 41% of business leaders say they don’t understand their data because it’s too complex or inaccessible.
Companies recognize that their legacy data tooling and processes are ill-equipped to handle the ongoing shifts in the big data landscape. The old approach leads to missed revenue opportunities, lower efficiency, and issues with productivity and quality. To overcome these issues, companies need access to the types of data transformation tools that create the foundation for innovation.
The industry usually defines big data by the 3 Vs: volume, velocity, and variety. But regardless of the scale of a company’s data across the 3 Vs, it can still ease its data challenges by leveraging what these new tools in a modern data stack (MDS) have to offer.
The traditional approach — and with it, the traditional data stack — should be strategically phased out in favor of the modern data stack. The MDS is a composable ecosystem of data tools that emerged out of the wild west of big data tools from the 2010s. An MDS differentiates itself from traditional tools by focusing on cloud-native technologies for storage, computing, and scalability.
To design a modern data stack that promotes successful, sustainable big data transformation consider the following:
Flexible ingestion options allow organizations to integrate with a variety of data sources, such as on-premises systems, cloud ecosystems, or SaaS offerings, and access data in a streamlined fashion.
Scalable and cost-effective cloud storage keeps data unified and centrally accessible at an optimal cost.
A variety of computing options enable organizations to work with numerous volumes and varieties of data and scale operations on demand to suit these needs.
Discovery and downstream process enablement where data is made available for the generation of traditional business intelligence and visuals, ad-hoc analysis, or served up for machine learning and advanced analytics.
So, what’s the secret sauce to an effective MDS? It is implementing big data transformation tools that allow users to execute different use cases, while leveraging the same integrated, governed data sets. With the cloud, users can model and conform data sets virtually to suit their needs with minimal help from any central IT team, all with standard governed tools to accomplish a unified business strategy.
By taking a modern approach, critical operations that were previously handled by the centralized teams are spread across the enterprise. The effect is that business users are empowered and free to experiment, ultimately resulting in new insights and opportunities for the company.
After implementing an effective MDS, organizations will start to see real value which results in a domino effect throughout the business. The bottleneck on IT becomes significantly reduced. Speed and agility are accelerated, leading to improved efficiencies and decision-making. The flexibility to innovate is enhanced as new ideas that require new data are managed within the business unit.
Kenway helped an asset management company implement a modern data platform that transformed its disconnected analytics program and established a common source of truth.
The company struggled to understand the full breadth of its sales team’s relationships with current and potential wealth advisor clients. Without a data solution for invaluable insights and analytics, the company lacked clarity around advisor profiles, such as the universe of products being purchased, the percentage of sales versus competitors, and engagement with the organization’s sales and marketing representatives.
Kenway collaborated with the asset management company to create a future state platform with best-in-class Data Architecture, Data Management, and Data Governance capabilities. The platform solved the client’s immediate issues around targeting and lead qualification while providing long-term extensibility for future use cases. With a complete view of its clients, the asset management company achieved better business outcomes around its sales, marketing, and product distribution efforts.
Having the right tools is just one part of the data transformation process. An organization must also consider its data strategy through the lens of the alignment of people, processes, and technology. Kenway Consulting aims to holistically partner with companies along the data enablement journey. Kenway’s approach includes:
Assessing: Kenway identifies how the organization invests in the applied uses of its data.
Enabling: The initial build and release of the data platform.
Empowering: Kenway provides platform training and documentation to internal teams.
Sustaining: Support for platform operational processes is decreased and transitioned to the organization.
Connect with us to learn more about how the Modern Data Stack can help your enterprise succeed.
What are the four types of data transformation?
The four most common types of data transformation are:
Structure Transformation: This involves changing the structure of the data (such as the from a CSV file to a JSON file) without altering the content.
Data Reduction: This is achieved by transforming large or detailed datasets into smaller, more concise forms for efficient processing or analysis.
Data Cleaning: This involves identifying and remedying inaccuracies, errors, and inconsistencies.
Semantic Transformation: This process focuses on changing the meaning or interpretation of the data (such as a language change) while keeping the structure.
What is an example of a data transformation process?
Converting raw transaction data into a structured data format suitable for business analytics and reporting is one example of a data transformation process. For example, if an online retailer wants to analyze its transaction data, the data would be collected, cleaned, and integrated. After that process, the data would be structured into the format needed for analysis. Then, the data would be formatted and loaded into a business intelligence tool, where users can generate reports and perform analyses.
